ML Payroll Fraud Detection via Neural Network Evaluation
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Solution Overview
Problem
Existing computerized audit systems for detecting payroll fraud are limited by their reliance on fixed rules, which fail to adapt to changing conditions, produce excessive false positives, and cannot identify new or unexpected fraud patterns, leading to inefficiencies in data analysis and missed fraud cases.
Innovation Solution
A machine learning-based system that employs various algorithms to evaluate payroll and HR data, using neural networks for dynamic fraud detection and pattern recognition, allowing for flexible and adaptive fraud identification and continuous improvement through feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed rules are used for fraud detection, then the system is simple to implement, but it cannot adapt to changes in the situation or assumptions underlying the rules
Solution Approach 1:
The patent applies dynamics by transitioning from static fixed rules to dynamic machine learning models that continuously learn and adapt to changing fraud patterns. The system updates its detection algorithms based on new data, allowing it to respond to evolving fraud schemes while maintaining manageable complexity through automated learning processes.
Solution Approach 2:
The patent changes the fundamental parameter of detection from rigid rule-based thresholds to flexible machine learning parameters that can be adjusted through training. This allows the system to adapt to changing conditions by modifying its internal parameters based on learned patterns rather than requiring manual rule updates.
2Measurement precision
If fixed rules are used for fraud detection, then the system is easy to operate, but it produces too much data with too many false positives
Solution Approach 1:
The patent replaces the mechanical rule-based system with a machine learning-based system that automatically processes and prioritizes fraud detection. This substitution improves measurement precision by learning from historical data while reducing the operational burden of sifting through false positives, as the system automatically ranks cases by likelihood of fraud.
Solution Approach 2:
The machine learning system performs self-service by automatically learning from data, identifying patterns, and improving its own detection accuracy over time without requiring manual adjustment of rules. This reduces the operational complexity of managing detection parameters while improving precision.
3Adaptability or versatility
If fixed rules are used for fraud detection, then the system has simple design requirements, but it cannot identify unexpected patterns in data that may be indicative of fraud
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical fraud data before deployment. This preliminary training enables the system to learn and recognize various fraud patterns, including unexpected ones, before actual fraud detection begins, allowing it to identify new fraud schemes without requiring complex real-time rule design.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and outcomes are fed back into the machine learning models to continuously improve pattern recognition. This feedback loop enables the system to adapt to new fraud patterns over time while maintaining manageable design complexity through automated learning rather than manual rule creation.
Data Source
AI summary
Computer-implemented machine learning (ML)-based techniques for detecting payroll fraud are provided. In one set of embodiments, these techniques employ a number of ML algorithms to evaluate different types of fraud-relevant data in different ways, such as outliers in salary increases, payment patterns, and so on. In some cases, the ML algorithms may be chained such that the output of one ML algorithm feeds as input into another. The results of these ML algorithms (or chains of algorithms) are fed into a neural network-based final evaluation engine that outputs an indication of whether a given employee is suspicious and should be audited as a potential payroll fraud case.