ML Fraud Prediction System for Transaction Devices
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Solution Overview
Problem
Current fraud prevention methods in banks are ineffective in proactively identifying compromised devices and accounts before fraudulent transactions occur, relying on manual interventions and outdated data, leading to inefficiencies, errors, and security breaches.
Innovation Solution
A machine learning model is developed to predict the likelihood of compromise in financial transaction devices by analyzing historical transaction data, using a gradient boosted algorithm to forecast potential risks and trigger proactive measures before fraudulent transactions happen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual fraud prevention methods are used, then human judgment can identify suspicious patterns, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual fraud detection (mechanical human analysis) with an automated machine learning system that processes transaction data, device information, and behavioral patterns to generate real-time fraud risk scores, eliminating human time constraints and errors while maintaining high detection accuracy
Solution Approach 2:
The system enables self-service fraud detection by automatically analyzing transaction data without human intervention, using trained machine learning models to independently identify compromised devices and generate risk assessments, freeing human operators from routine detection tasks
2Reliability
If reactive fraud detection is used, then fraudulent transactions can be identified after occurrence, but compromised devices cannot be identified proactively before fraud occurs
Solution Approach 1:
The patent implements preliminary action by proactively identifying compromised devices before fraudulent transactions occur, using machine learning models to analyze device information, transaction patterns, and behavioral data to predict future fraud risk, allowing institutions to take preventive measures rather than merely reacting to completed fraud
Solution Approach 2:
The system transitions from static reactive detection to dynamic proactive detection by continuously analyzing real-time transaction data, device information, and behavioral patterns, with machine learning models that adapt and update risk assessments dynamically as new information becomes available, enabling both preventive and detective capabilities
3Productivity
If automated machine learning systems are implemented, then real-time proactive fraud prediction is enabled, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the fraud detection system into distinct functional modules: data collection components that gather transaction and device information, feature extraction modules that process raw data, machine learning model components that perform prediction, and output systems that generate risk scores. This modular architecture manages complexity while enabling real-time automated fraud detection across multiple transaction channels
Data Source
AI summary
There is provided a computer implemented method, system and device for automatically generating a machine learning model for forecasting a likelihood of compromise in one or more transaction devices and subsequently triggering performing an action on one or more related computing devices based on a potentially compromised transaction device.