Financial Fraud Detection and Risk Intelligence

AI-powered fraud detection that transforms info into real-time risk scores, helping analysts identify suspicious activity faster, prioritize investigations, and build safer financial ecosystems.

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Inspiration

Financial fraud is becoming increasingly difficult to detect as transaction volumes grow. Manual review can be time-consuming, while traditional rule-based systems may generate too many false alerts or miss complex fraud patterns.

We wanted to build a system that could transform raw transaction data into actionable fraud intelligence. Our goal was to help analysts quickly identify suspicious transactions, understand their risk level, and prioritize investigations.

What it does

Financial Fraud Detection and Risk Intelligence uses machine learning to identify potentially fraudulent financial transactions.

The system processes transaction data and uses an XGBoost classification model to generate a fraud probability. This probability is converted into an intuitive 0–100 risk score, with transactions categorized as:

  • 🟢 Low Risk
  • 🟡 Medium Risk
  • 🟠 High Risk
  • 🔴 Critical Risk

A Streamlit dashboard allows users to upload transaction data, view fraud statistics, analyze risk distributions, filter suspicious transactions, and inspect individual transaction details.

The current system achieves:

  • ROC-AUC: 0.8987
  • PR-AUC: 0.5187
  • Fraud Precision: 58.26%
  • Fraud Recall: 45.20%
  • F1-score: 50.91%

How we built it

We used the IEEE-CIS Fraud Detection dataset as the primary data source.

The workflow consists of:

Transaction Data → Preprocessing → Feature Engineering → XGBoost → Fraud Probability → Risk Score → Streamlit Dashboard

Key technologies include:

  • Python for data processing and machine learning
  • Pandas & NumPy for data preparation
  • Scikit-learn for evaluation and preprocessing
  • XGBoost for fraud classification
  • Matplotlib/Seaborn for analysis and visualization
  • Streamlit for the interactive dashboard

We used a chronological train-test split to provide a more realistic evaluation setup for transaction data, where future transactions should not influence model training.

Challenges we ran into

One of the biggest challenges was dealing with the highly imbalanced nature of fraud detection datasets. Fraudulent transactions represent only a small portion of overall transactions, making accuracy alone an unreliable metric.

We therefore focused on metrics such as ROC-AUC, PR-AUC, precision, recall, and F1-score.

Another challenge was converting machine-learning predictions into information that could be easily understood by non-technical users. Instead of displaying only raw probabilities, we introduced risk scores and risk categories.

We also had to design the dashboard so that large volumes of transactions could be filtered and investigated efficiently.

Accomplishments that we're proud of

We successfully developed an end-to-end fraud detection pipeline that goes beyond simply training a machine-learning model.

Our main accomplishments include:

  • Built an XGBoost-based fraud detection model
  • Achieved 0.8987 ROC-AUC
  • Converted model probabilities into an intuitive 0–100 risk score
  • Created four understandable risk categories
  • Developed an interactive Streamlit fraud intelligence dashboard
  • Added transaction filtering and risk analysis
  • Used chronological validation for a more realistic transaction prediction scenario
  • Created a foundation for explainable and real-time fraud detection

Most importantly, we transformed a machine-learning prediction into a workflow that can support practical fraud investigation.

What we learned

This project helped us understand that fraud detection is not simply a classification problem.

We learned the importance of:

  • Handling severe class imbalance
  • Selecting appropriate evaluation metrics
  • Using chronological validation for transaction data
  • Understanding the difference between probability, threshold, precision, and recall
  • Designing ML outputs for actual users rather than only model evaluation
  • Converting complex model predictions into understandable risk intelligence

We also learned that an effective fraud detection system needs to combine machine learning, visualization, investigation workflows, and continuous monitoring.

What's next for Financial Fraud Detection and Risk Intelligence

We plan to evolve the prototype into a more complete fraud risk intelligence platform.

Our next improvements include:

  • SHAP-based explanations to show why individual transactions receive high-risk scores
  • Adjustable alert thresholds so analysts can balance fraud detection and false positives
  • CSV export for suspicious transactions
  • Analyst investigation workflows with statuses, notes, and ownership
  • Real-time API scoring for transaction-level fraud detection
  • Data-quality validation before model inference
  • Model drift monitoring to detect changes in transaction patterns
  • Time-based and merchant-level analytics to identify emerging fraud patterns

Our long-term vision is to move from a static fraud classifier toward a continuously monitored Fraud Risk Intelligence System that helps organizations detect, understand, and respond to suspicious financial activity faster.

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