Cutting-edge AI technology for highly accurate medical insurance fraud detection

What our customer encountered?

Our customer encountered several challenges in medical insurance fraud detection, including:

Defining abnormal claims was difficult due to complex medical records and many influencing factors.
Claims Investigator labelled abnormal claims inconsistently due to varying disease knowledge and prescription patterns.
Traditional rule-based fraud detection methods were not effective against evolving fraudster tactics.
Product or plan data changed constantly, requiring frequent updates to fraud detection logic.
Redefining fraud detection rules was time-consuming, especially with large datasets.

The challenges resulted in:

Complex labelling led to wrongly flagged legitimate claims and missed fraudulent ones.
Inadequate updates allowed fraudulent claims to go undetected.
Frequent updates and large datasets strained resources and slowed down the processing.
Delays in processing legitimate claims, led to frustration of customers.
Uncovered fraud and elevated false positives impacted the insurer’s finances.

What our customer needed?

The client required an advanced fraud detection system to handle dynamic data, streamline processing, improve customer satisfaction, and prevent financial losses.

The Solution

AI-powered NSure, a fraud detection framework harnessing the capabilities of AI, ML, and NLP algorithms, was developed and delivered. This framework efficiently consolidated historical data from diverse sources, processed extensive datasets, refined rules, and conducted social network analysis, ensuring swift and precise fraud detection. Moreover, NLP algorithms understood unstructured text with a higher level of accuracy, minimizing false positives by identifying the suspicious patterns and expediting decision-making.