Self-Adaptive Fraud Detection System with Real-Time Model Retraining
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Solution Overview
Problem
Existing fraud detection systems in banking and other sectors face challenges in adapting to real-time, ever-changing data streams, making them vulnerable to fraudulent activities due to limited portability and applicability of complex mathematical models.
Innovation Solution
A self-adaptive fraud detection system with a distributed database, real-time processing layer, and microservices architecture that continuously updates and redeploys fraud control models using online learning and redeployment mechanisms, enabling real-time adaptation and scalability to detect fraudulent data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex mathematical models are used for fraud detection, then detection precision is improved, but adaptability to real-time changing data deteriorates
Solution Approach 1:
The patent implements dynamic fraud detection models that continuously adapt to changing data patterns in real-time. The system transitions from static complex models to dynamic models that can adjust their parameters and structures based on incoming data streams, thereby maintaining both high detection precision and adaptability to evolving fraudulent behaviors.
Solution Approach 2:
The system dynamically changes model parameters based on real-time data characteristics. By adjusting parameters such as detection thresholds, model weights, and processing speeds according to the current data environment, the system maintains high detection precision while adapting to real-time changes in fraud patterns.
2Speed
If real-time processing is implemented, then responsiveness to fraud is improved, but system complexity increases
Solution Approach 1:
The patent segments the fraud detection system into multiple independent microservices, each handling specific processing tasks. This segmentation enables real-time processing by distributing computational loads across multiple service instances, while managing complexity through modular architecture where each microservice can be developed, deployed, and scaled independently.
Solution Approach 2:
The system employs universal microservice components that can perform multiple functions across different fraud detection scenarios. These multi-functional microservices reduce overall system complexity by reusing the same core processing logic for various detection tasks, while still achieving real-time processing capabilities through parallel instance deployment.
3Measurement precision
If continuous model updating is performed, then detection accuracy is improved, but processing interruption risk increases
Solution Approach 1:
The patent implements preliminary model training and validation processes that prepare updated fraud detection models before they are deployed to production. By pre-training and validating models in advance, the system can switch to updated models without interrupting ongoing fraud detection processing, thereby maintaining both high detection accuracy and processing continuity.
Solution Approach 2:
The system introduces an intermediary model management layer that coordinates between model training, validation, and deployment processes. This intermediary layer manages model updates by coordinating transitions between old and new models, ensuring that accuracy improvements are achieved without causing processing interruptions through controlled model swapping and validation.
4Productivity
If microservices architecture is used, then system scalability is improved, but operational complexity increases
Solution Approach 1:
The patent implements self-service mechanisms within the microservices architecture, where microservices automatically perform tasks such as health checking, load balancing, and coordination with other services. This self-service capability reduces operational complexity by eliminating the need for manual management of microservice interactions, while maintaining high scalability through automated service deployment and orchestration.
Data Source
AI summary
The present invention relates to an IT system for detecting fraud in a data flow comprising at least one data lake enabling the storage of large volumes of data, a platform containing at least one production environment comprising at least one self-adaptive hardware and software architecture comprising a plurality of stored fraud monitoring models, at least one memory for data storage, at least one processor, a layer for processing data in 'real time', an online learning unit and a service layer connected to the processing layer by a communication means, all or each one of the layers comprising at least hardware and software implementing a micro-service executed on the IT hardware architecture processor to enable, respectively, checking and/or monitoring of the data flow, in real time, retraining and updating of the monitoring models, in real time, and managing the data processed.