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  detecting financial fraud using agentic ai

Agentikk - Detecting Financial Fraud Using Agentic AI

Inspiration

Financial fraud is a massive problem, costing businesses and individuals billions every year. Traditional fraud detection methods depend on static rules and human oversight, making them slow and inefficient. We wanted to explore Agentic AI to automate and enhance fraud detection. By leveraging AI agents, we aimed to build a self-improving fraud detection system that can process transactions, select the best model, and optimize fraud detection strategies.

What It Does

Agentikk is an AI-driven fraud detection system that utilizes Agentic AI to handle fraud analysis end-to-end. The system consists of multiple AI agents that:

• Conducts an exploratory data analysis
  
• Performs data pre-processing
  
• Run fraud analysis models and validate results
  

This AI-driven approach ensures fast, adaptive, and accurate fraud detection with minimal human intervention.

How We Built It

1. Data Preprocessing

   • Handled missing values and outliers in financial transaction data.
     
   • Scaled numerical features and engineered new fraud indicators.
     
   • Applied feature selection to improve model performance.
     

2. AI Agent Workflow

   • EDA Agent: Performs exploratory data analysis and identifies key fraud patterns.
     
   • Pre-Processing Agent: Selects and trains fraud detection models, comparing algorithms like Random Forest, XGBoost, and Neural Networks.
     
   • Modeling Agent: Evaluates model performance using precision, recall, F1-score, and adjusts detection thresholds accordingly.
     

3. Model Interpretation & Reporting

   • Assessed false positives and negatives to improve detection accuracy.
     
   • Generated a detailed fraud detection report explaining why certain transactions were flagged.
     

Challenges We Ran Into

• Handling Data Complexity – Financial data can be noisy and unstructured. Ensuring clean and well-processed data was crucial for accurate fraud detection.
  
• Model Selection & Adaptability – Different models perform better under different conditions. The Agentic AI approach allowed us to dynamically select the best model.
  
• False Positives & Negatives – A balance had to be struck between catching fraud and avoiding unnecessary transaction blocks.
  
• Interpretable AI Decisions – Fraud detection needs explainability for regulatory and operational reasons. Generating human-readable fraud detection reports was key.
  

Accomplishments That We're Proud Of

• Successfully developed an end-to-end AI-powered fraud detection system.
  
• Built adaptive AI agents that can select and improve fraud detection models over time.
  
• Optimized fraud detection models to achieve high precision and recall, reducing false positives.
  
• Demonstrated that Agentic AI can enhance financial security by automating fraud analysis.
  

What We Learned

• AI agents can automate complex fraud detection tasks, reducing human workload while improving accuracy.
  
• Feature engineering is critical for fraud detection—well-designed fraud indicators significantly improve performance.
  
• Model selection should be dynamic—different fraud scenarios require different detection strategies.
  
• Interpretable AI is crucial—fraud detection systems must explain their decisions for trust and compliance.
  

What's Next for Agentikk

• Real-Time Fraud Detection – Deploy the system to analyze live transaction data.
  
• Improved Explainability – Integrate SHAP values to explain fraud decisions in more depth.
  
• Continuous Learning – Enable the model to adapt to emerging fraud trends over time.
  
• Integration with Financial Systems – Explore real-world applications in banking and e-commerce fraud prevention.
  

Built With

• amazon-web-services
• gemini
• python
• r
• sql