AI in Banking Training Course: Use Cases, Model Risk and Governance

Programme Summary

AI in banking is the subject of this one-week training course for senior banking executives and heads of function, ending in an AI adoption roadmap for one banking business line. Banks are pressed to use AI and generative AI in service, credit and operations while boards expect accountability for automated decisions. The programme links use case selection, data, model risk, conduct and governance into one sequence, applying each tool to a business line the participant leads. Mars Training Centre UK delivers this programme on AI in banking to mixed-organisation cohorts.

The Business Case

AI in banking touches customer contact, lending, compliance checks and back-office work at once, so a weak choice in one area spreads cost and risk into others. Where AI in banking is led by technology teams alone, banks collect pilots without owners and generative tools used outside policy. Leaders who rank use cases by value and risk, and set controls before scale, fund the right projects.

• Service Handling Time: Falls as suitable customer and staff tasks are supported by generative tools under review.
• Model Risk Exposure: Falls as validation, monitoring and ownership are agreed before deployment.
• Project Portfolio Yield: Improves as use cases are ranked and stopped against clear criteria.

Principles and Evidence Base

• NIST AI Risk Management Framework: Its govern, map, measure and manage functions shape the control design in Sessions 3 and 4.
• ISO/IEC 42001: The standard for AI management systems frames policy, roles and records for the governance model in Session 4.
• Model risk management: Inventory, validation and ongoing monitoring of models guide the oversight work in Session 3.

Intended Participants

• Executives who sponsor AI programmes across retail, corporate or operations units.
• Heads of function who decide where automated decisions and generative tools may be used.
• Risk and compliance leaders who challenge model use and customer outcomes.
• Technology and data leaders who deliver and support banking AI.
• Finance and strategy leaders who fund and track AI benefits.

Typical functions include retail banking, corporate banking, credit, operations and compliance.

Learning Outcomes

1. Participants will be able to evaluate AI and generative AI use cases across banking activities.
2. Participants will be able to prioritise use cases by value, feasibility and risk.
3. Participants will be able to diagnose data, explainability and bias weaknesses in a proposed model.
4. Participants will be able to construct a model risk and control approach for an AI use case.
5. Participants will be able to assess conduct and customer outcome risks from automated decisions.
6. Participants will be able to justify an adoption roadmap to a board committee.

Programme Structure

Day 1: AI in Banking and Use Case Mapping

Where can AI and generative AI change banking work, and where should they not?

• Banking Value Chain Map
• AI and Generative AI Capability Table
• Use Case Long List
• Value and Risk Screening Grid
• Business Line Baseline Sheet

Day 2: Service, Credit and Compliance Applications

How do AI tools support customers, lending decisions and control functions?

• Customer Service Assistant Design
• Credit Assessment Support Flow
• Document Review Workflow
• Compliance Monitoring Scenario
• Staff Productivity Use Case Brief

Day 3: Data, Explainability and Model Risk

How is a banking model shown to be fit for use and kept under review?

• Data Quality and Lineage Check
• Explainability Requirement Sheet
• Bias and Fairness Test Plan
• Model Inventory and Validation Record
• Ongoing Monitoring Dashboard

Day 4: Conduct, Controls and Governance

Who is accountable when an automated decision affects a customer?

• Accountability and Decision Rights Map
• Customer Outcome Risk Register
• Generative Tool Acceptable Use Policy
• Third-Party AI Assessment Checklist
• Escalation and Incident Protocol

Day 5: Applying the Tools to One Business Line

Do the use case, model risk and governance tools support one adoption roadmap?

• Applying the Session 1 Value and Risk Screening Grid
• Using the Session 2 Credit Assessment Support Flow
• Testing the Session 3 Model Inventory and Validation Record
• Checking the Session 4 Accountability and Decision Rights Map
• Completing the Banking AI Adoption Roadmap

Methodology

The programme uses a case analysis method built on anonymised banking scenarios from retail, corporate and operations settings. Participants work in peer groups on a shared business line, challenge each other's rankings and receive written facilitator feedback on each exercise set.

• Suggested activity: Ranking ten banking use cases and defending the order to a mock risk committee.
• Suggested activity: Reviewing a model summary to find missing validation evidence.
• Suggested activity: Drafting a rule for staff use of a generative assistant with customer data.

Capstone Output

Each participant completes an AI adoption roadmap for one banking business line. It holds a ranked use case list, a model risk assessment, an accountability map, a staged delivery plan and a benefits measure sheet. The roadmap is used at work to brief a board committee, agree ownership with risk colleagues and sequence the first funded projects.

Frequently Asked Questions

Is AI in banking training suitable for executives without a data science background?

Yes. AI in banking training assumes senior responsibility in a bank or financial institution and builds technical vocabulary step by step without coding.

Which frameworks does AI in banking rest on in this programme?

AI in banking rests on the NIST AI Risk Management Framework and ISO/IEC 42001 for governance, together with model risk management practice for validation and monitoring.

How is the roadmap from AI in banking used at work?

You use the AI adoption roadmap to brief a board committee, agree ownership with risk colleagues and sequence the first funded projects.

How does AI in banking training differ from fraud analytics training?

AI in banking training centres on use case choice, model risk and governance across banking activities, while fraud analytics training centres on detecting suspicious transactions with data methods.