AI in Investment Management Training Course: Research, Portfolio Decisions and Model Oversight

Programme Summary

AI in investment management is the subject of this one-week training course for investment, risk and fund leaders, ending in an AI model oversight pack for one investment process. Asset managers, banks and sovereign funds now use language models, alternative data and machine learning signals in research and portfolio work, often faster than their controls can follow. The programme moves from where AI adds value to testing signals, overseeing models and fixing accountability for decisions that use them. Mars Training Centre UK delivers this programme on AI in investment management to mixed-organisation cohorts.

The Business Case

Equity, credit and multi-asset teams are adopting AI tools for research, forecasting and portfolio support. A signal that looks strong in a weak test can lose money in live use, and a model nobody can explain is hard to defend to a committee or a client. Leaders who can question evidence and set limits protect capital and the reputation of the investment process.

• Decision Quality: Improves when signals are tested out of sample and compared with simple benchmarks.
• Model Risk: Falls when validation, limits and owners are fixed before capital is committed.
• Client Confidence: Holds when AI use is disclosed and its limits are explained plainly.

Principles and Evidence Base

• IOSCO guidance on AI and machine learning: Sets measures on oversight, testing, skills, third-party control and disclosure, applied in Sessions 4 and 5 to the oversight pack.
• IOSCO report on AI in Capital Markets: Describes use cases, model risk, data quality and provider concentration, applied in Sessions 1 and 3 to use case and risk review.
• ISO/IEC 42001: Defines an AI management system, applied in Session 5 to ownership and change control.

Intended Participants

• Chief investment officers and heads of strategy who decide where AI belongs in the investment process.
• Portfolio and research leaders who must judge the evidence behind AI-assisted views.
• Risk and compliance leaders who set limits and validate models.
• Fund governance members who approve AI use and answer for it.

Typical settings include asset management, banking, sovereign wealth and insurance investment teams.

Learning Outcomes

1. Participants will be able to evaluate where AI adds value in research, signals and portfolio construction.
2. Participants will be able to diagnose data leakage, overfitting and bias in a backtest.
3. Participants will be able to construct validation tests and risk limits for a model.
4. Participants will be able to prioritise AI use cases by value, evidence and risk.
5. Participants will be able to design disclosure and escalation rules for AI-assisted decisions.
6. Participants will be able to justify an AI model oversight pack to an investment committee.

Programme Structure

Day 1: AI in Investment Management Foundations

Where does AI add value to the investment process, and where does it not?

• Investment Process Map
• AI Use Case Catalogue
• Alternative Data Source Review
• Language Model Research Workflow
• Value and Evidence Scoring Grid

Day 2: Reading Signals and Backtests

Is a signal evidence of skill or a product of the test?

• Backtesting Design Sheet
• Data Leakage Checklist
• Overfitting Diagnostic
• Benchmark Comparison Table
• Bias Review Across Market Regimes

Day 3: Portfolio Use and Risk Limits

How may a model influence positions without exceeding agreed risk?

• Portfolio Construction Support Map
• Position and Turnover Limit Sheet
• Stress Scenario Set
• Crowding and Herding Review
• Explainability Briefing Template

Day 4: Model Validation and Third-Party Control

Who checks the model, the data and the provider?

• Model Validation Plan
• Independent Challenge Protocol
• Vendor Dependence Register
• Model Monitoring Dashboard
• Client Disclosure Statement

Day 5: AI Model Oversight Pack for One Investment Process

Do the earlier tools join into a pack a committee can approve?

• Applying the Session 1 Value and Evidence Scoring Grid
• Testing the Session 2 Data Leakage Checklist
• Checking the Session 3 Position and Turnover Limit Sheet
• Rehearsing the Investment Committee Challenge
• Completing the AI Model Oversight Pack

Methodology

The programme uses case analysis built on prepared material for two investment processes, one an equity research team using language models and one a multi-asset fund using machine learning signals. Participants read backtest reports, challenge each other's conclusions and defend their oversight choices before peers acting as an investment committee.

• Suggested activity: Finding the flaw in a backtest that reports an unusually smooth return.
• Suggested activity: Setting a position limit for a model and justifying it under a stress scenario.
• Suggested activity: Drafting a plain statement for clients on how AI is used in the process.

Capstone Output

Each participant completes an AI model oversight pack for one investment process. It holds a use case score, a validation plan, risk limits, a vendor register and a disclosure statement. At work it is used to brief an investment committee, to set conditions for model approval and to guide periodic review of the model.

Frequently Asked Questions

Is a training course on AI in investment management suitable for leaders who do not build models?

Yes. AI in investment management is taught through prepared cases and templates. The programme assumes responsibility for investment, risk or governance decisions, but no coding or advanced statistics.

Which standards and guidance does AI in investment management rest on?

AI in investment management rests on IOSCO guidance on AI and machine learning, the IOSCO report on AI in capital markets and ISO/IEC 42001 for AI management.

How is the pack from AI in investment management used at work?

You use the AI model oversight pack to brief an investment committee, set conditions for approving a model and guide its periodic review.

How does AI in investment management differ from a programme on quantitative finance?

AI in investment management concentrates on oversight, evidence and accountability for AI-assisted decisions, while a quantitative finance programme concentrates on building pricing and portfolio models.