Build job-relevant skills in AI-powered financial risk, fraud detection, credit analytics, portfolio risk, and liquidity analysis.
Learn how modern financial institutions use data, machine learning, scoring models, governance, and risk metrics to improve decisions and control exposure.
This Specialization develops practical capabilities across three connected areas of financial risk management. You’ll evaluate credit-risk data, credit scoring models, validation, calibration, model governance, deployment controls, and ongoing performance monitoring. You’ll examine AI and machine learning approaches for fraud detection across finance, insurance, trade finance, and SMEs, including anomaly detection, clustering, NLP, explainability, fairness, privacy, and human oversight. You’ll also analyze portfolio risk, diversification, volatility, drawdowns, tail risk, optimization, asset relationships, funding liquidity, cash-flow pressure, market confidence, and stressed conditions.
By the end, you’ll be able to interpret model outputs, assess credit and fraud risks, evaluate portfolio vulnerabilities, analyze liquidity pressure, and support responsible, data-driven financial risk decisions across banking, fintech, insurance, lending, and investment environments.
Applied Learning Project
Through applied projects and real-world financial risk scenarios, learners will evaluate credit scoring models, identify suspicious transactions using AI and machine learning techniques, and assess portfolio volatility, drawdowns, and liquidity pressures. Learners will interpret model performance, recommend fraud prevention strategies, examine governance controls, and communicate actionable risk insights to support responsible financial decision-making.
