Emerging Technologies Artificial Intelligence Machine Learning in Finance and Banking

Module 1: Introduction to Artificial Intelligence (AI) and Machine Learning - Fundamental Concepts and Applications

By the end of the module, the participants would be able to learn and understand:

• What is the definition and concept of Artificial Intelligence, Machine Learning and Deep Learning in Finance
• Differentiate between Artificial Intelligence, Machine Learning and Deep Learning in the context of financial services
• Indentify Key Application areas of Ai / ML in banking and finance
• Understand the role of Data Science in financial decision making

Module 2 - Technical Aspects and Application in Finance

• Master fundamental ML algorithms commonly used in finance (regression, classification, clustering)
• Understand Neural Networks and Deep Learning applications in financial modelling
• Develop proficiency in Financial data Pre-processing and feature engineering
• Learn time series analysis and forecasting techniques
• Learn Bias Testing Methodologies – Statical Testing Framework and Model Validation Process
• Explainable AI (XAI) Implementations

Module 3: Risk Management and Compliance – Case Studies

By the end of the module, the participants would be able to learn and understand:

• Understand AI-driven Credit Risk Assessment models
• Learn Fraud detection and prevention using machine learning(ML)
• Develop and Implement Market Risk modelling using advanced ML techniques
• Implement anti-money laundering (AML) Detection systems

Module 4: Regulatory Compliance in Banking and Financial Services – Case Studies

• Understand the Regulatory requirements for AI/ML systems in finance
• Learn model validation and governance frameworks
• Master documentation requirements for ML models
• Understand Bias detection and mitigation strategies

Module 5: Trading and Investment Management – Case Studies

By the end of the module, the participants would be able to learn and understand:

• Develop ML-based trading strategies for Investments
• Understand high-frequency trading systems
• Master portfolio optimization using AI techniques
• Learn sentiment analysis for market movement prediction
• Implement Robo-advisory systems
• Understand Asset allocation algorithms
• Master risk-adjusted return optimization
• Learn automated portfolio rebalancing techniques

 Module 6: Customer Experience and Operational Efficiency – Case Studies

• Implement chatbot and virtual assistant technologies
• Understand customer segmentation using ML
• Master personalization algorithms
• Learn recommendation systems for financial products
• Understand process automation using AI
• Master document processing using computer vision
• Learn predictive maintenance for banking systems
• Implement intelligent workflow optimization

Module 7: Ethical Considerations and Compliance – Use of AI and ML in Banking

By the end of the module, the participants would be able to learn and understand:

• The role of Ethics OF AI in Business and Finance
• What are the Ethical considerations and elements that need to addressed while implementing AI in Finance
  • Transparency
  • Accountability
  • Privacy
  • Bias
  • Security and Systemic Risk
• What is AI Compliance and why is compliance important?
• What are the international rule and regulations related to AI Compliance in Finance
  • The EU Artificial Intelligence Act
    • How do you ensure adherence to regulations and compliance of AI in Finance
• What are the Compliance and Regulatory Considerations for AI in Finance
• The Ethics of AI – Balancing Innovation and Responsibility
• Data Privacy and Protection
• Fairness and Non Discrimination
• Transparency and Explainability
• Accountability and Governance

Module 8: Real World Ethical Challenges and Resolutions – AI and ML in Finance-Problems and Solutions

• Credit Scoring Bias
• AI Chatbot Privacy Breach
• Algorithmic Transparency
• Regulatory Compliance

Module 9: Emerging Trends and Future Directions in AI and ML in Finance

• Recognize Emerging fintech trends
• Understand Open Banking implications
• Learn impact of AI on Financial Inclusion
• Master Sustainable Finance applications using AI

Module 10: Emerging Technologies –Disruptions Applications of AI and ML in Finance – Real World Case Studies

• Understand Quantum computing applications in Finance
• Learn Blockchain Integration with AI systems
• Master Edge Computing for financial applications
• Understand Federated learning for privacy-preserving ML
• AI-powered ESG (Environmental, Social, Governance) investing
• Distributed Ledger Technologies (DLT) and AI integration

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

• Understand the foundational concepts of AI, Machine Learning, and Deep Learning in finance.
• Analyse the evolution and impact of AI and ML on financial services, trading, and investment management.
• Master key AI and ML techniques such as Natural Language Processing (NLP), computer vision, reinforcement learning, and time series forecasting in finance.
• Develop proficiency in AI-driven applications like fraud detection, risk assessment, robo-advisory systems, and personalized financial products.
• Explore ethical considerations, regulatory compliance frameworks, and data governance principles for AI in banking.
• Implement AI-based risk management strategies, credit scoring models, and compliance monitoring techniques.
• Gain hands-on experience with algorithmic trading, high-frequency trading, and AI-powered portfolio optimization.
• Understand the role of AI in customer experience enhancement, including chatbots, personalization algorithms, and automated workflows.
• Learn about cutting-edge AI innovations such as quantum computing, blockchain integration, federated learning, and ESG investing.

• Data Scientists and Data Analysts - Master cutting-edge AI and ML techniques by refining your expertise in tools and methodologies that power advanced analytics and predictive modelling
• Risk Management and Compliance Professionals - Acquire the understanding and application of AI and ML technologies in regulatory compliance, fraud detection and prevention with the consideration of ethics and principles of data privacy in AI and ML in Finance
• Software Engineers and professionals - Gain a robust foundation in machine learning algorithms and real-world applications by transitioning AI / ML roles or integrating AI capabilities into existing projects
• Business Analysts and Consultants - Move beyond conventional analysis and harness AI to drive strategic, data-backed decision making, enabling smarter recommendations and improved outcomes for clients and stakeholders
• Product Managers and Product Owners - Expand you toolkit by integrating AI and ML into product development and understand how to design, develop and manage AI-driven solutions that meet user needs and boost performance
• Executives and Leaders - Gain a comprehensive understanding of the strategic implications of AI and ML, enabling you to guide teams, foster innovation and make informed decisions that align with business goals.

This course on Emerging Technologies in Artificial Intelligence and Machine Learning explores cutting-edge AI and machine learning applications transforming the financial industry, equipping professionals with practical knowledge of implementation strategies and emerging trends.

The first half of the training consists of the key topics including the foundations and evolution of AI and ML in Finance, data and technical infrastructure requirements for financial AI and ML applications, with ethical and regulatory considerations and compliance frameworks and the emerging trends and technologies of AI and ML. The core technologies of AI and ML explained include Natural Language Processing (NLP) for financial text analysis and sentiment extraction, computer vision applications in fraud detection and security, reinforcement learning for portfolio optimization and trading and time series forecasting models for market prediction. The applications of the core technologies like algorithmic trading systems and execution algorithms, credit scoring and risk assessment models, customer segmentation and personalized financial products, fraud detection and anti-money laundering systems and Robo-advisors and automated wealth management are illustrated and explained with real life case studies and examples.

The second half of the training consists of the application of AI and ML technologies in the areas of trading and investment, risk management, customer facing applications, compliance and fraud detection, operational efficiency and investment management. The ethical and regulatory compliance considerations of the AI and ML applications including regulatory compliance, fairness and bias, transparency and data privacy and security, systemic risk considerations, human oversight requirements and emerging standards of governance are enunciated and illustrated with real case studies and examples.