Artificial Intelligence in the Banking Sector - Benefits & Pitfalls

Introduction

If you or your clients are involved in any matters where AI is relevant, whether nationally or internationally governed by English law or beyond, you need not only be aware of past and recent impact, developments and trends of AI in the Banking Sector but also all the latest known developments, as well as the most likely potential future trends. All so that you can best advise your client and best develop and protect your practice.

This four-hour interactive virtual seminar will not only cover the basic mechanical AI functions in the sector, discuss their pros and cons and highlight some of the current key industry awareness issues, it will also highlight some of the new and current insights and trends that can help enable all those working in the industry to provide the best possible professional service to their clients and assist their colleagues.

What you will learn

This live and interactive course will cover the following:

• Basic tenants of the UK< US and EU Legal and Frameworks and potential developments in the AI Governance
• Use of AI in banking benefits:
• Speed: automates routine tasks and speeds up loan approvals
• Fraud detection: spots strange account activity and stops theft fast
• Personalisation: offers custom money tips and product ideas for each user
• Availability: powers 24/7 customer service chat bots
• Use of AI in banking pitfalls:
• Privacy risks: needs huge amounts of private user data, which can leak
• Bias: can make unfair choices if the training data has human flaws
• Cost: takes a lot of money and time to build and set up
• Errors: can make big mistakes if a model fails or hallucinates
• Key elements of AI fraud detection methods, including:
• Traditional predictive machine learning (ML)
• Encoder large language models (LLMs)
• Ensemble AI workflows: predictive MLS with LLMs
• Summary of the types of banking fraud AI can address, including:
• Identify theft and account takeover
• Phishing and social engineering scams
• Credit card and payment fraud
• Document forgery and synthetic identity creation
• Benefits of AI-based fraud detection in banking:
• Real-time detection
• Pattern recognition
• Reduced false positives
• Limitations of AI-based fraud detection in banking:
• Model weakness
• Integration complexity
• Regulatory scrutiny
• Implementation of AI fraud detection in banking:
• Importance of data prep
• Use of multilayered models
• Integrate into workflows
• Include human oversight
• Apply governance and testing
• How banks identify and address AI bias
• Case study of some real-world examples of AI fraud detection, the government and public sector as well as banking and finance and e-commerce, marketplaces and insurance
• US Department of Treasury - USD4B fraud
• HSBC and Google - AI-Based Dynamic Risk Assessment system
• Mastercard - use of generative AI and predictive algorithms
• Amazon
• Shopify
• GEICO and CCC Smart Red Flag Detection
• Update on how fraudsters and using generative AI to counter fraud protection systems
• Relevant AI insurance issues:
• Key mitigation tools and strategies
• 5 key AI fraud trends financial institutions need to understand in 2027 and beyond:
• The AI treat multiplier
• The Onboarding dilemma
• Authentication under siege
• The ‘all-green’ problem
• Fraud as an industrial operation
• Who pays when it all goes wrong and why

Recording of live sessions: Soon after the Learn Live session has taken place you will be able to go back and access the recording - should you wish to revisit the material discussed.