Actuarium
Official syllabus · B.Sc. (Hons.) Actuarial Science, Computer Science & AI

Full degree syllabus

Every course in the integrated actuarial science, computer science and AI degree: topics, learning outcomes, key formulas, the SOA/CAS/CPCU exams each course prepares, official past papers, formula sheets, Library readings and lesson films.

58 courses
450 syllabus topics
29 professional exams mapped
52 courses with lesson films
Build a study plan from this syllabus →Degree final examLesson filmsAll formula sheetsCourse path to ASA / FSA / ACAS

Year 1 · Foundations

Semester 1 · 17 credits

MATH 101Mathematics & Statistics4 credits

Calculus I

Limits, derivatives and integrals of one variable with an eye toward the continuous models actuaries use daily. Every technique is motivated by a rate, a present value or a density.

8 lesson films Plan this course
Topics (8)
  1. 01Limits & continuity▶ E1
  2. 02Differentiation rules▶ E2
  3. 03Optimisation▶ E3
  4. 04Riemann integral & FTC▶ E4
  5. 05Integration techniques▶ E5
  6. 06Improper integrals▶ E6
  7. 07Taylor series▶ E7
  8. 08Intro to ODEs▶ E8
Learning outcomes
  • Differentiate and integrate elementary functions fluently
  • Model growth, decay and accumulation with differential equations
  • Use Taylor series for approximation and error bounds
  • Translate word problems into calculus
Key formula
∫0∞e−δt dt=1δ\int_0^{\infty} e^{-\delta t}\,dt=\frac{1}{\delta}
Exams, past papers & formula sheets

Degree-internal course — no professional exam maps directly; supports mathematics & statistics skills used across the tracks.

    Find lectures on Scout: “calculus 1 university course” →
    MATH 111Mathematics & Statistics3 credits

    Linear Algebra

    Vectors, matrices and linear maps — the language of regression, Markov chains, neural networks and multi-state life models.

    8 lesson films Plan this course
    Topics (8)
    1. 01Gaussian elimination▶ E1
    2. 02Vector spaces & bases▶ E2
    3. 03Determinants▶ E3
    4. 04Eigen-decomposition▶ E4
    5. 05Orthogonality & projections▶ E5
    6. 06Least squares▶ E6
    7. 07SVD (intro)▶ E7
    8. 08Stochastic matrices▶ E8
    Learning outcomes
    • Solve linear systems and characterise solution spaces
    • Compute eigenvalues/eigenvectors and diagonalise
    • Apply orthogonal projections to least squares
    • Read and write matrix notation used in ML and Markov models
    Key formula
    β^=(X⊤X)−1X⊤y\hat{\beta}=(X^{\top}X)^{-1}X^{\top}y
    Exams, past papers & formula sheets

    Degree-internal course — no professional exam maps directly; supports mathematics & statistics skills used across the tracks.

      Find lectures on Scout: “linear algebra course for data science” →
      CS 101Computer Science4 credits

      Programming I (Python)

      First course in programming using Python, with actuarial data from day one. Emphasises clean, testable code and reproducible notebooks.

      8 lesson films Plan this course
      Topics (8)
      1. 01Syntax, control flow, functions▶ E1
      2. 02Data structures▶ E2
      3. 03File I/O & CSV▶ E3
      4. 04NumPy & pandas▶ E4
      5. 05Plotting▶ E5
      6. 06Unit testing▶ E6
      7. 07Git basics▶ E7
      8. 08Mini-project: premium calculator▶ E8
      Learning outcomes
      • Write structured Python programs with functions and classes
      • Manipulate tabular data with pandas
      • Test code and use version control
      • Automate a simple premium calculation end-to-end
      Exams, past papers & formula sheets

      Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

        Find lectures on Scout: “introduction to programming python course” →
        ACT 101Actuarial3 credits

        Introduction to Actuarial Science & Insurance

        What actuaries do, how insurers and pension plans work, and why decisions under uncertainty need mathematics, data and judgement together.

        8 lesson films Plan this course
        Topics (8)
        1. 01Risk & insurance principles▶ E1
        2. 02Insurance company operations▶ E2
        3. 03Products: life, annuity, health, P&C▶ E3
        4. 04Pensions & social insurance▶ E4
        5. 05Regulation overview▶ E5
        6. 06Actuarial control cycle▶ E6
        7. 07Credential pathways▶ E7
        8. 08Ethics primer▶ E8
        Learning outcomes
        • Explain the insurance mechanism, pooling and adverse selection
        • Describe life, health, P&C and pension practice areas
        • Map the SOA/CAS/CPCU credential pathways
        • Compute simple expected losses and premiums
        Exams, past papers & formula sheets

        Degree-internal course — no professional exam maps directly; supports actuarial skills used across the tracks.

          Find lectures on Scout: “introduction to actuarial science course” →
          ECON 101Professional & Ethics3 credits

          Principles of Microeconomics

          Consumer choice, markets, information asymmetry and insurance demand. Satisfies half of the Economics VEE.

          8 lesson films Plan this course
          Topics (8)
          1. 01Supply & demand▶ E1
          2. 02Elasticity▶ E2
          3. 03Consumer theory▶ E3
          4. 04Expected utility & risk aversion▶ E4
          5. 05Production & costs▶ E5
          6. 06Market structures▶ E6
          7. 07Information economics▶ E7
          8. 08Welfare & regulation▶ E8
          Learning outcomes
          • Analyse supply, demand and elasticity
          • Explain moral hazard and adverse selection in insurance markets
          • Apply expected utility to risk-averse choice
          • Evaluate market structures and regulation
          Key formula
          E[u(W−X)]=u(W−π)E[u(W-X)]=u(W-\pi)
          Exams, past papers & formula sheets

          VEE · Economics

          Find lectures on Scout: “principles of microeconomics course” →

          Semester 2 · 17 credits

          MATH 102Mathematics & Statistics4 credits

          Calculus II & Multivariable Calculus

          Sequences, series and functions of several variables: double integrals for joint distributions, gradients for optimisation, Lagrange multipliers for constrained portfolios.

          8 lesson films Plan this course
          Topics (8)
          1. 01Sequences & series▶ E1
          2. 02Power series▶ E2
          3. 03Partial derivatives▶ E3
          4. 04Gradient & Hessian▶ E4
          5. 05Lagrange multipliers▶ E5
          6. 06Double/triple integrals▶ E6
          7. 07Jacobians▶ E7
          8. 08Vector calculus (intro)▶ E8
          Learning outcomes
          • Evaluate double and triple integrals and change variables
          • Compute gradients, Hessians and constrained optima
          • Test convergence of series used in annuity and loss sums
          • Apply calculus to joint densities

          Prerequisites: MATH 101

          Key formula
          E[XY]=∬xy fX,Y(x,y) dx dy\mathbb{E}[XY]=\iint xy\,f_{X,Y}(x,y)\,dx\,dy
          Find lectures on Scout: “multivariable calculus course” →
          STAT 201Mathematics & Statistics4 credits

          Probability for Risk (Exam P)

          Rigorous calculus-based probability aligned with SOA Exam P / CAS Exam 1: univariate and multivariate distributions, transformations, moments and the insurance applications examined.

          8 lesson films Plan this course
          Topics (8)
          1. 01Combinatorics & conditional probability▶ E1
          2. 02Bayes' theorem▶ E2
          3. 03Discrete & continuous distributions▶ E3
          4. 04MGFs & transformations▶ E4
          5. 05Joint distributions & covariance▶ E5
          6. 06Conditional expectation▶ E6
          7. 07Central limit theorem▶ E7
          8. 08Insurance modifications▶ E8
          Learning outcomes
          • Compute probabilities, moments and MGFs for standard distributions
          • Work with joint, marginal and conditional distributions
          • Apply deductibles, limits and coinsurance to loss variables
          • Pass SOA Exam P

          Prerequisites: MATH 101

          Key formula
          E[X]=E[E[X∣Y]],Var⁡(X)=E[Var⁡(X∣Y)]+Var⁡(E[X∣Y])\mathbb{E}[X]=\mathbb{E}\big[\mathbb{E}[X\mid Y]\big],\quad \operatorname{Var}(X)=\mathbb{E}[\operatorname{Var}(X\mid Y)]+\operatorname{Var}(\mathbb{E}[X\mid Y])
          Find lectures on Scout: “exam P probability course actuarial” →
          CS 102Computer Science4 credits

          Programming II & Object-Oriented Design

          Object-oriented and functional design, complexity awareness and building a small actuarial library with proper packaging, typing and documentation.

          8 lesson films Plan this course
          Topics (8)
          1. 01OOP & design patterns▶ E1
          2. 02Functional programming▶ E2
          3. 03Recursion & complexity▶ E3
          4. 04Exceptions & robustness▶ E4
          5. 05Type hints▶ E5
          6. 06Packaging & docs▶ E6
          7. 07CI pipelines▶ E7
          8. 08Project: annuity library▶ E8
          Learning outcomes
          • Design classes and interfaces for a cash-flow engine
          • Reason about time and space complexity
          • Use typing, linting and CI
          • Package and document a reusable library

          Prerequisites: CS 101

          Exams, past papers & formula sheets

          Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

            Find lectures on Scout: “object oriented programming python course” →
            ECON 102Professional & Ethics3 credits

            Principles of Macroeconomics

            Inflation, interest rates, business cycles and monetary policy — the environment that drives investment returns, claim inflation and pension liabilities. Completes the Economics VEE.

            8 lesson films Plan this course
            Topics (8)
            1. 01GDP & national accounts▶ E1
            2. 02Inflation & unemployment▶ E2
            3. 03Money & central banking▶ E3
            4. 04Interest-rate determination▶ E4
            5. 05Business cycles▶ E5
            6. 06Fiscal policy▶ E6
            7. 07Open economy▶ E7
            8. 08Macro risk for insurers▶ E8
            Learning outcomes
            • Explain determinants of interest rates and inflation
            • Connect monetary policy to insurer asset returns
            • Interpret national accounts and growth
            • Analyse recessions' effect on claims and lapses
            Exams, past papers & formula sheets

            VEE · Economics

            Find lectures on Scout: “principles of macroeconomics course” →
            COMM 110Professional & Ethics2 credits

            Technical Writing & Communication for Quants

            Writing memos, explaining a model to a non-technical audience and presenting a recommendation — skills tested in every FSA/FCAS module and every job.

            8 lesson films Plan this course
            Topics (8)
            1. 01Audience analysis▶ E1
            2. 02Memo structure▶ E2
            3. 03Data visualisation principles▶ E3
            4. 04Presentations▶ E4
            5. 05Executive summaries▶ E5
            6. 06Peer review▶ E6
            7. 07Plain-language math▶ E7
            8. 08Ethics of communication▶ E8
            Learning outcomes
            • Write a one-page actuarial memo
            • Present quantitative results with clear visuals
            • Give and receive structured feedback
            • Cite sources and avoid plagiarism
            Exams, past papers & formula sheets

            Degree-internal course — no professional exam maps directly; supports professional & ethics skills used across the tracks.

              Find lectures on Scout: “technical writing for data scientists course” →

              Year 2 · Core actuarial & computing

              Semester 1 · 18 credits

              ACT 201Actuarial4 credits

              Financial Mathematics (Exam FM)

              Theory of interest: time value of money, annuities, loans, bonds, yield curves, duration and immunisation. Aligned with SOA Exam FM / CAS Exam 2.

              8 lesson films Plan this course
              Topics (8)
              1. 01Interest measures & force of interest▶ E1
              2. 02Annuities-certain▶ E2
              3. 03Loan amortisation▶ E3
              4. 04Bonds & yield▶ E4
              5. 05Term structure & spot/forward rates▶ E5
              6. 06Duration & convexity▶ E6
              7. 07Immunisation▶ E7
              8. 08Interest-rate swaps (intro)▶ E8
              Learning outcomes
              • Price annuities, loans and bonds under any interest pattern
              • Build amortisation and sinking-fund schedules
              • Compute duration and convexity; immunise a liability
              • Pass SOA Exam FM

              Prerequisites: MATH 101

              Key formula
              an‾∣=1−vni,Dmac=∑t vtCFt∑vtCFta_{\overline{n}|}=\frac{1-v^{n}}{i},\qquad D_{mac}=\frac{\sum t\,v^{t}CF_t}{\sum v^{t}CF_t}
              Find lectures on Scout: “financial mathematics exam FM course” →
              STAT 202Mathematics & Statistics4 credits

              Mathematical Statistics

              Estimation, hypothesis testing and inference from first principles: MLE, sufficiency, confidence intervals, likelihood-ratio tests. Satisfies the Mathematical Statistics VEE.

              8 lesson films Plan this course
              Topics (8)
              1. 01Sampling distributions▶ E1
              2. 02Point estimation & MLE▶ E2
              3. 03Fisher information & Cramér–Rao▶ E3
              4. 04Confidence intervals▶ E4
              5. 05Hypothesis tests & power▶ E5
              6. 06Likelihood-ratio tests▶ E6
              7. 07Bayesian estimation (intro)▶ E7
              8. 08Order statistics▶ E8
              Learning outcomes
              • Derive and evaluate estimators (bias, MSE, efficiency)
              • Construct confidence intervals and tests
              • Apply MLE and the delta method
              • Satisfy the Mathematical Statistics VEE

              Prerequisites: STAT 201

              Key formula
              θ^MLE=arg⁡max⁡θ∑ilog⁡f(xi;θ),n(θ^−θ)→dN ⁣(0,I(θ)−1)\hat\theta_{MLE}=\arg\max_\theta \sum_i \log f(x_i;\theta),\quad \sqrt{n}(\hat\theta-\theta)\xrightarrow{d}N\!\big(0,I(\theta)^{-1}\big)
              Exams, past papers & formula sheets

              VEE · Mathematical Statistics

              Find lectures on Scout: “mathematical statistics course inference” →
              CS 201Computer Science4 credits

              Data Structures & Algorithms

              Classic algorithms and data structures with analysis, applied to actuarial workloads such as triangle aggregation, policy matching and simulation scheduling.

              8 lesson films Plan this course
              Topics (8)
              1. 01Asymptotic analysis▶ E1
              2. 02Arrays, lists, stacks, queues▶ E2
              3. 03Hash tables▶ E3
              4. 04Trees & heaps▶ E4
              5. 05Graph algorithms▶ E5
              6. 06Sorting & searching▶ E6
              7. 07Dynamic programming▶ E7
              8. 08Greedy algorithms▶ E8
              Learning outcomes
              • Choose appropriate data structures for a workload
              • Analyse algorithms with Big-O
              • Implement sorting, hashing, trees and graphs
              • Design dynamic-programming solutions

              Prerequisites: CS 102

              Exams, past papers & formula sheets

              Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                Find lectures on Scout: “data structures and algorithms course” →
                CS 210Computer Science3 credits

                Databases & SQL for Insurance Data

                Relational modelling and SQL against realistic policy, exposure and claims schemas; introduces warehousing and data quality controls actuaries must document.

                8 lesson films Plan this course
                Topics (8)
                1. 01Relational model & ER design▶ E1
                2. 02SQL DML/DDL▶ E2
                3. 03Joins, aggregation, window functions▶ E3
                4. 04Indexes & query plans▶ E4
                5. 05Transactions▶ E5
                6. 06Data warehousing & star schemas▶ E6
                7. 07Data quality & lineage▶ E7
                8. 08Project: triangle from transactions▶ E8
                Learning outcomes
                • Design a normalised policy–claim–transaction schema
                • Write analytic SQL with joins, windows and CTEs
                • Build a loss triangle in SQL
                • Apply data-quality checks per ASOP 23

                Prerequisites: CS 101

                Exams, past papers & formula sheets

                Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                  Find lectures on Scout: “SQL databases course data analytics” →
                  FIN 201Professional & Ethics3 credits

                  Financial Accounting & Corporate Finance

                  Financial statements, insurer accounting basics, cost of capital, capital budgeting and valuation. Satisfies the Accounting & Finance VEE.

                  8 lesson films Plan this course
                  Topics (8)
                  1. 01Accounting cycle & statements▶ E1
                  2. 02Ratio analysis▶ E2
                  3. 03Time value & capital budgeting▶ E3
                  4. 04Cost of capital & CAPM▶ E4
                  5. 05Capital structure▶ E5
                  6. 06Insurer accounting & reserves▶ E6
                  7. 07IFRS 17 overview▶ E7
                  8. 08Valuation basics▶ E8
                  Learning outcomes
                  • Read and analyse balance sheets and income statements
                  • Compute NPV, IRR and WACC
                  • Explain insurer statutory vs GAAP/IFRS views
                  • Satisfy the Accounting & Finance VEE
                  Exams, past papers & formula sheets

                  VEE · Accounting & Finance

                  Find lectures on Scout: “corporate finance and financial accounting course” →

                  Semester 2 · 16 credits

                  ACT 202Actuarial4 credits

                  Long-Term Actuarial Mathematics I: Life Contingencies

                  Survival models, life tables, insurance and annuity present values, premiums and reserves — the long-term half of Exam FAM and foundation of ALTAM.

                  8 lesson films Plan this course
                  Topics (8)
                  1. 01Survival models & life tables▶ E1
                  2. 02Fractional ages▶ E2
                  3. 03Insurance EPVs▶ E3
                  4. 04Life annuities▶ E4
                  5. 05Premium principles▶ E5
                  6. 06Reserves & recursion▶ E6
                  7. 07Multiple-state models (intro)▶ E7
                  8. 08Profit testing (intro)▶ E8
                  Learning outcomes
                  • Work with survival functions, force of mortality and life tables
                  • Compute EPVs of insurances and annuities
                  • Derive net and gross premiums via equivalence
                  • Compute and interpret policy reserves

                  Prerequisites: ACT 201 STAT 201

                  Key formula
                  Aˉx=∫0∞vt tpx μx+t dt,a¨x=∑k≥0vk kpx\bar A_x=\int_0^\infty v^{t}\,{}_tp_x\,\mu_{x+t}\,dt,\qquad \ddot a_x=\sum_{k\ge0}v^{k}\,{}_kp_x
                  Find lectures on Scout: “life contingencies actuarial mathematics course” →
                  STAT 203AI & Machine Learning4 credits

                  Regression & Statistical Learning (Exam SRM)

                  Linear models, GLMs, regularisation, trees, PCA, clustering and time-series basics mapped to SOA Exam SRM, taught with insurance datasets.

                  8 lesson films Plan this course
                  Topics (8)
                  1. 01Linear regression & diagnostics▶ E1
                  2. 02GLMs & link functions▶ E2
                  3. 03Model selection & cross-validation▶ E3
                  4. 04Ridge/Lasso▶ E4
                  5. 05Decision trees & ensembles▶ E5
                  6. 06PCA & clustering▶ E6
                  7. 07Time series (AR/MA)▶ E7
                  8. 08Bias–variance trade-off▶ E8
                  Learning outcomes
                  • Fit and diagnose linear models and GLMs
                  • Apply cross-validation and regularisation
                  • Use trees, bagging and boosting
                  • Pass SOA Exam SRM

                  Prerequisites: STAT 202 MATH 111

                  Key formula
                  g(μi)=xi⊤β,β^ridge=arg⁡min⁡∥y−Xβ∥2+λ∥β∥2g(\mu_i)=x_i^{\top}\beta,\qquad \hat\beta_{ridge}=\arg\min\|y-X\beta\|^2+\lambda\|\beta\|^2
                  Find lectures on Scout: “statistical learning regression course exam SRM” →
                  CS 220Computer Science3 credits

                  Software Engineering & DevOps

                  Building maintainable systems in teams: requirements, architecture, testing, code review, containers and continuous delivery — applied to a pricing service.

                  8 lesson films Plan this course
                  Topics (8)
                  1. 01Agile & requirements▶ E1
                  2. 02Architecture & APIs▶ E2
                  3. 03Testing pyramid▶ E3
                  4. 04Code review▶ E4
                  5. 05Docker & CI/CD▶ E5
                  6. 06Observability & logging▶ E6
                  7. 07Security basics▶ E7
                  8. 08Team project▶ E8
                  Learning outcomes
                  • Work in an agile team with code review
                  • Write integration and property-based tests
                  • Containerise and deploy a service
                  • Document architecture decisions

                  Prerequisites: CS 102

                  Exams, past papers & formula sheets

                  Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                    Find lectures on Scout: “software engineering course devops” →
                    STAT 230Mathematics & Statistics3 credits

                    Stochastic Processes

                    Markov chains, Poisson processes, Brownian motion and martingales — the machinery behind multi-state models, claim arrivals, ruin theory and option pricing.

                    8 lesson films Plan this course
                    Topics (8)
                    1. 01Discrete-time Markov chains▶ E1
                    2. 02Continuous-time chains▶ E2
                    3. 03Poisson & compound Poisson▶ E3
                    4. 04Renewal processes▶ E4
                    5. 05Brownian motion▶ E5
                    6. 06Itô calculus (intro)▶ E6
                    7. 07Martingales▶ E7
                    8. 08Ruin theory▶ E8
                    Learning outcomes
                    • Analyse discrete and continuous-time Markov chains
                    • Model claim arrivals with (compound) Poisson processes
                    • Use Brownian motion and Itô's lemma at an introductory level
                    • Apply martingale arguments to ruin problems

                    Prerequisites: STAT 201 MATH 111

                    Key formula
                    ψ(u)≤e−Ru,λ MX(R)=λ+cR\psi(u)\le e^{-Ru},\quad \lambda\,M_X(R)=\lambda+cR
                    Library readings
                    Aggregate Loss Models
                    Find lectures on Scout: “stochastic processes course” →
                    ACT 210Integration & Practice2 credits

                    R & Python for Actuaries

                    Hands-on lab using the actuarial ecosystem: ChainLadder, lifecontingencies, actuar, statsmodels, scikit-learn and reproducible reports.

                    8 lesson films Plan this course
                    Topics (8)
                    1. 01R fundamentals & tidyverse▶ E1
                    2. 02actuar & ChainLadder▶ E2
                    3. 03lifecontingencies▶ E3
                    4. 04Python statsmodels▶ E4
                    5. 05scikit-learn pipelines▶ E5
                    6. 06Quarto/Jupyter reports▶ E6
                    7. 07Reproducibility▶ E7
                    8. 08Lab: rebuild a Library article▶ E8
                    Learning outcomes
                    • Run and validate reserving and life packages
                    • Reproduce a textbook result in code
                    • Produce a parameterised report
                    • Compare R and Python for the same task

                    Prerequisites: CS 101 ACT 201

                    Exams, past papers & formula sheets

                    Degree-internal course — no professional exam maps directly; supports integration & practice skills used across the tracks.

                      Find lectures on Scout: “R programming for actuaries course” →

                      Year 3 · Modelling, ML & practice

                      Semester 1 · 17 credits

                      ACT 301Actuarial4 credits

                      Short-Term Actuarial Mathematics: Loss Models

                      Severity, frequency and aggregate loss models, coverage modifications, parametric estimation and model selection — the short-term half of Exam FAM and core of ASTAM/MAS-I.

                      8 lesson films Plan this course
                      Topics (8)
                      1. 01Severity distributions & tails▶ E1
                      2. 02Frequency: (a,b,0) & (a,b,1)▶ E2
                      3. 03Coverage modifications▶ E3
                      4. 04Aggregate models & Panjer recursion▶ E4
                      5. 05Estimation: MLE, method of moments▶ E5
                      6. 06Goodness of fit▶ E6
                      7. 07Model selection▶ E7
                      8. 08Simulation▶ E8
                      Learning outcomes
                      • Fit and compare severity distributions
                      • Apply deductibles, limits and inflation to losses
                      • Compute aggregate loss distributions (Panjer, FFT, simulation)
                      • Pass SOA Exam FAM

                      Prerequisites: STAT 202

                      Key formula
                      pk=11−afX(0)∑j=1k(a+bjk)fX(j) pk−jp_k=\frac{1}{1-a f_X(0)}\sum_{j=1}^{k}\Big(a+\frac{bj}{k}\Big)f_X(j)\,p_{k-j}
                      Find lectures on Scout: “loss models actuarial course” →
                      ACT 310Actuarial3 credits

                      Ratemaking & Credibility

                      Basic ratemaking (CAS Exam 5) and credibility theory: exposure and premium on-levelling, trend, loss development, expense provisions, classification relativities and Bühlmann–Straub credibility.

                      8 lesson films Plan this course
                      Topics (8)
                      1. 01Exposure & premium bases▶ E1
                      2. 02On-level & trend▶ E2
                      3. 03Loss development for pricing▶ E3
                      4. 04Expense & profit provisions▶ E4
                      5. 05Rate indications▶ E5
                      6. 06Classification ratemaking▶ E6
                      7. 07Limited-fluctuation credibility▶ E7
                      8. 08Bühlmann & Bühlmann–Straub▶ E8
                      Learning outcomes
                      • Produce an overall rate indication by the loss-ratio and pure-premium methods
                      • Apply limited-fluctuation and Bühlmann credibility
                      • Compute class relativities with GLM or one-way methods
                      • Communicate a rate filing

                      Prerequisites: STAT 202 ACT 201

                      Key formula
                      Z=nn+K,K=E[Var⁡(X∣Θ)]Var⁡(E[X∣Θ])Z=\frac{n}{n+K},\quad K=\frac{\mathbb{E}[\operatorname{Var}(X\mid\Theta)]}{\operatorname{Var}(\mathbb{E}[X\mid\Theta])}
                      Find lectures on Scout: “ratemaking and credibility actuarial course” →
                      CS 301AI & Machine Learning4 credits

                      Machine Learning

                      Supervised and unsupervised learning from a statistical and optimisation viewpoint: gradient descent, kernels, boosting, neural nets, evaluation and calibration for insurance decisions.

                      8 lesson films Plan this course
                      Topics (8)
                      1. 01Optimisation & gradient descent▶ E1
                      2. 02Logistic regression & SVMs▶ E2
                      3. 03Kernels▶ E3
                      4. 04Gradient boosting (XGBoost/LightGBM)▶ E4
                      5. 05Neural networks (intro)▶ E5
                      6. 06Clustering & dimensionality reduction▶ E6
                      7. 07Calibration & evaluation▶ E7
                      8. 08Interpretability▶ E8
                      Learning outcomes
                      • Derive and implement core ML algorithms
                      • Tune models with proper validation and calibration
                      • Handle imbalanced, censored and truncated insurance data
                      • Interpret models with SHAP/partial dependence

                      Prerequisites: STAT 203 CS 201

                      Key formula
                      θt+1=θt−η ∇θL(θt)\theta_{t+1}=\theta_t-\eta\,\nabla_\theta \mathcal{L}(\theta_t)
                      Find lectures on Scout: “machine learning course” →
                      STAT 301Mathematics & Statistics3 credits

                      Bayesian Statistics & Decision Theory

                      Priors, posteriors, conjugacy, MCMC and Bayesian decision theory — the formal backbone of credibility, reserving uncertainty and the Decision-under-Uncertainty framework.

                      8 lesson films Plan this course
                      Topics (8)
                      1. 01Bayes' rule & conjugate families▶ E1
                      2. 02Prior elicitation▶ E2
                      3. 03Posterior predictive checks▶ E3
                      4. 04MCMC & diagnostics▶ E4
                      5. 05Hierarchical models▶ E5
                      6. 06Loss functions & Bayes rules▶ E6
                      7. 07EVPI / EVSI▶ E7
                      8. 08Bayesian credibility▶ E8
                      Learning outcomes
                      • Derive posteriors and predictive distributions
                      • Fit hierarchical models with MCMC (Stan/PyMC)
                      • Make decisions minimising expected loss
                      • Quantify the value of information (EVPI/EVSI)

                      Prerequisites: STAT 202

                      Key formula
                      π(θ∣x)∝f(x∣θ) π(θ),EVPI=Eθ[max⁡au(a,θ)]−max⁡aEθ[u(a,θ)]\pi(\theta\mid x)\propto f(x\mid\theta)\,\pi(\theta),\qquad \text{EVPI}=\mathbb{E}_\theta\big[\max_a u(a,\theta)\big]-\max_a\mathbb{E}_\theta[u(a,\theta)]
                      Exams, past papers & formula sheets

                      Degree-internal course — no professional exam maps directly; supports mathematics & statistics skills used across the tracks.

                        Find lectures on Scout: “bayesian statistics course” →
                        CS 310Computer Science3 credits

                        Numerical Methods & Scientific Computing

                        Root finding, quadrature, linear solvers, optimisation, FFT and Monte Carlo with error control — so that pricing and reserving code is fast and correct.

                        8 lesson films Plan this course
                        Topics (8)
                        1. 01Floating point & conditioning▶ E1
                        2. 02Root finding▶ E2
                        3. 03Quadrature▶ E3
                        4. 04Linear systems & decompositions▶ E4
                        5. 05Unconstrained/constrained optimisation▶ E5
                        6. 06FFT for aggregate losses▶ E6
                        7. 07Monte Carlo & variance reduction▶ E7
                        8. 08Vectorisation & profiling▶ E8
                        Learning outcomes
                        • Solve nonlinear equations (IRR, implied volatility) robustly
                        • Choose stable numerical integration and linear solvers
                        • Implement variance-reduction in Monte Carlo
                        • Vectorise and profile numerical code

                        Prerequisites: MATH 102 MATH 111 CS 201

                        Exams, past papers & formula sheets

                        Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                          Library readings
                          Aggregate Loss Models
                          Find lectures on Scout: “numerical methods scientific computing course” →

                          Semester 2 · 24 credits

                          ACT 320Actuarial4 credits

                          Loss Reserving & Stochastic Reserving

                          Development triangles, chain-ladder, Bornhuetter–Ferguson, Cape Cod, Berquist–Sherman diagnostics, Mack and bootstrap uncertainty, reserve ranges and ASOP 43 communication.

                          8 lesson films Plan this course
                          Topics (8)
                          1. 01Triangles & development factors▶ E1
                          2. 02Tail factors▶ E2
                          3. 03Chain-ladder, BF, Cape Cod▶ E3
                          4. 04Berquist–Sherman adjustments▶ E4
                          5. 05Frequency–severity methods▶ E5
                          6. 06Mack model▶ E6
                          7. 07Bootstrap ODP▶ E7
                          8. 08Reserve ranges & ASOP 43▶ E8
                          Learning outcomes
                          • Estimate unpaid claims with multiple deterministic methods
                          • Diagnose changing settlement and case-reserve adequacy
                          • Quantify reserve variability with Mack and bootstrap
                          • Select and document a point estimate and range

                          Prerequisites: ACT 310

                          Key formula
                          msep^(C^i,J)=C^i,J2∑k=I−iJ−1σ^k2f^k2(1C^i,k+1∑jCj,k)\widehat{msep}(\hat C_{i,J})=\hat C_{i,J}^{2}\sum_{k=I-i}^{J-1}\frac{\hat\sigma_k^2}{\hat f_k^2}\Big(\frac{1}{\hat C_{i,k}}+\frac{1}{\sum_{j}C_{j,k}}\Big)
                          Exams, past papers & formula sheets
                          Find lectures on Scout: “loss reserving actuarial course chain ladder” →
                          ACT 330Actuarial3 credits

                          Long-Term Actuarial Mathematics II & Pensions

                          Multiple-state and multiple-decrement models, joint lives, profit testing, universal life, pension plan valuation and retirement-benefit funding — mapped to Exam ALTAM.

                          8 lesson films Plan this course
                          Topics (8)
                          1. 01Multi-state models & Kolmogorov equations▶ E1
                          2. 02Multiple decrements▶ E2
                          3. 03Joint life & last survivor▶ E3
                          4. 04Profit testing▶ E4
                          5. 05Universal life & embedded options▶ E5
                          6. 06Pension funding methods▶ E6
                          7. 07Longevity & mortality improvement▶ E7
                          8. 08Retirement income products▶ E8
                          Learning outcomes
                          • Value benefits in multi-state and multi-decrement models
                          • Profit-test a universal life product
                          • Compute pension liabilities and normal costs under standard methods
                          • Model longevity risk

                          Prerequisites: ACT 202 STAT 230

                          Find lectures on Scout: “pension mathematics and multi-state life models course” →
                          CS 320AI & Machine Learning3 credits

                          Deep Learning

                          Neural network theory and practice: MLPs, CNNs, RNNs/Transformers, embeddings for high-cardinality rating factors, and mortality/claims forecasting with deep models.

                          8 lesson films Plan this course
                          Topics (8)
                          1. 01Backpropagation & optimisers▶ E1
                          2. 02Regularisation▶ E2
                          3. 03CNNs▶ E3
                          4. 04RNNs & attention▶ E4
                          5. 05Transformers▶ E5
                          6. 06Entity embeddings▶ E6
                          7. 07Combined Actuarial Neural Nets▶ E7
                          8. 08Uncertainty in deep models▶ E8
                          Learning outcomes
                          • Train and regularise deep networks with PyTorch
                          • Use embeddings for categorical insurance factors
                          • Apply sequence models to mortality and claims development
                          • Combine GLM and neural approaches (CANN)

                          Prerequisites: CS 301

                          Key formula
                          ∂L∂W(l)=δ(l) (a(l−1))⊤\frac{\partial \mathcal{L}}{\partial W^{(l)}}=\delta^{(l)}\,(a^{(l-1)})^{\top}
                          Find lectures on Scout: “deep learning course pytorch” →
                          STAT 320Mathematics & Statistics3 credits

                          Time Series & Forecasting

                          ARIMA, exponential smoothing, state-space and modern forecasting for premium, loss, inflation and mortality trends; forecast evaluation and scenario generation.

                          8 lesson films Plan this course
                          Topics (8)
                          1. 01Stationarity & ACF/PACF▶ E1
                          2. 02ARIMA & SARIMA▶ E2
                          3. 03Exponential smoothing▶ E3
                          4. 04State-space & Kalman filter▶ E4
                          5. 05Volatility (GARCH intro)▶ E5
                          6. 06Forecast evaluation▶ E6
                          7. 07Economic scenario generators▶ E7
                          8. 08Lee–Carter mortality▶ E8
                          Learning outcomes
                          • Identify and fit ARIMA/SARIMA models
                          • Build state-space and structural models
                          • Evaluate forecasts honestly (rolling origin)
                          • Produce trend factors with uncertainty bands

                          Prerequisites: STAT 203

                          Key formula
                          ϕ(B)(1−B)dyt=θ(B)εt\phi(B)(1-B)^d y_t=\theta(B)\varepsilon_t
                          Find lectures on Scout: “time series forecasting course” →
                          CS 330Computer Science3 credits

                          Distributed Systems, Cloud & Data Engineering

                          Scalable data pipelines for policy and claims data: Spark, orchestration, lakehouses, streaming and cloud cost/security controls, with governance for regulated data.

                          8 lesson films Plan this course
                          Topics (8)
                          1. 01Distributed computing concepts▶ E1
                          2. 02Spark & dataframes at scale▶ E2
                          3. 03Orchestration (Airflow)▶ E3
                          4. 04Lakehouse & Parquet▶ E4
                          5. 05Streaming▶ E5
                          6. 06Cloud services & IAM▶ E6
                          7. 07Data governance & privacy▶ E7
                          8. 08Project: claims pipeline▶ E8
                          Learning outcomes
                          • Build a batch and streaming pipeline
                          • Design a lakehouse for actuarial analytics
                          • Apply IAM, encryption and privacy controls
                          • Estimate and control cloud cost

                          Prerequisites: CS 210 CS 220

                          Exams, past papers & formula sheets

                          Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                            Find lectures on Scout: “data engineering cloud course spark” →
                            ACT 340Professional & Ethics2 credits

                            Professionalism, Ethics & Actuarial Standards

                            Code of Professional Conduct, ASOPs (23, 25, 41, 43, 56), model risk, regulatory duties and the ethics of algorithmic pricing — with case discussions.

                            8 lesson films Plan this course
                            Topics (8)
                            1. 01Code of Professional Conduct▶ E1
                            2. 02ASOP 23 data quality▶ E2
                            3. 03ASOP 25 credibility▶ E3
                            4. 04ASOP 41 communications▶ E4
                            5. 05ASOP 43 reserves▶ E5
                            6. 06ASOP 56 modelling▶ E6
                            7. 07Regulation & rate filings▶ E7
                            8. 08Algorithmic fairness law▶ E8
                            Learning outcomes
                            • Apply the Code of Conduct to realistic dilemmas
                            • Document work to ASOP 41 standards
                            • Assess model risk under ASOP 56
                            • Recognise unfair discrimination in AI pricing

                            Prerequisites: ACT 101

                            Exams, past papers & formula sheets
                            Find lectures on Scout: “actuarial professionalism ethics course” →
                            CS 340Computer Science3 credits
                            Elective

                            Cybersecurity, Privacy & Data Protection

                            Threat models, cryptography basics, privacy-enhancing technologies (differential privacy, federated learning) and the data-protection rules governing insurer data.

                            8 lesson films Plan this course
                            Topics (8)
                            1. 01Threat modelling▶ E1
                            2. 02Cryptography basics▶ E2
                            3. 03Authentication & authorisation▶ E3
                            4. 04Differential privacy▶ E4
                            5. 05Federated learning▶ E5
                            6. 06Secure development▶ E6
                            7. 07Privacy law▶ E7
                            8. 08Incident response▶ E8
                            Learning outcomes
                            • Threat-model an actuarial data pipeline
                            • Apply encryption and access control correctly
                            • Use differential privacy for released statistics
                            • Comply with GDPR/HIPAA-style rules

                            Prerequisites: CS 210

                            Exams, past papers & formula sheets

                            Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                              Find lectures on Scout: “cybersecurity and data privacy course” →
                              DS 310AI & Machine Learning3 credits
                              Elective

                              Data Science Lab for Actuaries (Exam PA / ATPA studio)

                              A studio course that runs the full data-science workflow on real insurance data: problem framing, feature engineering with exposure and censoring, GLM vs gradient-boosting bake-offs, calibration, and a written executive report in Exam PA style.

                              8 lesson films Plan this course
                              Topics (8)
                              1. 01Problem definition & data audit▶ E1
                              2. 02Exposure offsets & weights▶ E2
                              3. 03Feature engineering & leakage▶ E3
                              4. 04GLM vs GBM vs elastic net▶ E4
                              5. 05Calibration & lift charts▶ E5
                              6. 06Interpretation (PDP, SHAP)▶ E6
                              7. 07Report writing▶ E7
                              8. 08Reproducible pipelines▶ E8
                              Learning outcomes
                              • Frame a business question as a supervised-learning task with the right offset/weight
                              • Engineer exposure-aware features and handle leakage
                              • Compare GLM, GBM and penalised models with proper validation
                              • Write an Exam PA-style report with model justification

                              Prerequisites: STAT 203 CS 301

                              Key formula
                              log⁡E[Yi]=log⁡ei+xi⊤β\log \mathbb{E}[Y_i]=\log e_i+\mathbf{x}_i^{\top}\beta
                              Find lectures on Scout: “data science for insurance predictive analytics course” →

                              Year 4 · Fellowship-track & capstone

                              Semester 1 · 29 credits

                              ACT 401AI & Machine Learning3 credits

                              Predictive Analytics for Insurance (Exam PA)

                              End-to-end predictive-modelling projects in the style of SOA Exam PA / ATPA: business problem framing, data exploration, GLM/GBM/tree modelling, validation and executive reporting.

                              8 lesson films Plan this course
                              Topics (8)
                              1. 01Problem framing▶ E1
                              2. 02EDA & feature engineering▶ E2
                              3. 03GLM vs GBM▶ E3
                              4. 04Regularised regression▶ E4
                              5. 05Trees & random forests▶ E5
                              6. 06Model validation & lift▶ E6
                              7. 07Interpretation & recommendations▶ E7
                              8. 08Report writing▶ E8
                              Learning outcomes
                              • Frame a business problem as a modelling problem
                              • Build, compare and validate GLM and ML models
                              • Write a PA-style executive report
                              • Pass SOA Exam PA

                              Prerequisites: CS 301

                              Find lectures on Scout: “predictive analytics exam PA course” →
                              ACT 410Actuarial3 credits

                              Enterprise Risk Management, Risk Measures & Capital

                              Coherent risk measures, VaR/TVaR, copulas and dependence, economic capital, Solvency II/RBC, stress testing and ORSA — quantitative ERM for CERA and CAS Exam 7/9.

                              8 lesson films Plan this course
                              Topics (8)
                              1. 01Risk-measure axioms & coherence▶ E1
                              2. 02VaR, TVaR, spectral measures▶ E2
                              3. 03Copulas & tail dependence▶ E3
                              4. 04Capital allocation (Euler)▶ E4
                              5. 05Solvency II & RBC▶ E5
                              6. 06Stress testing▶ E6
                              7. 07Emerging risk▶ E7
                              8. 08ORSA & risk appetite▶ E8
                              Learning outcomes
                              • Compute and criticise VaR and TVaR
                              • Model dependence with copulas and allocate capital
                              • Design stress and reverse-stress tests
                              • Build an ORSA-style risk report

                              Prerequisites: ACT 301 STAT 301

                              Key formula
                              TVaRα(X)=11−α∫α1VaRu(X) du\mathrm{TVaR}_\alpha(X)=\frac{1}{1-\alpha}\int_\alpha^{1}\mathrm{VaR}_u(X)\,du
                              Exams, past papers & formula sheets
                              Find lectures on Scout: “enterprise risk management quantitative risk course” →
                              AI 401AI & Machine Learning3 credits

                              NLP & Large Language Models for Insurance

                              Text classification, information extraction from claims notes and policies, retrieval-augmented generation, evaluation and safe deployment of LLM assistants in regulated workflows.

                              8 lesson films Plan this course
                              Topics (8)
                              1. 01Tokenisation & embeddings▶ E1
                              2. 02Transformers▶ E2
                              3. 03Fine-tuning vs prompting▶ E3
                              4. 04Retrieval-augmented generation▶ E4
                              5. 05Structured extraction▶ E5
                              6. 06Evaluation & hallucination control▶ E6
                              7. 07Privacy & PII redaction▶ E7
                              8. 08Project: claims-notes triage▶ E8
                              Learning outcomes
                              • Build claims-text classifiers and extractors
                              • Design RAG systems over policy wordings
                              • Evaluate LLM outputs for accuracy and bias
                              • Govern LLM usage under privacy law

                              Prerequisites: CS 320

                              Exams, past papers & formula sheets

                              Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                Find lectures on Scout: “natural language processing large language models course” →
                                AI 410AI & Machine Learning3 credits

                                Decision Under Uncertainty & Reinforcement Learning

                                Sequential decisions: expected utility, robust and minimax-regret criteria, MDPs, dynamic programming, bandits and RL — applied to renewal pricing, reinsurance purchasing and dynamic hedging.

                                8 lesson films Plan this course
                                Topics (8)
                                1. 01Utility & risk preferences▶ E1
                                2. 02Robust & minimax-regret decisions▶ E2
                                3. 03MDPs & Bellman equations▶ E3
                                4. 04Dynamic programming▶ E4
                                5. 05Multi-armed bandits▶ E5
                                6. 06Policy gradient & Q-learning▶ E6
                                7. 07Off-policy evaluation▶ E7
                                8. 08Case: reinsurance purchasing▶ E8
                                Learning outcomes
                                • Formulate insurance decisions as MDPs
                                • Solve small problems by dynamic programming
                                • Apply bandit and policy-gradient methods safely
                                • Compare utility, robust and regret criteria

                                Prerequisites: STAT 301 CS 301

                                Key formula
                                V∗(s)=max⁡a[r(s,a)+γ∑s′P(s′∣s,a)V∗(s′)]V^*(s)=\max_a\Big[r(s,a)+\gamma\sum_{s'}P(s'\mid s,a)V^*(s')\Big]
                                Exams, past papers & formula sheets

                                Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                  Find lectures on Scout: “reinforcement learning and decision theory course” →
                                  ACT 490Integration & Practice3 credits
                                  Capstone

                                  Capstone I: Data-Driven Pricing & Reserving

                                  Team capstone on real public data (e.g. CAS Loss Reserve Database): build a reproducible reserving and pricing analysis combining classical methods with ML, under ASOP-grade documentation.

                                  8 lesson films Plan this course
                                  Topics (8)
                                  1. 01Project scoping▶ E1
                                  2. 02Data engineering▶ E2
                                  3. 03Reserving methods & diagnostics▶ E3
                                  4. 04Rate indication▶ E4
                                  5. 05ML enhancements▶ E5
                                  6. 06Uncertainty quantification▶ E6
                                  7. 07Peer review▶ E7
                                  8. 08Executive presentation▶ E8
                                  Learning outcomes
                                  • Deliver a reproducible end-to-end actuarial analysis
                                  • Compare classical and ML approaches with hindsight tests
                                  • Quantify uncertainty and communicate ranges
                                  • Present to a practitioner panel

                                  Prerequisites: ACT 320 ACT 310 CS 301

                                  Exams, past papers & formula sheets

                                  Degree-internal course — no professional exam maps directly; supports integration & practice skills used across the tracks.

                                    Find lectures on Scout: “actuarial capstone project reserving pricing” →
                                    ACT 480Integration & Practice2 credits

                                    Research Methods & Actuarial Research Seminar

                                    Reading and reproducing current research from ASTIN Bulletin, NAAJ, Variance and top ML venues; writing a short paper and reviewing peers' work.

                                    8 lesson films Plan this course
                                    Topics (8)
                                    1. 01Literature search▶ E1
                                    2. 02Reproducibility▶ E2
                                    3. 03Research design▶ E3
                                    4. 04Statistical rigour▶ E4
                                    5. 05Writing a paper▶ E5
                                    6. 06Peer review▶ E6
                                    7. 07Open science & code▶ E7
                                    8. 08Research ethics▶ E8
                                    Learning outcomes
                                    • Critically read a research paper
                                    • Reproduce a published result
                                    • Write a research note with proper citations
                                    • Referee a peer's paper

                                    Prerequisites: STAT 301

                                    Exams, past papers & formula sheets

                                    Degree-internal course — no professional exam maps directly; supports integration & practice skills used across the tracks.

                                      Find lectures on Scout: “research methods for data science course” →
                                      AI 440AI & Machine Learning3 credits
                                      Elective

                                      Causal Inference for Pricing & Claims

                                      Potential outcomes, DAGs, instrumental variables, difference-in-differences and uplift modelling — measuring the true effect of rate changes, claims interventions and retention offers.

                                      8 lesson films Plan this course
                                      Topics (8)
                                      1. 01Potential outcomes▶ E1
                                      2. 02DAGs & d-separation▶ E2
                                      3. 03Matching & propensity▶ E3
                                      4. 04Instrumental variables▶ E4
                                      5. 05Difference-in-differences▶ E5
                                      6. 06Regression discontinuity▶ E6
                                      7. 07Uplift modelling▶ E7
                                      8. 08Causal ML (DML)▶ E8
                                      Learning outcomes
                                      • Draw and analyse causal DAGs
                                      • Estimate treatment effects with matching, IV and DiD
                                      • Build uplift models for retention
                                      • Distinguish prediction from intervention

                                      Prerequisites: STAT 203

                                      Exams, past papers & formula sheets

                                      Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                        Find lectures on Scout: “causal inference course” →
                                        FIN 420Mathematics & Statistics3 credits
                                        Elective

                                        Financial Engineering & Derivatives

                                        No-arbitrage pricing, binomial and Black–Scholes models, Greeks, hedging embedded guarantees in variable annuities and interest-rate models — for the quantitative-finance track and ALTAM/QFI.

                                        8 lesson films Plan this course
                                        Topics (8)
                                        1. 01No-arbitrage & replication▶ E1
                                        2. 02Binomial trees▶ E2
                                        3. 03Black–Scholes & Greeks▶ E3
                                        4. 04Exotic options▶ E4
                                        5. 05Variable annuity guarantees▶ E5
                                        6. 06Interest-rate models▶ E6
                                        7. 07Monte Carlo pricing▶ E7
                                        8. 08Hedging & model risk▶ E8
                                        Learning outcomes
                                        • Price options in binomial and Black–Scholes settings
                                        • Hedge embedded guarantees dynamically
                                        • Calibrate short-rate models
                                        • Assess model risk in hedging

                                        Prerequisites: STAT 230 ACT 201

                                        Key formula
                                        C=S0N(d1)−Ke−rTN(d2)C=S_0N(d_1)-Ke^{-rT}N(d_2)
                                        Find lectures on Scout: “financial engineering derivatives course” →
                                        CS 450Computer Science3 credits
                                        Elective

                                        High-Performance & GPU Computing for Simulation

                                        Parallel and GPU programming for nested stochastic valuation, large-scale bootstrap and cat simulation; profiling, memory hierarchies and cost-aware scaling.

                                        8 lesson films Plan this course
                                        Topics (8)
                                        1. 01Parallel patterns▶ E1
                                        2. 02Multiprocessing & Dask▶ E2
                                        3. 03GPU programming (JAX)▶ E3
                                        4. 04Nested stochastic valuation▶ E4
                                        5. 05Least-squares Monte Carlo▶ E5
                                        6. 06Profiling▶ E6
                                        7. 07Cloud scaling▶ E7
                                        8. 08Reproducibility at scale▶ E8
                                        Learning outcomes
                                        • Parallelise a Monte Carlo valuation
                                        • Write GPU kernels via JAX/CUDA-Python
                                        • Profile and remove bottlenecks
                                        • Balance accuracy, runtime and cost

                                        Prerequisites: CS 310

                                        Exams, past papers & formula sheets

                                        Degree-internal course — no professional exam maps directly; supports computer science skills used across the tracks.

                                          Find lectures on Scout: “GPU computing parallel programming course” →
                                          AI 460AI & Machine Learning3 credits
                                          Elective

                                          Deep Learning for Time Series, Mortality & Claims Forecasting

                                          Sequence models for actuarial forecasting: Lee–Carter and its neural extensions, recurrent and attention models for claims development, probabilistic forecasts and backtesting against chain-ladder baselines.

                                          8 lesson films Plan this course
                                          Topics (8)
                                          1. 01Lee–Carter & CBD models▶ E1
                                          2. 02RNN/LSTM/Transformer basics▶ E2
                                          3. 03Neural mortality models▶ E3
                                          4. 04Claims development as sequences▶ E4
                                          5. 05Probabilistic forecasting & quantile loss▶ E5
                                          6. 06Backtesting vs chain ladder▶ E6
                                          7. 07Conformal prediction▶ E7
                                          8. 08Model risk▶ E8
                                          Learning outcomes
                                          • Fit Lee–Carter and a neural mortality model and compare forecasts
                                          • Build sequence models for incremental claims development
                                          • Produce calibrated prediction intervals
                                          • Backtest forecasts against actuarial baselines

                                          Prerequisites: CS 320 STAT 320

                                          Key formula
                                          log⁡mx,t=ax+bxkt+εx,t\log m_{x,t}=a_x+b_x k_t+\varepsilon_{x,t}
                                          Find lectures on Scout: “deep learning time series forecasting mortality course” →

                                          Semester 2 · 27 credits

                                          AI 420AI & Machine Learning3 credits

                                          Explainable AI, Fairness & Model Governance in Insurance

                                          Interpretability methods, fairness metrics and mitigation, proxy discrimination testing, model risk management, validation frameworks and regulatory expectations (NAIC, EU AI Act, Colorado SB21-169).

                                          8 lesson films Plan this course
                                          Topics (8)
                                          1. 01SHAP, PDP, ALE▶ E1
                                          2. 02Global vs local explanations▶ E2
                                          3. 03Fairness definitions & trade-offs▶ E3
                                          4. 04Proxy discrimination testing▶ E4
                                          5. 05Bias mitigation▶ E5
                                          6. 06Model risk management & ASOP 56▶ E6
                                          7. 07Validation & monitoring▶ E7
                                          8. 08Regulation of AI in insurance▶ E8
                                          Learning outcomes
                                          • Explain any pricing model to a regulator
                                          • Measure and mitigate disparate impact
                                          • Run an independent model validation
                                          • Write a model governance policy

                                          Prerequisites: ACT 401 ACT 340

                                          Exams, past papers & formula sheets

                                          Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                            Find lectures on Scout: “explainable AI fairness in machine learning course” →
                                            AI 430AI & Machine Learning3 credits

                                            MLOps, Agents & Automation of Actuarial Workflows

                                            Productionising models and building tool-using AI agents that run reserving, pricing and reporting pipelines with human-in-the-loop controls, audit trails and evaluation harnesses.

                                            8 lesson films Plan this course
                                            Topics (8)
                                            1. 01Feature stores & registries▶ E1
                                            2. 02Deployment patterns▶ E2
                                            3. 03Monitoring & drift▶ E3
                                            4. 04Agent architectures & tools▶ E4
                                            5. 05Function calling & structured output▶ E5
                                            6. 06Guardrails & approvals▶ E6
                                            7. 07Evaluation harnesses▶ E7
                                            8. 08Project: reserving agent▶ E8
                                            Learning outcomes
                                            • Deploy and monitor a model with drift detection
                                            • Build a tool-using agent for an actuarial task
                                            • Design guardrails and human approval steps
                                            • Evaluate agents with test suites

                                            Prerequisites: AI 401 CS 330

                                            Exams, past papers & formula sheets

                                            Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                              Find lectures on Scout: “MLOps and AI agents course” →
                                              ACT 420Actuarial3 credits

                                              Health Insurance, Reinsurance & Catastrophe Modelling

                                              Health pricing and risk adjustment, reinsurance structures and pricing, catastrophe model anatomy, climate risk and extreme-value methods for tail exposure.

                                              8 lesson films Plan this course
                                              Topics (8)
                                              1. 01Health pricing & risk adjustment▶ E1
                                              2. 02Quota share & excess of loss▶ E2
                                              3. 03Experience & exposure rating▶ E3
                                              4. 04Cat model components▶ E4
                                              5. 05EP curves & PML▶ E5
                                              6. 06Climate risk▶ E6
                                              7. 07Extreme value theory▶ E7
                                              8. 08Reinsurance optimisation▶ E8
                                              Learning outcomes
                                              • Price group health with trend and risk adjustment
                                              • Structure and price XoL and quota-share treaties
                                              • Interpret catastrophe model output (EP curves)
                                              • Apply EVT to large-loss data

                                              Prerequisites: ACT 301 ACT 310

                                              Key formula
                                              P{X−u>x∣X>u}≈(1+ξxβ)−1/ξP\{X-u>x\mid X>u\}\approx\Big(1+\xi\frac{x}{\beta}\Big)^{-1/\xi}
                                              Find lectures on Scout: “reinsurance and catastrophe modelling course” →
                                              ACT 491Integration & Practice3 credits
                                              Capstone

                                              Capstone II: InsurTech Product Studio

                                              Design, build and pitch a working insurance product or actuarial tool (pricing API, reserving dashboard, LLM assistant) with a business case, governance pack and live demo.

                                              8 lesson films Plan this course
                                              Topics (8)
                                              1. 01Product discovery▶ E1
                                              2. 02Architecture & build▶ E2
                                              3. 03Pricing & profitability▶ E3
                                              4. 04Capital & risk view▶ E4
                                              5. 05Governance pack▶ E5
                                              6. 06User testing▶ E6
                                              7. 07Demo day▶ E7
                                              8. 08Retrospective▶ E8
                                              Learning outcomes
                                              • Ship a deployed prototype with tests and docs
                                              • Produce a business case with pricing and capital view
                                              • Prepare a governance and compliance pack
                                              • Pitch to industry judges

                                              Prerequisites: ACT 490 CS 220

                                              Exams, past papers & formula sheets

                                              Degree-internal course — no professional exam maps directly; supports integration & practice skills used across the tracks.

                                                Find lectures on Scout: “insurtech product development course” →
                                                ACT 495Professional & Ethics2 credits

                                                Internship / Co-op Practicum

                                                Supervised professional experience (minimum 320 hours) in an insurer, consultancy, regulator or InsurTech, with a reflective report mapped to competency frameworks.

                                                6 lesson films Plan this course
                                                Topics (6)
                                                1. 01Placement search▶ E1
                                                2. 02Workplace professionalism▶ E2
                                                3. 03Competency log▶ E3
                                                4. 04Mentor meetings▶ E4
                                                5. 05Reflective report▶ E5
                                                6. 06Presentation▶ E6
                                                Learning outcomes
                                                • Apply coursework in a professional setting
                                                • Document work to professional standards
                                                • Reflect on ethical situations encountered
                                                • Build a professional network

                                                Prerequisites: ACT 340

                                                Exams, past papers & formula sheets

                                                Degree-internal course — no professional exam maps directly; supports professional & ethics skills used across the tracks.

                                                  Find lectures on Scout: “actuarial internship program” →
                                                  ACT 499Integration & Practice4 credits

                                                  Capstone Case Study: The Full Actuarial Engagement

                                                  One continuous, story-driven engagement on a real Workers' Compensation book (CAS Loss Reserve Database, Schedule P 1988–1997): from data intake and reserving through rate indication, credibility, class relativities, economic-scenario discounting, capital, governance sign-off and the regulator hearing. Every episode advances the same case; the practice exam tests the whole engagement at certification difficulty.

                                                  8 lesson films Plan this course
                                                  Topics (8)
                                                  1. 01Engagement kickoff: the book, the data and the mandate▶ E1
                                                  2. 02Reserving the book: Chain Ladder, BF and Cape Cod on Schedule P▶ E2
                                                  3. 03How wrong could we be: Mack, bootstrap and reserve ranges▶ E3
                                                  4. 04The rate indication: on-level premium, trend, development and expenses▶ E4
                                                  5. 05Credibility, class relativities and the experience-rating plan▶ E5
                                                  6. 06Investment income and the rate engine: Treasury curve and NAIC scenarios▶ E6
                                                  7. 07Capital, TVaR and the price of risk▶ E7
                                                  8. 08Governance, ASOPs and the regulator hearing▶ E8
                                                  Learning outcomes
                                                  • Run a complete reserving and pricing engagement on real industry data
                                                  • Defend method choices, credibility and rate actions to a regulator
                                                  • Quantify reserve and rate uncertainty (Mack, bootstrap, CTE) and translate it into capital
                                                  • Produce ASOP 41/43/56-compliant documentation and communicate a rate filing

                                                  Prerequisites: ACT 490

                                                  Exams, past papers & formula sheets
                                                  Find lectures on Scout: “actuarial capstone case study workers compensation pricing reserving” →
                                                  AI 450AI & Machine Learning3 credits
                                                  Elective

                                                  Generative Models & Synthetic Insurance Data

                                                  VAEs, GANs, diffusion and copula-based simulators for creating privacy-safe synthetic portfolios, stress scenarios and augmented training data, with fidelity and privacy evaluation.

                                                  8 lesson films Plan this course
                                                  Topics (8)
                                                  1. 01Density estimation▶ E1
                                                  2. 02VAEs▶ E2
                                                  3. 03GANs▶ E3
                                                  4. 04Diffusion models▶ E4
                                                  5. 05Copula simulators▶ E5
                                                  6. 06Tabular synthesis▶ E6
                                                  7. 07Fidelity & privacy metrics▶ E7
                                                  8. 08Scenario generation▶ E8
                                                  Learning outcomes
                                                  • Train generative models on tabular insurance data
                                                  • Evaluate fidelity, utility and privacy leakage
                                                  • Generate coherent economic and claims scenarios
                                                  • Document synthetic data for regulators

                                                  Prerequisites: CS 320

                                                  Exams, past papers & formula sheets

                                                  Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                                    Library readings
                                                    Dependence and Copulas
                                                    Find lectures on Scout: “generative models synthetic data course” →
                                                    ACT 460Actuarial3 credits
                                                    Elective

                                                    P&C Financial Reporting, Regulation & Solvency

                                                    Statutory accounting, Schedule P, Statements of Actuarial Opinion, RBC, IFRS 17 and the regulatory environment — the core of CAS Exam 6.

                                                    8 lesson films Plan this course
                                                    Topics (8)
                                                    1. 01Statutory vs GAAP▶ E1
                                                    2. 02Annual Statement & Schedule P▶ E2
                                                    3. 03RBC▶ E3
                                                    4. 04IFRS 17▶ E4
                                                    5. 05SAO & ASOP 36▶ E5
                                                    6. 06Rate regulation▶ E6
                                                    7. 07Guaranty funds▶ E7
                                                    8. 08Solvency monitoring▶ E8
                                                    Learning outcomes
                                                    • Reconcile Schedule P with the reserving analysis
                                                    • Explain RBC and IFRS 17 mechanics
                                                    • Draft a Statement of Actuarial Opinion outline
                                                    • Assess insurer solvency indicators

                                                    Prerequisites: ACT 320 FIN 201

                                                    Exams, past papers & formula sheets
                                                    Find lectures on Scout: “insurance financial reporting and regulation course” →
                                                    AI 470AI & Machine Learning3 credits
                                                    Elective

                                                    Reinforcement Learning for Dynamic Pricing, Hedging & Claims Triage

                                                    Sequential decisions under uncertainty for insurers: Markov decision processes, bandits for renewal pricing, deep hedging of embedded guarantees and claims-triage policies, with constraints for fairness and solvency.

                                                    8 lesson films Plan this course
                                                    Topics (8)
                                                    1. 01MDPs & dynamic programming▶ E1
                                                    2. 02Bandits & contextual bandits▶ E2
                                                    3. 03Policy gradient & actor–critic▶ E3
                                                    4. 04Deep hedging of guarantees▶ E4
                                                    5. 05Claims triage & fraud routing▶ E5
                                                    6. 06Off-policy evaluation▶ E6
                                                    7. 07Constrained & safe RL▶ E7
                                                    8. 08Regulatory constraints▶ E8
                                                    Learning outcomes
                                                    • Formulate an insurance decision as an MDP or bandit
                                                    • Train and evaluate an RL policy with off-policy checks
                                                    • Implement deep hedging and compare with delta hedging
                                                    • Impose fairness and capital constraints on learned policies

                                                    Prerequisites: AI 410

                                                    Key formula
                                                    Q∗(s,a)=r(s,a)+γ E[max⁡a′Q∗(s′,a′)]Q^{*}(s,a)=r(s,a)+\gamma\,\mathbb{E}\big[\max_{a'}Q^{*}(s',a')\big]
                                                    Exams, past papers & formula sheets
                                                    Find lectures on Scout: “reinforcement learning dynamic pricing finance course” →

                                                    Year 5 · M.Sc. / research

                                                    Semester 1 · 9 credits

                                                    ACT 601Actuarial3 credits

                                                    Advanced Stochastic Reserving & Micro-Level Models

                                                    Individual-claim (micro-level) reserving, GLM/GBM/neural reserving, Bayesian MCMC reserving and one-year reserve risk under Solvency II.

                                                    Generate lesson films Plan this course
                                                    Topics (6)
                                                    1. 01Micro-level reserving
                                                    2. 02Neural & GBM reserving
                                                    3. 03Bayesian reserving
                                                    4. 04Merz–Wüthrich one-year risk
                                                    5. 05Model validation
                                                    6. 06Case study
                                                    Learning outcomes
                                                    • Fit micro-level reserving models
                                                    • Compare aggregate and individual approaches
                                                    • Quantify one-year reserve risk
                                                    • Publish a reproducible reserving study

                                                    Prerequisites: ACT 320 CS 301

                                                    Exams, past papers & formula sheets

                                                    Degree-internal course — no professional exam maps directly; supports actuarial skills used across the tracks.

                                                      Find lectures on Scout: “advanced stochastic reserving graduate course” →
                                                      AI 601AI & Machine Learning3 credits

                                                      Probabilistic Machine Learning & Uncertainty Quantification

                                                      Gaussian processes, Bayesian deep learning, conformal prediction and distributional regression for calibrated predictive distributions in pricing and capital.

                                                      Generate lesson films Plan this course
                                                      Topics (6)
                                                      1. 01Gaussian processes
                                                      2. 02Variational inference
                                                      3. 03Bayesian deep learning
                                                      4. 04Conformal prediction
                                                      5. 05Distributional regression
                                                      6. 06Proper scoring rules
                                                      Learning outcomes
                                                      • Fit GP and Bayesian neural models
                                                      • Produce calibrated prediction intervals
                                                      • Use conformal methods with guarantees
                                                      • Evaluate probabilistic forecasts with proper scoring rules

                                                      Prerequisites: STAT 301 CS 320

                                                      Exams, past papers & formula sheets

                                                      Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                                        Find lectures on Scout: “probabilistic machine learning graduate course” →
                                                        ACT 610Actuarial3 credits

                                                        Advanced Ratemaking & Individual Risk Rating

                                                        Experience and retrospective rating, large-account pricing, ILFs and excess pricing, GLM-to-GBM migration and price optimisation ethics — CAS Exam 8 depth.

                                                        Generate lesson films Plan this course
                                                        Topics (6)
                                                        1. 01Experience rating
                                                        2. 02Retrospective rating
                                                        3. 03ILFs & exposure curves
                                                        4. 04Large-account pricing
                                                        5. 05Advanced GLM/GBM rating
                                                        6. 06Price optimisation & regulation
                                                        Learning outcomes
                                                        • Design experience and retro rating plans
                                                        • Price excess layers with ILFs and exposure curves
                                                        • Migrate a rating plan from GLM to GBM responsibly
                                                        • Assess price-optimisation constraints

                                                        Prerequisites: ACT 310 ACT 401

                                                        Find lectures on Scout: “advanced ratemaking individual risk rating course” →

                                                        Semester 2 · 15 credits

                                                        AI 610AI & Machine Learning3 credits

                                                        Trustworthy AI Systems & Regulation Lab

                                                        Lab-based study of AI assurance: red-teaming, robustness testing, documentation standards (model cards, datasheets), audits and regulatory sandboxes for insurance AI.

                                                        Generate lesson films Plan this course
                                                        Topics (6)
                                                        1. 01AI assurance frameworks
                                                        2. 02Robustness & adversarial testing
                                                        3. 03Red-teaming
                                                        4. 04Model cards & datasheets
                                                        5. 05Audit methodology
                                                        6. 06Regulatory sandboxes
                                                        Learning outcomes
                                                        • Audit an AI pricing system end-to-end
                                                        • Red-team an LLM assistant
                                                        • Produce assurance documentation
                                                        • Advise on regulatory compliance

                                                        Prerequisites: AI 420

                                                        Exams, past papers & formula sheets

                                                        Degree-internal course — no professional exam maps directly; supports ai & machine learning skills used across the tracks.

                                                          Find lectures on Scout: “trustworthy AI auditing course” →
                                                          ACT 620Actuarial3 credits

                                                          Longevity, Health Analytics & Mortality Modelling

                                                          Stochastic mortality models (Lee–Carter, CBD, APC), machine-learning mortality, health-claims analytics and longevity risk transfer.

                                                          Generate lesson films Plan this course
                                                          Topics (6)
                                                          1. 01Lee–Carter & extensions
                                                          2. 02CBD & APC models
                                                          3. 03ML mortality models
                                                          4. 04Health-claims analytics
                                                          5. 05Longevity risk transfer
                                                          6. 06Model risk
                                                          Learning outcomes
                                                          • Fit and forecast stochastic mortality models
                                                          • Apply ML to mortality and morbidity
                                                          • Price longevity swaps and buy-ins
                                                          • Assess model risk in longevity

                                                          Prerequisites: ACT 330 STAT 320

                                                          Exams, past papers & formula sheets

                                                          Degree-internal course — no professional exam maps directly; supports actuarial skills used across the tracks.

                                                            Find lectures on Scout: “mortality modelling longevity analytics course” →
                                                            ACT 690Integration & Practice9 credits
                                                            Capstone

                                                            M.Sc. Thesis

                                                            An original research contribution at the intersection of actuarial science, computing and AI, supervised jointly by an actuarial and a computer-science faculty member, with open code.

                                                            Generate lesson films Plan this course
                                                            Topics (6)
                                                            1. 01Proposal
                                                            2. 02Literature review
                                                            3. 03Methodology
                                                            4. 04Experiments
                                                            5. 05Writing
                                                            6. 06Defence
                                                            Learning outcomes
                                                            • Formulate a research question
                                                            • Execute a rigorous study
                                                            • Write and defend a thesis
                                                            • Release reproducible code and data

                                                            Prerequisites: ACT 480

                                                            Exams, past papers & formula sheets

                                                            Degree-internal course — no professional exam maps directly; supports integration & practice skills used across the tracks.

                                                              Find lectures on Scout: “actuarial science research thesis topics machine learning” →
                                                              Ask the tutor