AI Banking – Real-Time Transaction Monitoring

Created by: Amara Dawood

Overview

AI Banking is an AI-powered web application prototype built for the JS Bank PROCOM ’26 Hackathon – AI in Banking. The project focuses on real-time monitoring of banking transactions to detect suspicious and potentially fraudulent activities using artificial intelligence.

The solution demonstrates how AI can assist banks in proactively identifying fraud, reducing financial losses, and improving customer trust—using only synthetic data and a working end-to-end flow.

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Problem Statement

Banks process thousands of transactions every minute. Traditional rule-based systems:

• Miss new or evolving fraud patterns
• Generate high false positives
• React too late to suspicious activity

There is a need for a real-time, AI-driven monitoring system that can intelligently analyze transactions as they occur and flag high-risk behavior instantly.

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Solution

AI Banking provides:

• Real-time transaction monitoring
• AI-based fraud risk scoring
• Clear alerts for suspicious activity
• A simple dashboard for analysis and decision-making

The system simulates how modern banks can use AI to detect fraud early, support internal risk teams, and protect customers.

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Key Features

• 🔍 Real-Time Transaction Analysis
• 🚨 AI-Based Fraud Detection (Risk Flagging)
• 👤 Customer Risk Profiling
• 📊 Transaction & Fraud Dashboard
• 🧠 Explainable AI Logic (Demo-Level)

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AI Usage

The application uses AI in a meaningful and focused way:

• Machine Learning–based fraud classification
• Pattern detection on transaction amount, frequency, and type
• Risk scoring instead of hard rule-based decisions

AI improves:

• Speed of detection
• Accuracy over static rules
• Adaptability to new fraud patterns

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Data

• 100% synthetic data (hackathon compliant)
• No real customer or bank data used

Included Datasets:

• customers.csv – Customer profiles and risk levels
• transactions_sales.csv – Transaction & sales data
• fraud_cases.csv – Flagged fraudulent transactions

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Tech Stack (Prototype Level)

• Frontend: Web-based UI (Dashboard)
• Backend: Python-based logic
• AI/ML: Fraud classification & risk scoring
• Data: CSV-based synthetic datasets

(Designed at Option B – Sophisticated Prototype level)

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Demo

A 2–3 minute demo video showcases:

1. The banking fraud problem
2. Live transaction monitoring
3. AI-based fraud alerts
4. How banks can act on AI insights

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Responsible AI Considerations

• No personal identifiable information (PII)
• Transparent AI behavior (risk flags, not black-box decisions)
• AI assists humans, does not replace final decisions
• Clear limitations documented

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Limitations

• Prototype-level AI model
• Synthetic data only
• No real-time bank system integration

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Future Enhancements

• Adaptive learning from new fraud patterns
• Integration with bank core systems
• Multi-factor risk signals (location, device, behavior)
• Role-based dashboards for bank teams

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Conclusion

AI Banking demonstrates how real-time AI monitoring can meaningfully improve fraud detection in banking. The project is realistic, scalable, and aligned with modern banking needs while remaining safe, ethical, and hackathon-compliant.

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AI Banking Innovating Secure Banking with AI created by Ammara Dawood