AI in Banking and Financial Services

About the Programme

How can artificial intelligence (AI) and machine learning (ML) benefit banks and financial firms – and help their customers?

This interactive workshop is for mid-level and senior teams in banks and financial services firms.

Your people will learn from a world-class faculty of academics and industry practitioners. And you can tailor the learning specifically to your needs.

The programme will help you and your team get a better understanding of:

  • use in different sectors – retail banking, investment and wealth management
  • the ethical implications and considerations of deploying AI solutions in banking and financial services.

Who is the Programme for?

This programme will be of value to senior and mid-level teams in banks and other financial services organisations with responsibility for strategy and customer insight. In particular, the session will benefit c-level executives, directors and heads with responsibilities for:

  • customer channels
  • customer insight
  • product development
  • support functions such as risk and compliance
  • technology.

Studying

You will study a total of four modules.

The Programme

Indicative Content

  • Topic 1: An introduction to Fintech and AI

    Fintech – scope and interfaces to traditional financial services

    What are key characteristics of fintech?

    • What does fintech have in common with concepts such as ‘insuretech’ and ‘legaltech’?
    • Where do fintech trends emerge?
    • What are the global similarities and local differentiators?
    • What is the monetary market share of fintech on all financial services?
    • Where do we see and anticipate strongest growth

    Fintech technology

    • What are the working hypotheses of fintech regarding technology accessibility, processes and legal foundation?
    • Is fintech necessarily mobile?
    • How is fintech related to, and dependent on, ‘big data’ technology?
    • Which of the ‘HiFive’ criteria apply to fintech applications?
    • Where in the value chain is fintech related to AI?
    • Which concepts of AI is fintech exploiting now and what is anticipated for the future?
  • Topic 2: The fundamentals of AI in Fintech

    Why now?

    • Computational power
    • Ubiquitous data availability
    • Modern algorithms

    What is AI?

    • The AI effect
    • Machine learning, expert systems and the ‘internet of things’
    • Hardware implementation
    • Applications

    Machine learning

    • Basic working principles of machine learning
    • Linear versus nonlinear models
    • Shallow versus deep learning
    • Frameworks and implementation

    Limitations

    • Availability of high-quality data
    • Interpretability of machine learning model decisions
    • Correlation versus causation

    Anomaly detection

    • Why is anomaly detection one of the most important techniques in business?
    • Anomaly detection using autoencoders
    • Anomaly detection in financial transactions

    Natural language processing (NLP)

    • Classifying financial transactions with NLP
    • Understanding banking contracts
    • Investment research with NLP

    Recommender engines

    • Amazon’s recommendations also work in fintech
    • The mechanism of recommender engine
  • Topic 3: Fintech and AI in retail banking, wealth management and investment management

    Payment services

    • The individual steps in the electronic payment process
    • How fintech accesses, accelerates and automates these steps
    • The role of aggregation platforms, for example WeChat
    • Technology for payment services

    Robo advisory

    • The robo advisory business case
    • Live robo advisory examples and comparison of selected offer

    Report generation

    • Introduction to NLP for speech generation
    • Live example of financial report generation
    • Applications of report generation

    ‘Know your customer’ (KYC) and ‘anti-money laundering’ (AML)

    • AI techniques for automating KYC and AML processes
    • The client2vec algorithm

    Credit risk management

    • End-to-end example for AI-based credit scoring

    Fraud screening

    • Credit card fraud screening
    • Transaction fraud screening

    Portfolio management

    • How fintech can provide competitive advantages in investing
    • Alternative data
    • Crowd investment
    • Fintech and factoring

     

  • Topic 4: Ethics and AI for financial services applications

    Ethical AI development in the fintech space

    • Singapore’s ‘Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT)
    • Ethics Guidelines for Trustworthy Artificial Intelligence
    • The Algo.Rules

    Interpretability of AI models

    • The difference of explainability and interpretability of AI models
    • Global interpretability
    • Local interpretability
    • Fintech-related examples

    Bias and de-biasing data

    • Intentional and unintentional bias in data
    • How biased data leads to biased AI models
    • Examples of the bias problem
    • De-biasing of data

Register Your
Interest

If you are interested in the programme from either a personal or organisation perspective and would like to receive further information we would be delighted to hear from you.