Artificial Intelligence (AI) in Banking: Revolutionizing Financial Services
02 -06 March 2026
Sandton, Johannesburg South Africa
Cost per Delegate
R19,999.00
Introduction
The banking and financial services sector is undergoing a profound transformation driven by Artificial Intelligence (AI). From intelligent chatbots and automated credit scoring to fraud detection, risk management, and personalized customer experiences, AI is redefining how banks operate, compete, and deliver value.
This course provides a comprehensive and practical understanding of how AI technologies are being applied across modern banking operations. Participants will explore key AI concepts, tools, and real-world banking use cases, with a focus on improving efficiency, strengthening risk controls, enhancing customer service, and ensuring regulatory compliance.
Designed for both technical and non-technical professionals, the course demystifies AI and explains its strategic, operational, and ethical implications within the banking environment. By the end of the program, participants will have a solid foundation to understand, evaluate, and support AI-driven initiatives in financial institutions.
Objectives:
By the end of this course, participants will be able to:
1. Understand AI Fundamentals
o Explain core AI concepts such as machine learning, deep learning, natural language processing (NLP), and robotic process automation (RPA).
o Distinguish between traditional automation and AI-driven systems.
2. Apply AI in Banking Operations
o Understand how AI is used in customer service (chatbots, virtual assistants).
o Explore AI applications in credit scoring, loan approvals, and customer onboarding (KYC).
3. Enhance Risk Management and Fraud Detection
o Analyze how AI detects fraud, money laundering, and suspicious transactions.
o Understand predictive analytics for credit risk, market risk, and operational risk.
4. Improve Customer Experience
o Learn how AI enables personalized banking products and services.
o Understand customer behavior analysis and data-driven decision-making.
5. Support Compliance and Regulatory Requirements
o Understand AI’s role in regulatory reporting, compliance monitoring, and audit support.
o Explore ethical considerations, data privacy, bias, and explainable AI in banking.
6. Develop Strategic Awareness
o Assess the benefits, limitations, and risks of AI adoption in banking.
o Understand future trends and how AI will shape digital banking and fintech collaboration.
Who Should Attend?
• Banking and Financial Services Professionals
• Risk, Compliance, and Audit Officers
• IT, Digital Transformation, and Innovation Teams
• Finance, Credit, and Lending Officers
• Quality Assurance and Process Improvement Officers
• Senior Management and Decision Makers
• Regulators, Policymakers, and Financial Consultants
course Outline
1. Introduction to AI in Banking
• Overview of AI applications in banking
• Benefits of AI adoption: improved accuracy, efficiency, and customer satisfaction
2. AI-Powered Customer Service and Engagement
• Chatbots and virtual assistants for customer support
• AI-driven personalized marketing and product recommendations
• Sentiment analysis and social media monitoring
3. AI-Driven Risk Management and Fraud Detection
• Machine learning for credit risk assessment and approval
• AI-powered fraud detection and prevention
• Anomaly detection and continuous monitoring
4. AI-Assisted Lending and Credit Decisioning
• Automated loan processing and approval
• AI-driven credit scoring and risk assessment
• Predictive analytics for loan default and recovery
5. AI and Anti-Money Laundering (AML) and Know Your Customer (KYC)
• AI-powered AML and KYC compliance
• Automated customer due diligence and risk assessment
• Machine learning for suspicious activity detection
6. AI-Driven Trading and Investment
• AI-powered trading and portfolio management
• Machine learning for predictive analytics and market forecasting
• Automated investment advice and wealth management
7. Implementing AI in Banking - Best Practices
• Change management and organizational readiness
• AI project management and implementation strategies
• Collaboration with external stakeholders (e.g., fintechs, regulators)
8. Regulatory and Ethical Considerations
• Regulatory landscape for AI in banking
• Ethical considerations: bias, transparency, and accountability
• Industry standards and guidelines for AI adoption
9. Case Studies and Group Exercises
• Real-world examples of AI in banking
• Group exercises to apply AI concepts to banking scenarios
10. Conclusion and Next Steps
• Recap of key takeaways
• Action plan for implementing AI in banking
• Ongoing support and resources for continued learning and development.
End of the Workshop
For Training arrangements call us on the detail below
TANZANIA: +255 749 50 26 78
SOUTH AFRICA: +27 694 31 79 73
KENYA: +255 749 50 26 78
DUBAI: +27 694 31 79 73
