FWA Detection: How AI Helps

Reducing the Need for Rules in FWA

Rules-based systems require constant maintenance, with auditors keying in each finding, new fraud schemes, and new parameters for identifying fraud, waste, and abuse. It is true that rules are simple to code and put into production, but it’s almost impossible to stay current when a system has thousands or even millions of lines of code supporting just as many rules. And rules can have unexpected influences on a system when they are added or subtracted.  If one rule is changed, it can have unforeseen consequences by affecting other rules that depend on it.


AI models are built using a small number of rules plus algorithms designed to identify more complex fraud. FWA utilizes labeled data and teaches the model all the rules attached to that data. The data may need some maintenance now and then, but properly designed and modeled AI continually and independently updates. If rules or regulations change, those need to be manually updated and old code removed, but in a rules-based system, filtering and making final decisions on millions of transactions takes significant time. With AI, transactions are filtered in nanoseconds making real-time results to approve or halt the transaction at source possible.


Continuous Learning

Without continuous learning, feedback, and updates, FWA detection systems quickly become antiquated. AI models record the nuances of change with businesses and their customers through continual updates. New trends and patterns are recognized and saved in the model, identifying “good” and “bad” behaviors.  An AI model improves with age as it learns more about your business, your expected outcomes, and what normal transaction behavior looks like. Model development, enhanced pattern recognition, and incremental learning are all byproducts of AI’s ability to continuously learn.


Allows for Real-Time Detection

With a lower dependence on rules, continuous learning model updates, and pattern recognition technology, fraudulent transactions to be detected and denied before they are processed. In industries such as healthcare where pay and chase investigations are still the norm, this shift to real-time detection can totally transform an industry.


Uses One-to-One Analysis

The biggest advantage of AI is that it identifies patterns that humans miss. While humans do define the problem to be solved and thresholds for the model, humans are limited by our own knowledge, experience, and biases.  AI’s algorithms identify and compare every data point within a transaction, looking for behaviors and patterns. They compare frequency, transaction size, location, health diagnoses, sudden changes in behavior, and more. The data is then compared to results from other medical claims or providers to identify anomalous behavior. AI reduces hours of investigation to milliseconds.

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