AI-Powered Transaction Classification System for Personal Finance Management Enhancement

The client’s existing rule-based transaction classification system misclassified approximately 35% of customer transactions, leading to inaccurate data presentation, reduced customer trust, and suboptimal personal finance management features. The current system also lacked real-time categorization and intuitive insights, hindering user engagement and personalized financial advice.

A mid-to-large size retail banking institution aiming to improve customer engagement and financial insights through advanced transaction categorization and personal finance tools.

• Develop and implement an AI-powered transaction classification system with an accuracy target of at least 92%, properly categorizing 98% of customer transactions by value.
• Enhance the personal finance management capabilities within the digital banking platform by providing automatic transaction categorization, real-time notifications, and actionable insights.
• Increase mobile banking app engagement by improving transaction data presentation, encouraging more frequent logins and interactions.
• Leverage structured transactional data for advanced customer segmentation, targeted product offerings, and personalized notifications.
• Build robust, scalable architecture with real-time processing capabilities, ensuring high availability and security.

• Automated analysis of transaction attributes including amount, description, recipient details, currency, and Merchant Category Code (MCC).
• Categorization of transactions into predefined groups such as transportation, entertainment, shopping, health & beauty, income, expenses, savings & investments, subscriptions, and other relevant categories.
• Real-time transaction data streaming and processing using a scalable platform (e.g., Apache Kafka).
• A microservice architecture for ML model deployment to support high availability and easy integration.
• AI and ML models to be trained on anonymized transaction data to improve accuracy and adapt to market-specific payment types.
• User experience enhancements within the mobile app, including active hints, alerts, notifications, and visualization of spending patterns.

• Bank’s core transaction processing systems for sourcing customer transaction data
• Existing mobile banking application for user interface integration
• Notification and alert system for real-time customer engagement
• Data analytics and segmentation modules for marketing and product recommendations

• Achieve at least 92% accuracy for transaction categorization
• Support real-time processing and classification with minimal latency
• Ensure data security and compliance with relevant regulations
• Design for scalability to handle increasing transaction volume and user base

Implementation of the AI-powered transaction classification system is expected to significantly improve transaction accuracy, reaching over 92%, thereby increasing customer trust and engagement. The enhanced personal finance features and real-time insights will promote more frequent app usage, boost customer loyalty, and enable the bank to leverage structured spending data for targeted marketing, product recommendation, and advanced customer segmentation.