D'Math University | Finance & Actuarial

MSc Risk Management & Mathematics

An advanced postgraduate programme that bridges rigorous mathematical modelling and professional risk management practice. Covering Value-at-Risk, credit risk, regulatory capital frameworks, stochastic calculus, and enterprise risk — graduates are equipped for senior roles at banks, insurers, and financial regulators worldwide.

Postgraduate 1 Year Online & Blended Finance & Actuarial
12
Core Modules
£68k
Average Graduate Salary
50+
Partner Universities
1
Year Programme

Programme Overview

Programme Overview

  • Integrated study of mathematical risk theory and professional risk management frameworks
  • Semester 1: Stochastic calculus, statistical methods, and financial risk measures
  • Semester 2: Credit risk, market risk, operational risk, and regulatory capital
  • Covers Basel III/IV, Solvency II, and IFRS 17 frameworks in detail
  • Quantitative modelling in Python and R with financial market datasets
  • Dissertation on a contemporary risk management problem
  • Strong preparation for FRM, PRM, and actuarial professional qualifications

Entry Requirements

  • BSc in Mathematics, Statistics, Finance, or Actuarial Science (2:1 or above)
  • Strong quantitative background including probability and linear algebra
  • Basic familiarity with financial instruments advantageous but not required
  • Programming experience in Python or R preferred
  • Industry experience in finance or insurance valued for mature applicants
  • Two academic or professional references
  • English proficiency: IELTS 6.5+ or equivalent

Core Curriculum

📐
Stochastic Calculus for Risk
Brownian motion, Itô's lemma, SDEs, and their application to option pricing and interest rate modelling.
📉
Market Risk & VaR
Value-at-Risk, Expected Shortfall, historical simulation, Monte Carlo methods, and stress testing frameworks.
💳
Credit Risk Modelling
Structural models, reduced-form models, credit derivatives, CDO pricing, and counterparty credit risk.
⚙️
Operational Risk
Loss distribution approach, scenario analysis, key risk indicators, and AMA capital calculation methods.
🏛️
Regulatory Frameworks
Basel III/IV capital requirements, FRTB, Solvency II, IFRS 9 and 17, and the mathematics behind regulatory models.
📊
Copulas & Dependence
Modelling joint distributions, tail dependence, Gaussian and Archimedean copulas for portfolio risk.
🌐
Enterprise Risk Management
Risk appetite frameworks, ORSA, risk aggregation, economic capital, and board-level risk governance.
📋
Dissertation
Original applied research on a contemporary risk management challenge, with industry supervision where available.

Course Catalogue

Click any course to view its objective and learning outcomes.

RMM 501 Probability & Stochastic Processes +

Objective

To master probability and stochastic processes for risk modelling.

Learning Outcomes

  • Apply measure-theoretic probability.
  • Use martingale theory.
  • Apply Brownian motion.
  • Use Itô calculus.
  • Apply Lévy processes.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 502 Quantitative Risk Measures +

Objective

To compute and analyse modern risk measures.

Learning Outcomes

  • Compute VaR via parametric and historical methods.
  • Apply Expected Shortfall.
  • Use coherent risk measures.
  • Apply law-invariant measures.
  • Discuss elicitability.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 503 Market Risk +

Objective

To model and manage market risk in portfolios.

Learning Outcomes

  • Apply factor models.
  • Use principal components for risk.
  • Apply GARCH for volatility.
  • Stress-test portfolios.
  • Use Monte Carlo for VaR.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 504 Credit Risk +

Objective

To model credit risk and price credit derivatives.

Learning Outcomes

  • Apply structural and reduced-form models.
  • Price CDS.
  • Use CDO pricing.
  • Apply correlation models.
  • Discuss CVA.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 505 Operational Risk +

Objective

To model operational risk losses.

Learning Outcomes

  • Apply loss distribution approach.
  • Use scenario analysis.
  • Apply extreme value theory.
  • Use Bayesian methods.
  • Discuss Basel II/III operational risk.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 506 Liquidity Risk +

Objective

To measure and manage liquidity risk.

Learning Outcomes

  • Apply liquidity coverage ratio.
  • Use net stable funding ratio.
  • Apply funding cost models.
  • Stress-test liquidity.
  • Discuss liquidity in crises.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 507 Insurance Risk +

Objective

To model insurance risks and reserves.

Learning Outcomes

  • Apply collective risk model.
  • Use Cramér-Lundberg.
  • Apply chain-ladder reserving.
  • Use stochastic reserving.
  • Discuss Solvency II.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 508 Risk Aggregation +

Objective

To aggregate risks across portfolios using copulas.

Learning Outcomes

  • Apply copula theory.
  • Use Gaussian and t copulas.
  • Apply Archimedean copulas.
  • Use vine copulas.
  • Aggregate risks coherently.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 509 Extreme Value Theory +

Objective

To model and estimate extreme events.

Learning Outcomes

  • Apply block maxima method.
  • Use peaks-over-threshold.
  • Apply generalised extreme value.
  • Use generalised Pareto.
  • Estimate tail risk.
RMM 510 Regulatory Frameworks +

Objective

To navigate Basel, Solvency II and IFRS requirements.

Learning Outcomes

  • Apply Basel III for banks.
  • Use Solvency II for insurers.
  • Apply IFRS 9 for credit losses.
  • Build internal capital models.
  • Conduct ORSA.
RMM 511 Risk Management Software +

Objective

To implement risk models in industrial software.

Learning Outcomes

  • Use R or Python for risk computation.
  • Apply Monte Carlo simulation.
  • Build risk dashboards.
  • Use validation frameworks.
  • Document models for audit.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
RMM 512 Master's Project +

Objective

To complete an original risk management research project.

Learning Outcomes

  • Identify a research-quality problem.
  • Apply rigorous methodology.
  • Use real risk data.
  • Write a 15,000-word dissertation.
  • Defend orally.

Career Pathways

🏦
Market Risk Analyst
Model and report trading book risk at investment banks, measuring VaR, sensitivity, and scenario exposures daily.
💳
Credit Risk Manager
Develop credit scoring, PD/LGD models, and stress-test frameworks for retail and corporate lending portfolios.
📋
Regulatory Capital Analyst
Support Basel capital calculations, ICAAP/ILAAP submissions, and regulatory reporting at major financial institutions.
🏛️
Financial Regulator
Join the FCA, PRA, ECB, or equivalent supervisory authorities in roles reviewing firms' risk models and capital adequacy.
📉
Insurance Risk Actuary
Model reserving, solvency, and catastrophe risk within the Solvency II framework for life and non-life insurers.
🔬
Quantitative Risk Researcher
Develop next-generation risk models for hedge funds, asset managers, and academic risk research centres.

Top Global Universities

London School of Economics Imperial College London ETH Zürich University of Oxford Bocconi University Carnegie Mellon University University of Edinburgh Columbia University University of Toronto NUS Singapore

Why D'Math University

STEP 01
Mathematical Foundations
We teach the mathematics behind the models — not just the software buttons. Graduates understand why their models work, not just how to run them.
STEP 02
Regulatory Depth
Comprehensive coverage of Basel, Solvency II, and IFRS frameworks gives graduates an immediate competitive advantage in compliance and risk roles.
STEP 03
Professional Alignment
Curriculum designed to support FRM and PRM professional qualifications — study towards industry certification alongside your MSc.
STEP 04
Industry Connections
Partnerships with banks, insurers, and regulators provide placement opportunities and real-world data for dissertation research.
Enrol in MSc Risk Management & Mathematics →

Applications open year-round — join the next cohort today.