In this continuation of the previous part we would be discussing further how challenges in Insurance Frauds can be tackled with the help of AI/ ML
AL/ ML and Digital Technologies In Fraud Handling
As frauds pose a major challenge for the insurance industry contributing towards major financial losses, insurers today need to implement processes that detect fraud quickly and accurately. With the help of advancements in AI, organizations can leverage the power to detect and prevent such fraudulent activities e.g.:
Behavioral analytics helps insurers to tackle insurance fraud
With the help of bots speeding up the claim process gives frauds less time to manipulate any kind of data and hence stops fraud
With the real time images, insurers are using latest AI/ ML based tools to assess the cost of loss during FNOL stage itself
Insurers can identify whether the photos submitted for claims are real and not submitted for any previous claim
With huge amount of data available now a days from various sources of data, these tools can process the data rather quickly
How AI Helps in Addressing Some of Insurance Fraud Problems:
Staged Accidents:
AI algorithms would help in identifying staged accidents by analyzing images and videos related to accidents or associated with claims to detect manipulation cues. By carefully examining and analyzing image quality, visuals etc, AI algorithms can identify tampered images/ videos.
Fake Claims:
Predictive analysis models use and analyze historical insurance data, past claims made, time interval between claims. Information like claim details, policyholder information, provider details and any other information is extracted to create predictive models which then use various ML algorithms to analyze the extracted information to classify the claim as fake or genuine.
Forged/ Manipulated Documents:
AI enabled OCR technology, image analysis algorithms can extract information from various documents like forms, records, history etc and detect any inconsistency or manipulation like forged signatures/ dates etc.
Compensation Frauds:
Text mining and NLP techniques can be used to analyze data like incident details, witness statements, medical reports etc and identify patterns to indicate conflicting information to detect worker's compensation related frauds.
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