AI in Financial Decision-Making Simulation

In this simulation, participants explore how artificial intelligence transforms investment, risk, and corporate finance decisions - balancing data-driven insights with human judgment, ethics, and organizational strategy under uncertainty.

AI in Financial Decision-Making Simulation Overview

Participants step into the role of finance professionals using AI-powered tools to support critical decisions. Each round introduces new opportunities and risks: algorithmic trading strategies, credit scoring, risk modeling, or corporate investment planning.

They must balance AI-driven recommendations with ethical, regulatory, and strategic considerations. Scenarios highlight both the potential and limitations of AI - bias in algorithms, data quality issues, transparency concerns, and overreliance on automation.

This simulation is designed for finance, data analytics, and strategy courses, as well as executive and corporate training. It brings abstract AI concepts to life through immersive financial decision-making scenarios.

AI in Financial Decision-Making Simulation Concepts

Participants work through realistic scenarios, which can be customized to emphasize or exclude specific topics depending on the learning goals. This modular structure allows the simulation to be tailored to any type of session. Key concepts include:

AI applications in trading, credit, and corporate finance
Data-driven decision-making and predictive analytics
Algorithmic bias and ethical considerations
Regulatory challenges in AI adoption
Human-AI collaboration in financial contexts
Risk management with AI-based models
Transparency, interpretability, and trust in algorithms
Cost-benefit analysis of AI adoption
AI’s role in financial innovation and disruption
Limits of automation and importance of human oversight

Gameflow

What Participants Do

In the simulation, participants act as financial decision-makers leveraging AI. They:

Review AI model outputs for trading, lending, or investment decisions
Balance human judgment with machine recommendations
Manage risks from data bias, errors, or system failures
Respond to regulatory scrutiny or stakeholder concerns
Adapt strategies to external shocks such as volatility or market disruptions
Present AI-driven strategies to boards, regulators, or clients

Learning Objectives

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

Apply AI concepts in financial decision-making
Recognize the strengths and weaknesses of AI models
Balance automation with human oversight in high-stakes contexts
Evaluate data quality and model transparency issues
Manage ethical and regulatory challenges of AI in finance
Respond to external shocks with agility
Communicate AI-based strategies persuasively to stakeholders
Collaborate across technical and business functions effectively
Anticipate long-term impacts of AI adoption in financial services
Build confidence in integrating AI into strategic finance decisions

The simulation’s flexible structure ensures that these objectives can be calibrated to match the depth, duration, and focus areas of each program, whether in higher education or corporate learning.

How the AI in Financial Decision-Making Simulation Works

The simulation can be delivered individually or in teams, in classrooms, workshops, or executive education. Each cycle mirrors a decision-making round enhanced by AI inputs.

1. Receive a Scenario or Brief: Participants are introduced to a financial challenge where AI tools provide data-driven insights.

2. Analyse the Situation: They review model outputs, financial data, and contextual factors to weigh trade-offs.

3. Make Strategic Decisions: Participants decide whether to follow AI recommendations, override them, or adjust strategies.

4. Collaborate Across Roles: Teams represent different stakeholders - finance leaders, data scientists, regulators - debating risks and opportunities.

5. Communicate Outcomes: Participants deliver strategy memos, board updates, or client presentations explaining their choices.

6. Review and Reflect: Feedback highlights outcomes, AI effectiveness, and ethical implications. Participants refine their judgment in later rounds.

Frequently Asked Questions

Do participants need technical AI skills? No. The simulation focuses on strategy and decision-making, not coding.
What types of AI are included? Applications like trading algorithms, credit scoring, and predictive models.
Can it be tailored for industries? Yes. Scenarios can reflect banking, investment, or corporate finance contexts.
Is bias in AI models included? Yes. Scenarios highlight fairness, ethics, and transparency issues.
Can teams take different roles? Yes. Teams may represent executives, data scientists, or regulators.
How long does the simulation run? It can be a 4-hour session or extended into a multi-day workshop.
Is it suitable for executives? Absolutely. It’s designed for both students and professionals in finance.
Does it cover regulation? Yes. Compliance and oversight are integral to the gameplay.
Can it run online? Yes. The simulation supports online, hybrid, and in-person delivery.
How is performance measured? By financial outcomes, ethical awareness, and communication effectiveness.

Assessment

Assessment can be tailored to focus on technical, strategic, or ethical decision-making. Participants may be evaluated on:

Quality of decisions balancing AI and human input
Recognition of risks and limitations in AI use
Clarity and persuasiveness of communication
Responsiveness to regulatory and stakeholder challenges
Collaboration across technical and strategic roles

You can also include memo writing and debrief presentations as part of the assessment structure. Additionally, you can also add a built-in peer and self-assessment tool to see how participants rate themselves. This flexibility allows the simulation to be easily integrated by professors as graded courses at universities and by HR at assessment centres at companies.