Credit Risk and AI Applications in Financial Risk Training Course

Effective management of financial risk is a critical skill in today’s fast-changing business and investment environment. This course focuses on credit risk fundamentals—such as underwriting, collateral evaluation, credit scoring, probability of default, Basel III standards, and stress testing—while integrating cutting-edge AI applications and algorithmic risk models.

Participants will learn how artificial intelligence, machine learning, and real-time analytics are transforming financial risk management practices. The program covers how AI tools are used for fraud detection, scenario modeling, market volatility assessment, and stress testing. This unique combination of traditional credit risk principles with next-generation algorithmic risk management prepares professionals to handle complex challenges in the modern financial landscape.

Objective

1. To understand the core principles of credit risk management (underwriting, collateral, credit scoring, probability of default).
2. To analyze Basel III standards and their implications on lending and risk capital.
3. To apply stress testing and scenario modeling in dynamic and volatile markets.
4. To explore AI applications in financial risk management, including algorithmic trading risk and fraud detection.
5. To develop skills in real-time analytics for managing credit and market risks.
6. To bridge traditional risk management with AI-driven algorithmic solutions.

Target Audience

This program is designed for:

1. Risk management professionals in banking, insurance, and investment sectors.
2. Financial analysts and portfolio managers.
3. Credit officers and underwriters.
4. Compliance and regulatory specialists.
5. Data scientists and AI professionals entering the finance domain.
6. Entrepreneurs and executives managing financial risk in startups and corporations.

Course Modules

Module 1: Fundamentals of Credit Risk Management

• Credit risk basics: underwriting, collateral, credit scoring.
• Probability of default (PD) and loss given default (LGD).
• Contextual keywords: market volatility, stress testing.

Module 2: Basel III and Regulatory Standards

• Capital adequacy and leverage ratios.
• Basel III’s impact on lending and liquidity.
• Regulatory requirements in risk management frameworks.

Module 3: Stress Testing and Scenario Modeling

• Designing stress test models for banking and investments.
• Scenario modeling in periods of market volatility.
• Integration of stress testing into strategic decision-making.

Module 4: AI Applications in Financial Risk

• AI models for credit scoring and fraud detection.
• Algorithmic risk management: automation of underwriting and credit monitoring.
• Use of real-time analytics in portfolio and lending risk assessment.

Module 5: Algorithmic Risk and Market Analytics

• Algorithmic trading and systemic risk.
• Detecting anomalies and financial fraud through AI.
• Predictive analytics for proactive risk identification.

Module 6: Case Studies and Practical Applications

• AI-powered fraud detection in retail banking.
• Stress testing and AI models during financial crises.
• Scenario modeling in entrepreneurial and corporate finance.

Key Features

• Integration of traditional credit risk frameworks with AI and real-time analytics.
• Hands-on exposure to scenario modeling, stress testing, and fraud detection techniques.
• Application to market volatility and algorithmic risk environments.
• Balance between financial theory and applied AI-driven risk management.