Cambridge
Academy
In London

Introduction

Financial institutions face strict regulatory environments and growing risks. AI enhances risk detection, compliance monitoring, and regulatory reporting.

Course Objectives

By the end of this course, participants will be able to:

·       Understand AI applications in financial risk management.

·       Apply AI for regulatory compliance and monitoring.

·       Learn how AI detects financial crimes (AML, KYC).

·       Build AI-driven risk frameworks.

Target Audience

This course is designed for:

·       Risk managers

·       Compliance officers

·       Bankers

·       Financial regulators

Course Outlines

Day 1: AI in Financial Risk Management

·       AI in credit risk modeling

·       Operational risk detection with AI

·       Tools for real-time risk dashboards

·       Case study: AI in banking risk management

·       Discussion: AI as risk partner vs. risk itself

Day 2: AI in Regulatory Compliance

·       AI for monitoring financial regulations

·       Automating compliance reporting

·       Tools: Ayasdi, Darktrace, Theta Lake

·       Case study: AI in Basel III & IFRS compliance

·       Exercise: Build a compliance monitoring plan with AI

Day 3: AI in Financial Crime Detection

·       AI for Anti-Money Laundering (AML)

·       AI in Know Your Customer (KYC) checks

·       Detecting insider trading with AI

·       Case study: AI in fraud & AML detection

·       Workshop: Fraud detection simulation

Day 4: Stress Testing & Scenario Analysis

·       AI stress testing of portfolios

·       Predictive stress models for banks

·       Scenario analysis using AI simulations

·       Group activity: Build a risk stress test with AI

·       Peer feedback & review

Day 5: Future of Risk & Compliance in AI Age

·       Ethical and legal risks of AI in finance

·       Balancing regulation with innovation

·       Group project: Build an AI-driven risk framework

·       Final presentations & expert feedback

·       Wrap-up & certification