AI Transaction Dispute Prediction System
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Solution Overview
Problem
Conventional systems are inadequate in accurately identifying and predicting fraudulent activities in digital content and transactions, often confusing non-fraudulent with fraudulent ones, leading to inefficiencies and customer dissatisfaction.
Innovation Solution
An AI-based communication system that leverages machine learning and predictive analytics to monitor and analyze digital content and transactions in real-time, providing proactive alerts and resolutions for potential non-fraud disputes, integrating data from various sources and employing techniques like natural language processing and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fraud detection techniques (data mining or statistics) are used, then fraud detection capability is provided, but accuracy in identifying fraudulent activity deteriorates leading to confusion between fraudulent and non-fraudulent activities
Solution Approach 1:
The patent replaces conventional statistical and data mining approaches with machine learning-based predictive analytics. The system uses trained machine learning models that automatically learn patterns from historical transaction data, replacing manual rule-based detection mechanisms with adaptive algorithms that continuously improve accuracy through training on labeled datasets of fraudulent and non-fraudulent transactions.
Solution Approach 2:
The system performs preliminary analysis by pre-processing and feature engineering on transaction data before actual fraud detection. Historical transaction data is pre-trained into machine learning models to establish baseline patterns of normal and fraudulent behavior, enabling faster and more accurate real-time detection without retraining during transaction processing.
2Reliability
If conventional systems treat all disputes as potential fraud, then security is maintained, but efficiency deteriorates due to increased processing time and resources
Solution Approach 1:
The patent applies different levels of scrutiny and analysis to different transactions based on their risk profiles. The machine learning system assigns risk scores to individual transactions, enabling the system to apply intensive fraud detection resources only to high-risk cases while quickly clearing low-risk transactions, thereby maintaining security while improving overall processing efficiency.
Solution Approach 2:
The system performs preliminary risk assessment on all transactions using pre-trained machine learning models before full fraud investigation. This preliminary filtering identifies low-risk transactions that can be automatically approved or flagged for minimal review, reserving comprehensive investigation resources for transactions that show higher probability of fraud based on preliminary analysis.
3Measurement precision
If more resources are allocated to monitor and detect fraud, then detection capability is improved, but operational costs increase
Solution Approach 1:
The patent replaces resource-intensive manual fraud investigation processes with automated machine learning systems. The machine learning models automatically analyze transaction patterns, identify anomalies, and generate fraud probability scores, eliminating the need for human analysts to manually review every transaction and reducing operational costs while maintaining or improving detection precision.
Solution Approach 2:
The system enables self-service fraud detection where the machine learning models autonomously perform data collection, feature extraction, pattern recognition, and fraud classification without requiring continuous human intervention. The models self-train on new data and automatically adapt to emerging fraud patterns, reducing the need for ongoing manual resource allocation.
Data Source
AI summary
A system for predicting a non-fraud dispute using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a data access interface to receive instructions historical transaction and disputes data from at least one data source associated with an account issuer. The data access interface may also receive incoming transaction data associated with a transaction from at least one data source associated with an account holder. The system may comprise a processor to predict a likelihood of a non-fraud dispute associated with the transaction by: examining the historical transaction and disputes data; retrieving non-fraud dispute attributes; parsing the incoming transaction data; applying predictive analytics to the incoming transaction data to yield a prediction value; determining that the prediction value meets a predetermined threshold; and generating a prediction for the likelihood of a non-fraud dispute associated with the transaction associated with the account holder to be outputted, via an output interface to a user device.