AI in Risk Management
Every model a risk function relies on can be wrong. An AI model can be wrong in ways nobody in the room can explain — and the risk function is the one that has to answer for it.
Format
Classroom · Virtual
Upcoming sessions
Pick a session to applyADMISSIONS OPENCity cohorts run once six delegates are nominated. Tell us your preferred month and we’ll confirm dates at least four weeks ahead — please hold travel bookings until then — or offer the nearest confirmed session, live virtual, or in-house. No payment until confirmation.
The programme
AI in Risk Management is a two-day programme for chief risk officers, risk managers, model validation, risk analytics and internal audit teams who need to judge where artificial intelligence and machine learning genuinely strengthen risk measurement — and where they add risk of their own. It covers machine learning in credit risk, early-warning signals, AI model risk management, explainable AI in banking risk and the accountability chain behind an AI-supported decision. Participants compare machine learning with traditional scorecards, challenge model outputs and explanations, and test validation evidence against the questions a supervisor will ask. The Financial Stability Board’s November 2024 report on the financial stability implications of AI names model risk, data quality and governance among the vulnerabilities that stand out; the EU AI Act, a widely used benchmark, treats AI that assesses the creditworthiness of individuals as high-risk. Case studies and peer benchmarks run through both days. Delivered in English and French in Dubai, London and across the Middle East, Africa, Asia and Europe — and live online.
What you will do
Who attends
- Chief risk officers, heads of risk and members of risk and credit committees in banks and financial institutions
- Credit risk, market risk, liquidity risk and operational risk managers
- Model validation, model risk management and risk analytics teams
- Internal auditors who review models, data and automated decisions
- Compliance and governance professionals, and supervisors who oversee the use of AI in risk
- Participants should understand the risk measures their institution uses; no programming or data science background is required
Programme agenda
Structured around one question — would you sign off this model’s decision? — day one where AI strengthens risk measurement, day two challenge, validation and accountability
I.Where AI genuinely strengthens risk measurement
- A map of AI and machine learning use cases across credit, market, liquidity and operational risk
- What machine learning does differently — non-linear patterns, wider data, faster refresh — and what it does not fix
- Prediction, automation and generative AI: three different risk profiles for the risk function
II.Machine learning in credit risk
- Application and behavioural scoring, and early-warning signals on the performing book
- Machine learning against scorecards and logistic regression: where the lift is real and where it is overfitting
- Rare events and low-default portfolios: what scarce defaults do to an AI model
III.Explainable AI: what an explanation has to do
- Global and local explanations, and what each one can and cannot show
- Reasons for a decline, a limit or a watch-list flag: what the customer, the credit committee and the supervisor each need
- When an explanation is not enough and a simpler method should win
IV.AI model risk management
- Inventory and tiering: which AI tools count as models, and how much scrutiny each deserves
- Validating a model you cannot look inside: conceptual soundness, outcomes analysis and benchmarking against challenger approaches
- Vendor and third-party AI: what the institution remains accountable for
V.Data, bias and drift
- Data quality and lineage, read against the Basel Committee’s principles for risk data aggregation and risk reporting
- Bias and fairness testing: proxies, excluded groups and the questions to put to the data
- Monitoring for drift, and the triggers that should send a model back for review
VI.Generative AI inside the risk function
- Summarising credit files, searching policies and preparing risk reports: where it saves time
- Errors, fabricated content and confidentiality: the controls a risk function should insist on
- Where generative AI may assist and where it must never decide
VII.Accountability, governance and the supervisor’s view
- Who owns an AI-supported risk decision across the three lines, and what internal audit should test
- The FSB’s November 2024 report: third-party concentration, market correlations, cyber risk, and model risk, data quality and governance
- The EU AI Act as a benchmark: creditworthiness assessment of individuals as a high-risk use, and what that means for evidence and oversight
VIII.Integrated case study
- A machine learning credit-scoring proposal reaches the risk committee with a vendor model and a validation report
- Challenging the evidence: performance, explanations, bias tests, monitoring plan and accountability
- Arguing approve, approve with conditions or reject — before a mock risk committee
Frequently asked
What is AI in risk management?
It is the use of artificial intelligence and machine learning to measure, monitor and manage risk — for example credit scoring, early-warning signals, anomaly detection and the summarising of risk information. In a bank, it also means managing the risks the AI itself brings: models that are hard to explain, data that carries bias, performance that drifts, and decisions whose accountability must stay with named people.
Why is AI model risk a priority now?
Standard-setters and legislators are looking closely. The Financial Stability Board’s November 2024 report on the financial stability implications of AI lists model risk, data quality and governance among the AI-related vulnerabilities that stand out, alongside third-party dependencies, market correlations and cyber risk. The EU AI Act, which many institutions use as a benchmark, treats AI used to assess the creditworthiness of individuals as high-risk; according to the European Commission, the obligations for such high-risk uses apply from 2 December 2027.
How is it different from the model risk management and AI governance programmes?
Model Risk Management: Governance & Validation Masterclass covers the model risk discipline for all models. AI Governance & Responsible AI for Financial Institutions covers enterprise-wide AI governance and regulation. This programme sits inside the risk function: where AI improves risk measurement, how risk teams challenge AI outputs, and how accountability for AI-supported risk decisions is traced.
What level is the programme, and do I need to code?
It is for risk professionals who already understand the risk measures their institution uses. No programming or data science background is needed: the focus is on judgement — what to ask of an AI model, its data and its validation — rather than on writing code.
Can it run live virtual or in-house?
Yes. Public cohorts run in the classroom and live virtual. For one institution, the programme can run in-house as a custom programme, scoped on request, in English or French.
Is there a group rate, and do delegates receive a certificate?
Institutions sending several delegates, in one cohort or across the year, receive group terms; fees are provided on enquiry. Each participant receives a BIZENIUS Banking Academy certificate of completion. The programme is practice-led rather than exam-led and is not preparation for a third-party qualification.
Who teaches this
Practitioners, not presenters.
Led by practitioners who have built and run technology, data and AI capability inside institutions rather than presented it to them: heads of digital and product innovation in banks, data and AI leaders who have taken models into production under governance, and technology risk specialists who advise boards on what the estate can and cannot yet do.
What the bench brings
- AI and machine learning applied to financial services
- Data science and big-data practice
- AI adoption with owners, controls and returns
- Digital banking, paytech and wealthtech models
- Enterprise risk framework design and risk culture
- Risk appetite, limits and risk and control self-assessment
Where they have practised
Current and former practitioners — people who hold the seat today alongside those who have held it.
Sectors: Banking & financial services · Technology & fintech · Oil & gas · Insurance
Regions: Africa · the Middle East · Europe · Asia · the Americas
How they teach
- Hands-on labs and worked data sets
- Case studies of real implementations
- Group design of business cases and journeys
- Demonstrations judged by adoption and control
- Knowledge checks and a personal action plan
Cohorts are kept small so every exercise is worked on the participants’ own situations — in person or live virtual.
The faculty profile for your cohort is sent with the full agenda and the next dates when you enquire.Request brochure →
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In their words
Knowledge transfer, emphasised throughout
“We worked with BIZENIUS for our Fresh Graduates Programme — they are simply amazing. Knowledge transfer and practical learning were emphasised throughout.”
Kuwait Investment Authority
From the Mandate Record
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The most disciplined credit book in the market had to become its most accessible. The data showed exactly how.
Open the dossier →
The Capability Arc™
Fix it · Advisory
AI Advisory
When the institution wants the work done with it — an inventory of the AI already in use, a validation standard the second line can apply, and a clear record of who approved what.
Automate it · Smart IT
AI Build & Deployment
When a scoring or early-warning tool has to go live inside a regulated institution’s controls — monitored, explainable and auditable.
Learning is one point on the Capability Arc. Many institutions pair this programme with the advisory engagement — and automate what the framework demands.
Teams from these institutions train with BIZENIUS
Related programmes
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View programmeBackground reading on this subject
Written by the practitioners who lead the programme — read before you enquire.
How to validate an AI model you cannot look inside
When the mechanism is unreadable or the vendor will not show it, validation shifts from inspecting the model to interrogating its behaviour. The tests that work, what to demand at procurement, and when the honest answer is a restriction rather than an approval.
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Not an ethics statement and not a policy document — the decision rights, risk tiering, validation standards and evidence that let an institution answer for what its models did. What a working framework contains and what a decorative one looks like.
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Bank failure is uncommon, and that single fact undoes most of what makes machine learning attractive elsewhere. Why headline accuracy flatters a useless model, what class imbalance really costs, and what a supervisor should require before relying on an output.
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One request — the full agenda, the faculty and the next cohort dates, sent personally by the admissions team.