Harnessing Predictive Analysis for Fraud Prevention

Imagine having the ability to predict behavior and detect fraud before it happens. That’s the power of Pattern of Life (POL) analysis. Using historical data, POL identifies trends and anomalies, making it an indispensable tool for spotting irregularities and preventing fraud. POL is used across many industries including like healthcare to detect fraudulent insurance claims by analyzing patient treatment patterns, finance to spot unusual transaction patterns to prevent money laundering, and retail to monitor purchasing behaviors to detect and prevent return fraud.

Predictive analysis leverages statistical algorithms and machine learning techniques to predict future events based on historical data. POL fits into this framework by providing a detailed view of behavior patterns, which can be analyzed to identify deviations indicative of fraudulent activity. By integrating POL with predictive analysis, organizations can proactively identify and mitigate risks before they result in significant losses.

For one of our civilian clients, Aeyon utilizes machine learning algorithms to analyze POL data, which helps anticipate future application behavior and detect deviations from expected patterns. Specifically, our R code for POL analysis incorporates an exponential weighting function with a decay factor, prioritizing recent data to enhance sensitivity to recent changes. Customizable alert thresholds allow us to match the specific needs of organizations, ensuring accurate and relevant fraud detection.

Each week, we perform POL analysis for organizations, identifying instances where application counts exceed expected thresholds. This process has proven effective; from September 2023 to May 2024, our advanced analytics led us to contact organizations 442 times, resulting in the identification of abnormal, potentially fraudulent behavior and triggering further investigations.

We are currently developing Generation 2 of our POL solution by building an AI model using Keras and TensorFlow. This model aims to predict individual high-risk applications based on multiple variables, enhancing our ability to detect and prevent fraud. This advancement will further refine our fraud detection capabilities, ensuring even greater accuracy and efficiency.

By leveraging POL and predictive analysis, we can proactively address potential fraud, and protect public funds. Our innovative approach not only serves our civilian client but also sets a precedent for fraud prevention across various sectors. At Aeyon, we are committed to pioneering solutions that safeguard the integrity of essential programs and support the communities they serve. Our POL-based predictive analysis is just one example of how we are leading the charge in fraud prevention and data analytics.

Let's connect to discuss how our advanced analytics can help your organization detect and prevent fraud, ensuring the security and efficacy of your financial programs.