Machine Learning Image Classification for Fraudulent Receipt Detection
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Solution Overview
Problem
Current expense report auditing systems rely heavily on human auditors, which can lead to inefficiencies, errors, and resource wastage, and lack effective mechanisms for detecting fraudulent or duplicate receipts.
Innovation Solution
Implementing machine learning models trained on historical data to classify images as authentic or generated, and to detect duplicate or fraudulent receipts, integrating Optical Character Recognition (OCR) for extracting receipt data, and using policy models to ensure compliance with organizational policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human auditors are used for expense report auditing, then accuracy in detecting fraudulent receipts can be maintained, but productivity and efficiency deteriorate due to manual processing time and resource constraints
Solution Approach 1:
The patent replaces the mechanical system of manual human auditing with an automated machine learning-based image analysis system. The system uses trained models to detect fraudulent receipts, duplicate submissions, and policy violations automatically, eliminating the need for manual human review while maintaining high accuracy through sophisticated image authentication algorithms.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the expense report submission and final approval. These models act as a mediator that pre-screens receipts, identifies potential fraud, and flags suspicious patterns before human or automated approval, thereby improving both accuracy and productivity by handling the initial filtering at scale.
2Reliability
If more human auditors are deployed to improve fraud detection accuracy, then reliability improves, but loss of time and resource wastage worsen
Solution Approach 1:
The patent replaces multiple human auditors with a single automated machine learning system that can process unlimited receipts simultaneously. The system uses ensemble models and multiple analysis techniques to maintain high fraud detection accuracy without requiring additional human time or resources, as the automated system operates at machine speed with no fatigue or downtime.
Solution Approach 2:
The patent performs preliminary fraud detection and authentication analysis automatically before human review or approval processes. By pre-screening all receipts through machine learning models that detect fake images, duplicates, and policy violations upfront, the system eliminates the need for time-consuming manual verification of legitimate receipts, thereby reducing overall auditing time while maintaining accuracy.
3Ease of operation
If traditional auditing methods are used, then ease of operation is maintained with simple processes, but device complexity worsens when implementing automated detection systems
Solution Approach 1:
The patent uses image copying and comparison techniques to detect fraudulent receipts. The system creates digital copies of submitted receipts and compares them against known fraudulent patterns, database entries, and authentication features. This copying approach maintains operational simplicity by working with familiar image formats while enabling sophisticated automated detection through pattern recognition and comparison algorithms.
Solution Approach 2:
The patent implements a universal machine learning system that performs multiple auditing functions simultaneously - detecting fake images, identifying duplicates, verifying policy compliance, and extracting receipt data. This multi-functional approach consolidates what would otherwise require separate complex systems into a single automated platform, managing complexity through integration while providing comprehensive fraud detection capabilities.
4Productivity
If automated systems are implemented to increase productivity, then auditing speed improves, but reliability worsens due to potential errors in automated classification
Solution Approach 1:
The patent merges multiple machine learning models and detection techniques into a unified automated auditing system. By combining image authentication models, duplicate detection algorithms, policy compliance checkers, and fraud pattern recognizers, the system achieves high reliability through ensemble decision-making. The merged system cross-validates findings across multiple models, reducing individual model errors while maintaining high processing speed through parallel operation.
Solution Approach 2:
The patent implements feedback mechanisms where the automated system continuously learns from audit outcomes and fraud cases. The machine learning models are retrained and refined based on detected fraud patterns, false positive analysis, and updated policy requirements. This feedback loop improves reliability over time by adapting the system to emerging fraud techniques while maintaining high auditing speed through automated model updates and continuous learning.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.