Financial models now drive pricing, lending, capital allocation, impairment estimates and investment approvals, and the arrival of generative AI has made it far quicker to build them. That speed brings new risks: formulas that look plausible but contain silent errors, invented assumptions, untraceable code and over-reliance on tools nobody has validated. Supervisors and auditors increasingly expect firms to treat models, including AI-assisted ones, within a formal model risk management framework.

The AI-Assisted Financial Modelling and Scenario Analysis Under Model Risk Governance Training Course provides both halves of the answer. The first half builds capability: modelling structure and best practice, three-statement and valuation models, forecasting, credit and liquidity applications, and the use of AI assistants and Python to accelerate drafting, auditing and analysis. The second half builds control: scenario design, stress testing, validation techniques, documentation standards, inventory and tiering, change management and reporting of model risk to senior committees.

Over the ten days, participants work through a continuous case from blank workbook to validated, documented model pack. They learn where AI helps, where it must be challenged, and how to record its use so that reviewers can reproduce results. The course closes with an integrated project presented to a mock model risk committee.

By the end of the programme, participants will be able to:

• Structure financial models using recognised best practice for inputs, calculations and outputs.
• Build integrated three-statement, valuation, credit and liquidity models.
• Use AI assistants, Python and pandas to draft formulas, code and forecasts while verifying every result.
• Design sensitivity, scenario and stress analyses, and run Monte Carlo simulations.
• Validate models through independent review, benchmarking, back-testing and error checks.
• Operate model inventory, tiering and change control, and document AI use and data lineage in an auditable form.
• Report model risk and scenario results clearly to management and committees.

The programme suits finance and risk professionals who build, use, review or oversee models, including:

• Financial analysts and FP&A managers
• Credit risk, market risk and liquidity risk officers
• Treasury and asset-liability management staff
• Investment analysts, fund managers and corporate finance teams
• Actuaries and insurance analysts
• Model validators and independent review teams
• Internal auditors and compliance officers reviewing models
• Chief financial officers and heads of finance responsible for model governance
• Regulators and central bank supervisors

Participants finish the ten days with:

• A fully structured, integrated financial model built from a blank workbook.
• A scenario and stress testing pack with documented assumptions.
• A Python notebook for forecasting and Monte Carlo simulation.
• A validation checklist and test log applicable to their own models.
• A model inventory and tiering template with change control steps.
• An AI use protocol covering prompts, review and record keeping.
• A model risk report format suited to committees and auditors.

The programme is a laboratory for building and testing models under realistic controls. Methods include:

• Instructor-led demonstrations followed by extended hands-on modelling labs
• A continuous case study that develops from blank workbook to validated pack
• Side-by-side exercises comparing AI-generated and manually checked output
• Independent validation drills in which participants test each other's models
• A final team project reviewed by a mock model risk committee

Participants who complete the ten days of sessions and the practical labs receive a Certificate of Completion, CPD-accredited and issued by Vision Reach Global Consultancy.