Fraud Detection

Helps your team identify risky claims and improve claim handling outcomes

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Overview

Consistently, fraudulent claims pose a challenge to different TPAs, PEOs, and companies when dealing with Workers’ Compensation and General Liability. According to the National Insurance Crime Bureau (NICB), at least 10% of all Property and Casualty Claims are fraudulent, demonstrating how this one obstacle alone is costing billion dollar losses yearly in the country.. Specifically with Workers’ Compensation, insurance professionals may be met with claimant fraud, provider fraud, and vendor fraud that may act as a threat to not only insurance companies, but the average household as well. It’s also important to note that increases in fraudulent claims have increased insurance premiums and have negatively impacted prices of goods (NICB). Not only can fraud be difficult to detect, but claims adjusters and risk managers can spend lengthy amounts of time as well as money on investigations and evaluations. However, with Klear.ai’s solution, fraudulent claims can easily be detected and avoided in order to prevent it from taking such a huge toll on companies. As one of Klear.ai’s top proven models, the Fraud Prediction Model utilizes artificial intelligence and machine learning algorithms to flag any potential fraud or abuse within Workers’ Compensation claims. By incorporating the Klear.ai solution into your claims management systems, insurance professionals can quickly single-out fraudulent claims.

Deeper look

As a way to detect and prevent fraud early on in the claims’ life cycles, Klear.ai offers 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 into your claim 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. 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 fraud management. 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 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. 

Why Klear.ai ?


Klear.ai’s Fraud Prediction Model, powered by artificial intelligence and machine learning algorithms, can assist insurance professionals in managing claims more effectively. Being a cloud-based system that has a high level of accuracy, it can easily integrate with a multitude of third-party systems and self-owned enterprise applications. Within just the last few months, 1.5% of claims were being flagged as ‘Suspicious,’ demonstrating the necessity of the Klear.ai solution to easily detect suspicious claim activity at an early time. In the quick demonstration, potential clients would be able to learn specifically the predictive models that Klear.ai has created along with its ability to organize and interpret complex data. Specifically, in regards to fraud detection within Workers’ Compensation, the demonstration will showcase the interactive and easy-to-use platform that would predict whether a claim is ‘Suspicious’ or ‘Non-Suspicious.’ How the platform is able to produce a model confidence percentage will also be thoroughly touched-on and those who do schedule a meeting will be able to see first-hand how they are able to adjust the deck completely to their own liking and convenience. Detecting fraud is often an extensive and difficult task, but with Klear.ai’s thoroughly trained and developed models, insurance providers and risk managers can quickly anticipate any abuse or fraud.

FAQs

What is fraud in Workers’ Compensation claims?


Specifically in Workers’ Compensation, insurance professionals may be met with claimant fraud, provider fraud, and vendor fraud that may act as a threat to not only insurance companies, but the average household as well. Misclassification, under reported wages, and inflation of bills are all ways and examples of fraud within Workers’ Compensation claims and can be extremely costly to companies. The risk of Workers’ Compensation fraud can take a huge toll on businesses and lead to higher insurance premiums as well.




How can Klear.ai detect fraudulent claims early?


Klear.ai’s solution is able to provide valuable insights into your claim 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. 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 fraud management. 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.




What is the Fraud Prediction Model?


The Fraud Prediction Model is a predictive model that is powered by artificial intelligence to classify the Workers’ Compensation 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 also able to calculate the probability of a claim being suspicious or not.




How is the fraud status determined for open claims?


All the incoming new data and information are translated into the system which keeps the fraud status of open claims up-to-date, as well. Since our models are deep learning, the new data and previous performances will allow the algorithms to continue to adjust and become more accurate over time.




How would Klear.ai’s Fraud Prediction Model help insurance providers and risk managers?


Detecting fraud is often an extensive and difficult task, but with Klear.ai’s thoroughly trained and developed models, insurance providers and risk managers can quickly anticipate any abuse or fraud. With the Fraud Prediction Model, any suspicious claim activity can be detected and the claim will then be flagged as ‘Suspicious.’