Cambridge
Academy
In London

Introduction

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

Objectives

- 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.

Audience

- Risk managers

- Compliance officers

- Bankers

- Financial regulators

5-Day Outline

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