Fraud Detection System
A transaction-monitoring simulation designed around explainable risk signals. Instead of a vague “AI score,” the page shows why a transaction looks suspicious, what queue it belongs in, and how an analyst should think about the alert.
Interactive Model
Score, Filter, Triage
Use the page like a review analyst would: scan the highest-risk items, inspect the reason codes, and decide whether the activity belongs in monitoring, review, or escalation.
What This Models
A rules-based fraud engine where ticket size, merchant type, location, timing, and repeat behavior combine into a transaction risk score.
How To Use It
Start with the queue metrics, filter the transaction list, and inspect the reasons behind the highest-risk items before deciding whether to escalate.
How To Read The Output
A high score matters only if the alert is explainable. This page is built to surface the logic behind the score, not just the number itself.
Methodology
- Risk is additive across suspicious amount, merchant type, geography, timing, and repeat exposure.
- Reason codes are generated alongside the score so the queue remains reviewable by a human analyst.
- Queue state is deliberately simple: monitor, review, or escalate.
Limits
- No identity graph, device intelligence, or historical cardholder behavior is modeled here.
- This is not a live ML classifier and does not update from investigator outcomes.
- The value of the page is transparent fraud triage logic, not production-grade fraud prevention.