Credit Risk Modelling

Course Overview

A practical, analytics‑driven course that teaches learners how to assess borrower risk, build predictive credit models, and apply statistical and machine‑learning techniques used in banking and financial services. The course covers credit scoring, probability of default (PD), loss given default (LGD), exposure at default (EAD), model validation, and regulatory expectations.

Target Audience

• Credit risk analysts and financial analysts

• Banking, lending, and fintech professionals

• Data scientists working in risk and compliance

• Students or career switchers entering financial analytics

• Managers responsible for credit decisions and portfolio risk

Course Outcomes

• Understand key credit risk concepts, metrics, and regulatory frameworks

• Build credit scoring models using statistical and machine‑learning techniques

• Estimate PD, LGD, and EAD for risk assessment and capital calculations

• Analyse borrower behaviour, default patterns, and portfolio performance

• Prepare and clean financial datasets for modelling

• Validate, monitor, and interpret model performance

• Use analytics to support lending decisions and risk mitigation

• Communicate model insights clearly to risk, finance, and leadership teams