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📘 Use Case Description: Fraud Detection at Contoso Bank

🔎 What Is This Use Case About?

This use case focuses on helping Contoso Bank's Fraud Investigation Team identify and prioritize customers who are most likely to be victims of actual fraud. The goal is to prevent financial losses and avoid unnecessary disruptions for customers who are falsely flagged.

The bank has already flagged 30 customers through its automated systems, but needs human analysts to: - Narrow it down to the top 5 highest-priority fraud cases. - Justify why each customer was chosen. - Predict how many of these are actual fraud (from 0 to 4).

🪧 Who Is This For?

  • Bank fraud analysts
  • Data scientists working in fraud detection
  • Compliance and risk management teams
  • AI model developers evaluating fraud prioritization models

📈 What Data Is Used?

The analysis is based on 12 different datasets, including:

✅ 1. Transaction Patterns

  • Total transactions in the last 24 hours
  • Unusual volume or amounts
  • Any transactions flagged by automated systems

✅ 2. Fraud Risk Scores

  • A score from 0 to 100 showing how likely a customer is to be involved in fraud

✅ 3. Customer Profiles

  • Account age
  • VIP status
  • Past fraud history

✅ 4. Device Usage Patterns

  • New device logins
  • Suspicious access attempts

✅ 5. Transaction Location

  • Multiple or unfamiliar locations used in recent activity

✅ 6. Merchant Categories

  • High-risk categories like travel, electronics, gambling, etc.

✅ 7. Customer Satisfaction

  • How happy the customer has been historically

✅ 8. Internal Audit Flags

  • Compliance issues or AML (Anti-Money Laundering) alerts

✅ 9. Recent Customer Communication

  • Contact history, timing, and reasons for contact

✅ 10. Social Media Activity

  • Mentions that might indicate dissatisfaction or security issues

✅ 11. Call Transcripts

  • Full transcripts of customer service interactions
  • Used to spot behavioral anomalies or identity verification failures

✅ 12. Recent Purchases

  • Purchase behavior that may or may not align with a customer's typical profile

🔬 What Are Analysts Asked To Do?

  1. Rank the top 5 customers most likely to be involved in fraud.
  2. Explain why each customer was selected.
  3. Predict which ones are actual fraud cases (0–4 expected).

Each recommendation must: - Use logic based on hard data - Consider multiple datasets - Minimize false positives to avoid disturbing legitimate customers - Be auditable and traceable

🌟 Why Is This Important?

Fraud detection is a critical function in banking, and it’s not enough to rely solely on automated systems. This use case provides a framework for: - Making human-auditable, data-backed decisions - Training or testing fraud detection models like O1 - Creating analyst-friendly summaries with justifications - Minimizing reputational and financial risk while protecting customer trust

🌐 Ideal Use Case for the O1 Model

This problem requires multi-dataset correlation, structured reasoning, and precision in prioritization. O1 is well-suited because of its: - Deliberate processing style - Strong traceability across datasets - Audit-friendly format - Compact and logically aligned rationale generation

📄 Expected Output

The analyst (or model) should return: - A 1-paragraph executive summary - A ranked list of 5 customers with reasons - Predictions for which of the 5 are actual fraud cases - Data references and intermediate reasoning steps

This helps the investigation team act quickly and intelligently on the most urgent fraud risks.