AI & Machine Learning in Fintech: From Fundamentals to Real-World Impact

AI is no longer experimental in finance; it has become core operational infrastructure. This training is designed to bridge the gap between theory and real-world application, enabling participants to understand and actively apply Artificial Intelligence (AI) and Machine Learning (ML) in modern financial services. 

The programme is structured as a progressive learning journey across three interconnected sessions. It begins with foundational concepts, introducing the AI/ML landscape in financial services, key financial data types, feature engineering techniques, and core machine learning algorithms with finance-appropriate evaluation metrics. It then advances into real-world applications, covering credit risk scoring and underwriting, real-time fraud detection, robo-advisory systems, natural language processing (NLP) for financial text, deep learning architectures, and time-series forecasting for trading. The final part focuses on critical aspects of deployment and governance, including explainability techniques (such as SHAP, LIME, and counterfactual methods), algorithmic bias and fairness, and the global regulatory landscape, including frameworks such as the EU AI Act, GDPR, SR 11-7, and MAS FEAT. It also introduces MLOps practices and the full production lifecycle of machine learning systems. 

Throughout the training, participants will gain a clear understanding of how AI and ML are transforming financial services while actively building solutions across key domains such as credit scoring, fraud detection, customer intelligence, and financial text analysis. Each session combines lecture, live demonstrations, and hands-on workshops to ensure both conceptual understanding and practical capability. 

By the end of the training, participants will have designed and implemented complete machine learning workflows and will have the confidence to evaluate, build, and lead AI-driven initiatives in financial environments. 

The training programme covers the following :

• Module 1: AI/ML Landscape in Financial Services
• Module 2: Financial Data and Feature Engineering
• Module 3: Core ML Algorithms and Model Evaluation
• Module 4: Credit Risk, Fraud Detection and Customer Intelligence
• Module 5: Deep Learning, NLP and Time Series Forecasting
• Module 6: Algorithmic Trading and Quantitative Strategies
• Module 7: Explainability and Ethical AI
• Module 8: Regulatory Landscape and Model Risk Governance
• Module 9: MLOps, Production Deployment and Monitoring
• Capstone: Build, Present and Deploy a Fintech ML Solution 

By the end of this training, participants will: 

• Understand how AI is applied across modern financial services  
• Build and evaluate machine learning models for real fintech problems  
• Gain practical experience with financial datasets and feature engineering  
• Interpret and explain model decisions using industry-standard techniques  
• Understand key regulatory and ethical constraints in AI systems  
• Be able to translate business problems into AI-driven solutions 

• Lecture 
• Hand-on Practices 
• Exercise 
• Small practical projects