Machine Learning in Banking and Finance Masterclass
What machine learning can and cannot do for banking and risk — supervised and unsupervised methods, alternative data and explainable AI, worked in practice.
Format
Classroom · Virtual
Upcoming sessions
Pick a session to applyADMISSIONS OPENThe programme
Machine learning has moved deep into banking, risk management and modelling — but adoption without interrogation swaps one model risk for another. This intensive course examines what the advances actually offer financial institutions, and where the limitations sit. Working from practical examples and use cases, participants build command of supervised and unsupervised learning, sequential and deep learning, alternative data, and the metrics of explainability and interpretability for AI models. Behavioural and quantitative finance applications are developed — including investment strategies with ML and graphical ML — and a practical case study lets the cohort apply the theory to real-world examples, including conducting AI responsibly without reinforcing existing bias.
What you will do
Who attends
- Risk management and quantitative analytics teams
- Data science and machine learning practitioners
- Portfolio management and financial engineering professionals
- IT teams supporting model platforms
Programme agenda
Built for the decisions no textbook prepares you for
I.ML foundations for finance
- Supervised and unsupervised learning
- Recent advances in sequential and deep learning
- Opportunities and limitations for financial institutions
II.Data and explainability
- Alternative data: promise and pitfalls
- Metrics of explainability and interpretability
- Implications for finance and regulatory compliance
III.Applications
- Behavioural and quantitative finance use cases
- Investment strategies with ML and graphical ML
- Automation and the efficiency of the finance function
IV.Responsible adoption
- Interrogating models and partnering with data science
- Avoiding reinforced bias
- Threats, threat actors and vulnerabilities
- Practical case study of ML use cases
Frequently asked
Do I need a data science background to attend?
No — the course is aimed at risk management and quantitative analytics teams, portfolio management and financial engineering professionals, and IT teams supporting model platforms, alongside data science practitioners. It works from practical examples and use cases, and teaches participants to interrogate models and partner with data scientists rather than build everything themselves.
Which machine learning methods does the course cover?
Supervised and unsupervised learning, recent advances in sequential and deep learning, alternative data, and investment strategies with ML and graphical ML. Explainability and interpretability metrics — and their implications for regulatory compliance — run through the programme, with a practical case study applying the theory to real-world examples.
How does the programme address responsible AI?
Participants work through conducting AI responsibly without reinforcing existing bias, and managing potential threats, threat actors and vulnerabilities. BIZENIUS delivers the course in English and French, with in-house editions available; sessions run on a rolling calendar and fees are provided on enquiry.
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In their words
Knowledge transfer, emphasised throughout
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The Capability Arc™
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Advisory & Consultancy
A senior bench across risk, treasury and regulation.
Learning is one point on the Capability Arc. Many institutions pair this programme with the advisory engagement — and automate what the framework demands.
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Take the brochure with you.
One request — the full agenda, the faculty and the next cohort dates, sent personally by the admissions team.