Machine Learning False Fraud Prediction System
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Solution Overview
Problem
Current systems for preventing false fraud in credit card transactions are inefficient and often fail to address claims below a certain threshold, leading to opportunities for false fraud. Issuers lack sufficient data to make accurate false fraud predictions, relying on costly and time-consuming investigations by card networks.
Innovation Solution
A method for collaborative false fraud prevention that involves receiving user-based allegations of fraudulent transactions, collecting and sharing merchant and issuer data, and applying a machine learning model to generate predictions about the legitimacy of transactions. This system provides users with reports detailing the factors behind the predictions and allows for feedback to improve the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If issuers conduct individualized transaction inquiries to detect false fraud, then measurement precision improves, but loss of time and productivity worsen
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing merchant data, device information, and transaction patterns before a false fraud claim is made. Machine learning models pre-assess risk levels and generate predictions about potential false fraud cases, so that when a claim is filed, the investigation can proceed with pre-analyzed information rather than starting from scratch.
Solution Approach 2:
The system creates copies of relevant data from multiple sources including merchant transaction records, device identifiers, IP addresses, and historical user behavior. These data copies are aggregated and analyzed by machine learning models to generate predictive insights, eliminating the need for time-consuming primary investigations of each individual claim.
2Loss of energy
If issuers set high transaction thresholds for investigations, then loss of energy and resources improves, but reliability of false fraud prevention worsens
Solution Approach 1:
The system changes the parameter of investigation selection from a simple transaction amount threshold to a multi-dimensional risk score generated by machine learning models. The model considers transaction amount along with device information, merchant data, user behavior patterns, and historical fraud indicators to dynamically determine which cases warrant investigation, regardless of transaction size.
Solution Approach 2:
The system replaces the mechanical threshold-based filtering system with an intelligent machine learning-based prediction system. Instead of automatically applying a fixed dollar amount threshold, the ML models analyze multiple data parameters and predict false fraud likelihood, enabling resource allocation based on actual risk rather than arbitrary financial thresholds.
3Device complexity
If issuers rely on card network investigations, then device complexity is reduced, but loss of information and measurement precision worsen
Solution Approach 1:
The system introduces an intermediary machine learning prediction layer between the issuer and card network investigation processes. The ML models analyze merchant data, device information, and transaction patterns to generate predictions about false fraud cases before claims are submitted to card networks, ensuring that only well-vetted cases proceed to external investigation.
Solution Approach 2:
The system performs preliminary data aggregation and analysis by collecting merchant transaction records, device identifiers, IP addresses, and historical user behavior data before card network investigations are initiated. This preliminary action ensures that comprehensive information is available to the issuer for making informed decisions about false fraud claims.
Data Source
AI summary
An exemplary method comprises receiving, by a processor, a user-based allegation of a fraudulent transaction, receiving, by the processor, merchant data pertaining to a user associated with the user-based allegation as well as a transaction underlying the user-based allegation, and receiving, by the processor, issuer data pertaining to the user and the transaction. The exemplary method further comprises applying, by the processor, a machine learning model to the merchant data and issuer data to generate a prediction as to whether the transaction was fraudulent, providing, by the processor, a report to the user comprising one or more factors on which the prediction is based, the one or more factors based on at least one of the merchant data and the issuer data, receiving, by the processor, feedback relating to the prediction, and updating, by the processor, the machine learning model using the feedback as an input.