Case Study - Risk Analytics
I built a transaction risk analytics workflow that scores suspicious activity, explains why a transaction is flagged and turns model output into an investigation queue.
Fraud teams need to review suspicious activity quickly, but high-volume transaction data creates a practical problem: too many records, too many weak alerts and too little time to identify the cases that matter.
The goal was to show how anomaly detection and rule-based explanation can support triage without hiding the reason behind a risk score.
Fraud detection lives in a tradeoff: missing real fraud is costly, but sending too many normal transactions to review slows operations and damages customer experience. The dashboard therefore needs to make score, reason, exposure and queue priority visible together.
I designed the demo around a review workflow: score transactions, explain risk reasons, estimate exposure, prioritize the queue and support investigation decisions.
I framed the project around one question:
This shaped the product around anomaly scoring, risk indicators, investigation state and exportable review evidence.
I designed the transaction risk workflow, synthetic data pattern, scoring narrative, dashboard layout, risk-reason logic, investigation queue and sandbox interface. The project demonstrates machine-learning thinking, risk analytics and operational dashboard design.
The architecture connects transaction signals to risk scoring and investigation action.
Flag unusual transactions before loss or settlement exposure increases.
Score incoming claims against behavioural and historical patterns to prioritize review.
Identify account takeover, unusual transfer velocity or location mismatches for investigation.
The workflow helps fraud teams decide which transactions to review first, which risk driver triggered the case, what exposure is associated with it and whether the queue is becoming too noisy.
It also shows how model outputs can support an investigation workflow instead of sitting as isolated scores.
This is a portfolio demo using synthetic transaction patterns. It does not claim certified fraud detection performance, production latency, regulatory approval or guaranteed loss reduction.
Those claims would require real transaction history, labelled fraud outcomes, back-testing, model monitoring, security review and production incident handling.
Core skills: anomaly detection, risk scoring, investigation workflow design, dashboard UX, model interpretation and operational analytics. Typical stack: Python analytics patterns, FastAPI-style service thinking, browser-based sandbox logic and Power BI-style visual reporting.
The live demo lets visitors score synthetic transactions, inspect risk reasons and review the investigation queue.