Fraud Detection System Analyzing Digital Transactional Data
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Solution Overview
Problem
Current systems fail to effectively detect fraudulent activities by supplier accounts that exploit early payment discounts, leading to potential financial losses for buyer accounts during procurement transactions.
Innovation Solution
A fraud detection system that analyzes digital transactional data before and after an early payment discount is offered, identifying unusual price increases or changes in charges, and alerts the buyer account to potential fraud, thereby preventing further payments until the issue is resolved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If early payment discounts are offered to buyer accounts, then cash flow and payment speed are improved, but susceptibility to supplier fraud increases
Solution Approach 1:
The system performs preliminary fraud detection analysis before the buyer account processes or acts on the early payment discount. By analyzing transactional data, price changes, and supplier behavior patterns in advance, the system identifies potential fraud risks before they materialize into financial losses, allowing the buyer to proceed with confidence or take preventive measures
Solution Approach 2:
The system continuously monitors transactional data and provides feedback to the buyer account about detected fraud risks. This feedback mechanism enables real-time or near-real-time detection of suspicious patterns such as price increases or charge changes, allowing the buyer to respond appropriately to protect against fraud while maintaining efficient payment processes
2Measurement precision
If fraud detection analysis is performed on all transactional data, then detection accuracy is improved, but computational resources and processing time are consumed
Solution Approach 1:
The system applies fraud detection analysis selectively to specific portions of transactional data that are most relevant to fraud risk, such as price changes, charge modifications, and suspicious transaction patterns. Rather than uniformly analyzing all data with the same intensity, the system focuses computational resources on high-risk areas, improving detection accuracy while reducing overall resource consumption
Solution Approach 2:
The system dynamically adjusts the depth and intensity of fraud detection analysis based on risk parameters. When low risk is detected, lighter analysis is applied to conserve resources; when suspicious patterns emerge, the system intensifies analysis on those specific parameters. This adaptive approach maintains high detection accuracy while optimizing resource utilization
Data Source
AI summary
A fraud detection system for detecting fraudulent acts related to payment discounts from digital transactional data is disclosed. In some embodiments, the fraud detection system is programmed or configured with data structures and/or database records that are arranged to detect an occurrence of a triggering event, such as receiving an early payment discount by a buyer account from a supplier account. The fraud detection system is programmed to analyze how a first amount charged for certain items by the supplier account to the buyer account before the triggering event has changed to a second amount after the triggering event from digital documents related to procurement transactions. The certain items may include items for sale or for other miscellaneous items. The digital documents may include catalogs, purchase requisitions, purchase orders, or invoices. The fraud detection system is programmed to further detect any potential fraud committed by the supplier account based on the analysis result. Finally, the fraud detection system is programmed to send a warning of the potential fraud to other buyer accounts that have been offered promotions, such as the early payment discounts, from the supplier account.