Real-Time Expense Report Generation from Authorization Data
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Solution Overview
Problem
Existing expense reporting systems are prone to errors and inefficiencies, including delayed submissions, forgotten expenses, and inaccurate accounting, due to manual entry and reliance on settled transactions.
Innovation Solution
A system that generates expense reports in real-time based on transaction authorization data, utilizing contextual information and machine learning to automate the process, identify missing information, and classify expenses accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual entry and reliance on settled transactions are used for expense reporting, then employees can submit expense reports after expenses are incurred, but the process suffers from delayed submissions, forgotten expenses, and inaccurate accounting
Solution Approach 1:
The system performs preliminary action by generating expense reports at the time of transaction authorization rather than waiting for settlement. The expense reporting system receives authorization data from card issuers and generates expense reports immediately, capturing expense information before the transaction is finalized. This preliminary generation eliminates delays and prevents forgotten expenses while maintaining accuracy through real-time data capture.
2Productivity
If real-time generation of expense reports based on authorization data is implemented, then timeliness and accuracy of expense reporting improve, but the system complexity increases due to integration requirements with card issuers and processing of authorization data
Solution Approach 1:
The expense reporting system performs multiple functions through a single integrated platform: it receives authorization data from card issuers, generates expense reports, processes expense information, and manages reimbursement workflows. This multi-functional approach consolidates what would otherwise require separate systems for data collection, report generation, and expense management, thereby improving productivity while managing system complexity through functional integration.
3Measurement precision
If machine learning models are used to classify expenses and identify missing information, then thoroughness and accuracy of expense classification improve, but the computational resources and processing time required increase
Solution Approach 1:
The machine learning model performs self-service by automatically classifying expenses and identifying missing information without requiring manual intervention. The model processes authorization data, determines expense categories, and flags incomplete information autonomously. This self-service capability improves classification accuracy while reducing the need for additional computational resources that would be required for manual review and verification processes.
Data Source
AI summary
An expense report generation system receives transaction authorization data from a card issuer and compares the data with expense report generation criteria to determine whether to generate an expense report, prior to settlement of the transaction, based on the authorization data. The expense report generation system evaluates additional data obtained from other data sources including contextual information of the employee, transaction authorization, location, and other employees to generate the expense report. The expense report generation system subsequently updates the generated expense report based on updated transaction authorization data and/or transaction settlement data. The expense report generation system trains and uses a machine learning model for efficiency and accuracy in generating expense reports from transaction authorization data while reducing an employee's burden in manually inputting expense information and the approval process burden.