Real-Time Spend Approval Using Activity-Based Compliance Rules
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Solution Overview
Problem
Organizations face vulnerabilities due to trusting individuals with their funds, leading to potential embezzlement and unintentional misuse through digital payment means like digital cards and cryptocurrencies, lacking real-time compliance checks.
Innovation Solution
A method and system using processors to analyze scheduling data and expenditure rules, and optionally machine learning models, to automatically approve or reject expenditure requests in real-time based on user activities and predefined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time automated approval system is implemented, then embezzlement and misuse are reduced, but system complexity increases
Solution Approach 1:
The approval system is segmented into multiple independent modules: scheduling data analysis module, expenditure rule compliance checking module, machine learning model application module, and approval decision module. Each module handles a specific aspect of the approval process, making the overall system more manageable and maintainable while providing comprehensive fund protection
Solution Approach 2:
The system performs preliminary actions by pre-defining expenditure rules, pre-training machine learning models with historical data, and pre-analyzing scheduling data before actual expenditure requests are processed. This preparation work is done in advance to enable rapid real-time approval decisions without compromising security
Solution Approach 3:
The patent introduces an intermediary automated approval system that acts as a mediator between expenditure requesters and fund accounts. This intermediary layer analyzes requests against predefined rules and machine learning models before allowing transactions, providing fund security without requiring direct human intervention in each transaction
2Reliability
If manual expenditure review is performed, then compliance can be checked, but processing time increases and real-time approval is not achieved
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated electronic system that uses computer algorithms to analyze expenditure requests. The system automatically retrieves scheduling data, applies expenditure rules, and executes machine learning models to make approval decisions in real-time, eliminating the time delay associated with manual human review while maintaining compliance checking
Solution Approach 2:
The system implements feedback mechanisms where machine learning models are continuously trained on historical approval data and actual expenditure outcomes. This feedback loop allows the system to learn from past decisions and improve its compliance checking accuracy over time, while maintaining rapid automated processing speeds
3Ease of operation
If traditional payment means are trusted without verification, then ease of operation is maintained, but vulnerability to embezzlement increases
Solution Approach 1:
The system enables self-service for users by allowing them to submit expenditure requests and receive automated approval decisions without manual intervention. The automated system independently verifies compliance with expenditure rules and checks scheduling data, maintaining operational convenience while implementing security verification that prevents embezzlement
Solution Approach 2:
The patent applies preliminary anti-action by implementing preventive measures before transactions occur. The system pre-defines expenditure rules that restrict unauthorized spending, pre-analyzes scheduling data to verify legitimate business purposes, and uses machine learning models to identify potential fraud patterns, thereby counteracting embezzlement risks before they can materialize
Data Source
AI summary
A method of approving expenditures in real-time, comprising receiving an expenditure request initiated by a user associated with an organization for transferring funds of the organization in exchange for one or more products and/or services, identifying request attribute(s) relating to the user, the value, the funds, the product, the service, a time of reception of the expenditure request and/or a geographical location of the user, analyzing scheduling data obtained from one or more online data sources which is indicative of one or more activity attributes of activity(s) scheduled for the user, automatically determining compliance between the request attribute(s) and one or more expenditure rules predefined for the product(s) and/or service(s) with respect to the activity attribute(s) and transmitting a response to the expenditure request according to the determination which includes approval of the expenditure request in case of compliance and rejection in case of incompliance.