Isolation Forest Model for Real-Time Insurance Fraud Detection
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Solution Overview
Problem
Current methods for detecting underwriting fraud in insurance policies are labor-intensive, error-prone, and unable to handle large volumes of data in real-time, especially in online transactions where fraudulent patterns constantly evolve.
Innovation Solution
A computer-implemented system using an unsupervised isolation forest model for anomaly detection, which processes tabular and social connection features to predict fraudulent activity by generating an ensemble of decision trees and iteratively tunes the model to improve accuracy, allowing for real-time identification and flagging of potentially fraudulent policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual screening methods are used to detect fraudulent policies, then detection accuracy can be maintained through human judgment, but the system cannot handle large volumes of data in real-time and becomes labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical screening processes with an automated machine learning system comprising isolation forest models, decision trees, and ensemble methods. This substitution enables real-time processing of large-scale policy data while maintaining or improving detection accuracy through algorithmic pattern recognition, thereby resolving the contradiction between processing speed and detection accuracy.
Solution Approach 2:
The system implements self-service through automated model training, validation, and deployment pipelines that continuously learn from new data without manual intervention. The machine learning models autonomously identify fraudulent patterns, score policies for fraud risk, and update their parameters, enabling high-speed real-time detection while maintaining accuracy through self-optimization.
2Adaptability or versatility
If rule-based systems are implemented for fraud detection, then consistent detection criteria can be applied, but the systems become limiting and outdated as fraudulent patterns evolve
Solution Approach 1:
The patent implements dynamic adaptability through machine learning models that continuously learn from new fraudulent patterns and update their detection criteria. The isolation forest and ensemble methods dynamically adjust to evolving fraud schemes without requiring manual rule updates, providing adaptability while managing complexity through automated learning processes.
Solution Approach 2:
The system changes parameters dynamically by using machine learning models that automatically adjust detection thresholds, feature weights, and model parameters based on learned patterns from training data. This allows the system to adapt to new fraudulent behaviors while managing complexity through data-driven parameter optimization rather than manual configuration.
3Measurement precision
If retroactive screening of policies is performed to detect fraud, then comprehensive analysis can be conducted, but the process becomes time-consuming and impractical for large numbers of daily transactions
Solution Approach 1:
The patent applies preliminary action by training and validating machine learning models on historical policy data before deployment. The models learn fraudulent patterns in advance and are ready to perform real-time detection, eliminating the need for time-consuming retroactive screening while maintaining thorough detection capabilities through pre-learned patterns.
Solution Approach 2:
The system rushes through the detection process by using pre-trained machine learning models that can evaluate policies in real-time without manual review. The automated ensemble methods quickly score and flag fraudulent policies, skipping the time-consuming steps of manual screening while maintaining detection thoroughness through algorithmic completeness.
Data Source
AI summary
The disclosed method and system provide an unsupervised machine learning model, specifically an isolation forest model, to identify potential fraudulent activity in policies communicated within a distributed computing system. This model is trained and fine-tuned using tabular data, including social graph connectivity features, with a training dataset containing unlabelled data and a tuning dataset comprising labelled instances of fraudulent activity. Through iterative tuning, the model adjusts its features (e.g. model splitting thresholds) to optimize detection accuracy, ensuring that anomalies predicted by the model align with labelled fraudulent policies in the tuning dataset. Subsequently, computerized actions are triggered based on the model's predictions to manage displaying, routing and processing the policy to one or more other computing devices for action based on the prediction within the distributed computing system.