Expense Report Fraud Detection Using Semantic Analysis
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Solution Overview
Problem
Enterprises face challenges in detecting fraud and compliance issues in expense reports, leading to unnecessary spending on false or dishonest claims, which can be time-consuming and costly to investigate.
Innovation Solution
A computerized method using semantic analysis and machine learning algorithms to detect anomalies in expense reports by enriching data with web-scale information, verifying receipts, and identifying inappropriate expenses, thereby classifying risks and flagging potential fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual investigation of expense reports is performed, then fraud detection accuracy is maintained, but time consumption and investigation costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical investigation processes with automated computer-based systems that use machine learning algorithms, semantic analysis, and data processing to automatically detect fraud patterns in expense reports, thereby reducing time consumption while maintaining detection accuracy
Solution Approach 2:
The system enables self-service fraud detection by automatically analyzing expense reports without requiring manual human intervention for each case, using automated algorithms to identify suspicious patterns and flag potential fraud for review
2Measurement precision
If comprehensive fraud detection analysis is performed on all expense reports, then fraud detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing expense report data using machine learning models to identify high-risk patterns before full investigation, allowing prioritized processing that maintains accuracy while improving overall processing throughput
Solution Approach 2:
The fraud detection process is segmented into multiple stages: initial automated screening using algorithms, intermediate risk assessment, and final detailed investigation only for high-risk cases, thereby maintaining detection accuracy while processing larger volumes of reports efficiently
3Productivity
If automated algorithms are used to detect fraud, then processing speed is improved, but false positive rates may increase
Solution Approach 1:
The system incorporates feedback mechanisms where detection results are continuously refined based on outcomes, allowing the machine learning algorithms to learn from false positives and improve detection reliability over time while maintaining high processing speeds
Solution Approach 2:
The detection system dynamically adjusts its sensitivity and thresholds based on learned patterns and feedback, adapting its behavior to reduce false positives while maintaining high processing speed through optimized algorithmic parameters
Data Source
AI summary
In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.