Fraud detection in workers compensation | Using AI and ML to foresee claimant and provider fraud.
ACE Event - "The Use of New Technology in Fraud Detection in workers compensation"
CHECK OUT: Klear.ai Workers Comp Fraud Detection system with Native AI : https://www.klear.ai/insurance-fraud-detection-software
As a way to detect [ fraudulent workers compensation claims ] and prevent fraud early on in the claims’ life cycles, Klear.ai offers proprietary Fraud Prediction Models that can classify the claim as either ‘Suspicious’ or ‘Non-Suspicious.’ With artificial intelligence having the ability to give better insights and act as a faster and more reliable tool, it is able to calculate the probability of a claim being suspicious or not. Statistical modeling, texting mining, database searches all work interchangeably to create the artificial intelligence-infused predictive models. To handle the complexity and influx of data, augmented analytics is used within the Klear.ai models in order to process and organize the data. Klear.ai’s solution is able to provide valuable insights for Fraud detection in workers compensation into your claims data, and with deep learning algorithms, the system is constantly learning from incoming new data which leads to the improvement in the accuracy of the results and better prediction of fraudulent workers compensation claims.
Each of the characteristics of the model––business rule-driven, anomaly detection techniques, statistical techniques, social network analytics, and natural language processing––all work alongside each other to create an effective visual platform that allows insurance professionals to handle and accurately predict fraudulent workers compensation claims EFFORTLESSLY. All these traits help to keep track of unusual patterns, notify of possible collusion, pinpoint any words that are a part of Klear.ai’s fraud library, and label any suspicious transactions. Being business rule-driven is a specific aspect that can help to anticipate and identify suspicious and fraudulent claim activity.
These automated business rules are integrated into the fraud management programs so that the model could accurately flag any abuse or fraud. Users of the dashboard with the Klear.ai solution will be able to clearly see the “Current Fraud Prediction Status,” and be given a “Model Confidence Percentage” along with the status. Within the Fraud Prediction Model, there are also four sub-models created that give claims managers an overview of how these claims would affect their companies in less than 30 days, less than 180 days, less than 360 days, and more than 360 days and Runtime.
At the ACE Virtual Forum 2021 Klear.ai’s Anand Shirur, Senior Vice President - Product Management kickstarted the discussion with the most pressing challenges of the insurance industry concerning fraud mitigation.
Speakers : Jessica Albano, SIU Unit Leader - Westfield
Frank Neugebauer, Senior Architect - Cincinnati Insurance Companies
Steve Robles, Assistant Chief Executive Officer - County of Los Angeles
Dennis Tierney, Senior Vice President, National Director of Workers’ Compensation Claims - Marsh
Watch Video Now! Visit www.klear.ai today!
Topics Covered
-Fraud detection in workers compensation
-how to detect workers compensation fraud
-fraudulent workers comp claims prevention
ACE Event – “The Use of New Technology in Fraud Detection in workers compensation”
CHECK OUT: Klear.ai Workers Comp Fraud Detection system with Native AI : https://www.klear.ai/insurance-fraud-detection-software
As a way to detect [ fraudulent workers compensation claims ] and prevent fraud early on in the claims’ life cycles, Klear.ai offers proprietary Fraud Prediction Models that can classify the claim as either ‘Suspicious’ or ‘Non-Suspicious.’ With artificial intelligence having the ability to give better insights and act as a faster and more reliable tool, it is able to calculate the probability of a claim being suspicious or not. Statistical modeling, texting mining, database searches all work interchangeably to create the artificial intelligence-infused predictive models. To handle the complexity and influx of data, augmented analytics is used within the Klear.ai models in order to process and organize the data. Klear.ai’s solution is able to provide valuable insights for Fraud detection in workers compensation into your claims data, and with deep learning algorithms, the system is constantly learning from incoming new data which leads to the improvement in the accuracy of the results and better prediction of fraudulent workers compensation claims.
Each of the characteristics of the model––business rule-driven, anomaly detection techniques, statistical techniques, social network analytics, and natural language processing––all work alongside each other to create an effective visual platform that allows insurance professionals to handle and accurately predict fraudulent workers compensation claims EFFORTLESSLY. All these traits help to keep track of unusual patterns, notify of possible collusion, pinpoint any words that are a part of Klear.ai’s fraud library, and label any suspicious transactions. Being business rule-driven is a specific aspect that can help to anticipate and identify suspicious and fraudulent claim activity.
These automated business rules are integrated into the fraud management programs so that the model could accurately flag any abuse or fraud. Users of the dashboard with the Klear.ai solution will be able to clearly see the “Current Fraud Prediction Status,” and be given a “Model Confidence Percentage” along with the status. Within the Fraud Prediction Model, there are also four sub-models created that give claims managers an overview of how these claims would affect their companies in less than 30 days, less than 180 days, less than 360 days, and more than 360 days and Runtime.
At the ACE Virtual Forum 2021 Klear.ai’s Anand Shirur, Senior Vice President – Product Management kickstarted the discussion with the most pressing challenges of the insurance industry concerning fraud mitigation.
Speakers : Jessica Albano, SIU Unit Leader – Westfield
Frank Neugebauer, Senior Architect – Cincinnati Insurance Companies
Steve Robles, Assistant Chief Executive Officer – County of Los Angeles
Dennis Tierney, Senior Vice President, National Director of Workers’ Compensation Claims – Marsh
Watch Video Now! Visit www.klear.ai today!
Topics Covered
-Fraud detection in workers compensation
-how to detect workers compensation fraud
-fraudulent workers comp claims prevention
