Artificial Intelligence within the Insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors.
While faster claims processing is a desired goal for most insurance companies, speed comes with the potential risk of paying more fraudulent claims.
Most Insurers are aware of this problem, which is why they’re looking to leverage artificial intelligence to detect fraudulent activities in insurance claims.
But to incorporate those AI technologies into claims fraud management, it’s important to understand the three critical components of operationalizing them!
AI system supported with enough data
Developing algorithm for AI claims require a lot of data. The more data that’s analyzed, the more effective the system will be at automatically recognizing suspicious patterns indicative of a fraud.
Analyzing a wide variety of data along with internal data sets is essential to recognizing any emerging patterns and identifying fraud risks.
Deriving actionable insights from collected data
It is important to analyze if the data collected is able to provide right insights from your AI model.
McKinsey reported that insurer data is typically “incomplete or miscoded and substantial effort is required to bring the data into working condition.”
Predictive models should not only show claims for fraud potential but also provide relevant reasons suspecting any attributes pointing towards fraud behavior.
Key success factors include providing insights that enhance decision-making, focusing on data that matters and embracing advanced analytics and science.
Depth of data derived
Deriving actionable insights from AI models not only requires the right data but also the necessary depth of data.
Over the past few years, the velocity, variety and volume of data coming into organizations has increased dramatically.
For example, First Notice of Loss (FNOL), which had conventionally been a telephone call process, is now capturing data through multiple processes like email, online submissions , SMS and image/video attachments. Then, throughout the lifecycle of a claim, the handler can still continue to receive hundreds of reports and communications from all interested parties.
And as adjusters finally gathers reports, interviews, and specifics on the event and entities, adding that information is vital for final accurate analysis.
Technologies like Artificial intelligence, Machine learning, Computer vision are all improving Fraud analytics and the way the organization uses the data.
The insurers must now exploit the existing data by using AI and fraud analytics to create a effective insurance claims fraud detection.
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