Real-time Expense Auditing with Machine Learning Risk Scoring
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Solution Overview
Problem
The existing expense reporting systems are prone to errors and inefficiencies, leading to rejected expense descriptions due to lack of real-time auditing and manual processing, which can result in employees incurring unreimbursed expenses or overspending/underspending, affecting both individual and organizational budgets.
Innovation Solution
An expense auditing system that utilizes machine learning to identify potential audit risks in real-time, providing warnings to employees and automating the auditing process by learning patterns predictive of reimbursement issues, and leveraging intelligent agents for natural language processing to assist users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual expense report preparation and auditing is used, then employees can submit expense reports, but the process is time-consuming and error-prone leading to rejected expense descriptions
Solution Approach 1:
The patent replaces manual mechanical auditing processes with machine learning-based automated auditing. The system uses trained ML models to automatically review expense descriptions, identify policy violations, and predict audit outcomes, substituting human auditors' mechanical review process with intelligent automated analysis that is both faster and more accurate.
Solution Approach 2:
The patent enables employees to self-audit their expense reports using the system's predictive analytics. The machine learning model provides real-time feedback to employees about potential issues with their expense descriptions before submission, allowing them to correct problems themselves and avoid rejection, thus serving their own auditing needs.
2Measurement precision
If real-time auditing is implemented, then reimbursement accuracy improves, but system complexity increases
Solution Approach 1:
The patent performs preliminary auditing actions by training machine learning models on historical expense data before actual expense review. The system pre-processes and analyzes past expenses to learn patterns of compliant and non-compliant submissions, building predictive capabilities in advance that simplify real-time auditing while maintaining high accuracy.
Solution Approach 2:
The patent applies partial auditing by focusing machine learning analysis on specific high-risk expense categories or problematic patterns rather than uniformly auditing all expenses. The system identifies and concentrates auditing resources on areas most likely to contain violations, achieving high accuracy without proportionally increasing overall system complexity.
Data Source
AI summary
Techniques for real-time expense auditing and machine learning are disclosed. An expense auditing system trains a machine learning model to compute audit risk scores as a function of expense descriptions. The auditing system receives an expense description associated with an employee. The expense auditing system computes, using the trained machine learning model, an audit risk score associated with the expense description. The expense auditing system compares the audit risk score with an audit trigger. The audit trigger includes one or more conditions that, when satisfied, identifies expense descriptions that are at risk of being audited. The expense auditing system determines that the audit risk score satisfies the audit trigger. Responsive to determining that the audit risk score satisfies the audit trigger, the expense auditing system alerts the employee that the expense description is at risk of being audited.