This professional course explores the application of Artificial Intelligence (AI) and Machine Learning (ML) in transforming financial services. From credit scoring and fraud detection to algorithmic trading and customer personalization, the training course offers a practical foundation in how AI is reshaping finance. Participants will examine key use cases, build awareness of AI-driven decision-making, and learn to collaborate effectively with technical teams in AI projects.
Hands-on demos using AI models for finance
Use case simulations (fraud, underwriting, chatbots, etc.)
Model explanation workshops and algorithm walkthroughs (non-coding)
Group strategy design based on real data challenges
Understand AI and ML concepts in financial contexts
Identify major AI use cases across banking, insurance, and investment
Evaluate the benefits and limitations of AI in decision-making
Explore supervised and unsupervised learning methods
Apply AI to risk management, fraud detection, and credit analytics
Collaborate effectively on AI model lifecycle (build, test, monitor)
Address ethical, explainability, and regulatory concerns in AI finance
Session 1: Introduction to AI and Machine Learning
Definitions, types of AI (narrow vs general)
Supervised vs unsupervised learning explained
Examples from finance: underwriting, compliance, service automation
Session 2: Key ML Algorithms in Finance (No-Code Overview)
Decision trees, logistic regression, clustering
Neural networks and natural language processing (NLP)
Matching algorithms with problems: when and where to use what
Session 3: Data as the Fuel for AI
Quality, structure, and labeling of financial datasets
Bias, noise, and ethics in financial data
Governance and data access policies
Session 1: Fraud Detection and Transaction Monitoring
Anomaly detection in payments
Real-time fraud alerts with ML models
Behavioral biometrics and pattern recognition
Session 2: Credit Scoring and Underwriting Automation
Traditional vs AI-powered credit models
Alternative data for thin-file customers
Risks and regulator views on black-box models
Session 3: Chatbots and Customer Service Automation
AI-driven conversational finance (chatbots, voice assistants)
Integration with CRM and service flows
Case study: virtual agents in banks and insurance firms
Session 1: AI Model Governance and Explainability
Model testing, monitoring, and revalidation
Regulatory expectations for transparency
Explainable AI (XAI) and model accountability
Session 2: Ethics, Bias, and Compliance in AI
Sources of bias and ways to detect/mitigate it
Fairness in lending, investment, and HR-related AI
Emerging global AI standards (EU AI Act, FATF guidance)
Session 3: Building an AI Roadmap in Financial Institutions
AI transformation stages: pilot, scale, mature
Team skills, tools, and talent needs
Final group exercise: map out an AI project in your domain
We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.
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