Machine Learning Expense Document Classification and Validation
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Solution Overview
Problem
Existing approaches for expense report creation and processing are complex and burdensome, requiring manual effort to validate and reimburse expenses, which leads to delays and inefficiencies.
Innovation Solution
A machine learning-based mechanism is employed to classify and validate documents associated with expenses by using optical character recognition, text tokenization, and document classification models trained on previously validated documents, thereby automating the extraction of relevant information and the generation of reimbursement instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing is used for expense report validation, then users can review and verify each expense claim, but the processing time increases and productivity decreases
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning-based document classification and validation system. The system uses trained models to automatically extract information from receipts, validate expenses against company policies, and generate approval decisions, substituting human manual verification with automated intelligent processing while maintaining high accuracy through multiple validation checks
Solution Approach 2:
The patent introduces an intermediary validation layer between document submission and final reimbursement. This intermediary system uses machine learning models to pre-validate documents, check against expense policies, and flag only exceptional cases for manual review, thereby reducing the burden on manual processing while maintaining reliability
2Reliability
If complex manual review processes are implemented, then expense claims can be thoroughly validated, but the operational complexity and time required increase
Solution Approach 1:
The patent segments the expense validation process into distinct automated stages: document classification, information extraction, validation rule application, and anomaly detection. Each stage handles specific validation tasks independently, reducing overall process complexity while maintaining thoroughness through systematic multi-stage processing
Solution Approach 2:
The patent performs preliminary validation actions automatically before manual review is needed. The machine learning system pre-checks documents against expense policies, validates extracted information, and identifies potential issues in advance, so that manual reviewers only need to handle exceptional cases rather than performing comprehensive reviews
3Reliability
If multiple manual steps are required for expense claim processing, then thorough validation can be achieved, but the time required for reimbursement increases
Solution Approach 1:
The patent implements continuous automated validation processing that operates throughout the expense claim lifecycle. Instead of sequential manual steps with waiting periods, the machine learning system continuously processes documents as they are uploaded, performs real-time validation, and maintains processing flow without interruptions, significantly reducing overall reimbursement time while maintaining validation completeness
Solution Approach 2:
The patent replaces multiple sequential manual processing steps with parallel automated machine learning operations. The system simultaneously performs document classification, information extraction, validation checking, and anomaly detection, eliminating the sequential delays inherent in manual multi-step processes while maintaining thorough validation
Data Source
AI summary
Computer-readable media, methods, and systems are disclosed for applying machine learning mechanisms to classify and validate documents based on expense rule sets and external data validation services. Document images associated with expenses are received in connection with a reimbursable event. For each received document image data associated with the received document image is transmitted to an optical character recognition image processor that can recognize contents and associated coordinates. OCR data is received and transmitted to a text tokenizer. Tokenized text is received corresponding to expense details, and the tokenized text and coordinates are sent to a text feature generator. Text feature vectors are received and transmitted to a document classifier and a document classification received. Document fields are extracted and based thereon a document is validates and a corresponding reimbursement instruction generated.