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Using analytics for insurance fraud detection

analytics for insurance fraud detection

The Gartner Market Guide for Online Fraud Detection has stated “by the year 2023, 20% of companies in the insurance and healthcare industries will use online fraud detection (OFD) tools, which is an increase from fewer than 5% today. And orchestration of third-party capabilities will be listed as a critical requirement in more than 75% of fraud detection RFPs, which is an increase from fewer than 25% today.”

Here is a question to ponder about before you continue reading… Do insurance leaders & insurers need to analyze the data gathered from customers to investigate potential fraud or other discrepancies?

Traditionally, insurance companies found fraud detection to be the most difficult aspect in their business to deal with. They were also working with limited data and analysis tools. They often lost customers in the process, not to mention loss of funds. Infact, we have written about this in our blog earlier. In this post, we shall dive deeper into the analytics and their role. 

Value of Analytics

Today, things have changed and insurance companies are relying heavily on analytics to help them refine the process, especially fraud detection, to not only increase accuracy but efficiency as well. With AI, ML capabilities, and connected wearables playing a big role in the business of insurance, instances of fraud are detected way ahead in the insurance cycle, with improved underwriting checks along the way. Analytics addresses all the challenges faced by the insurance companies and plays a crucial role in fraud detection. 

A few key benefits of analytics are,

    1. It helps insurers derive value of both structured and unstructured data that is gathered from customers along the purchase journey. With claims processing, insurance leaders need to differentiate between authentic and fraudulent claims, which is done through analytics tools. The main area where analytics changes things is verification of supporting documents submitted, and information from various social media channels. Analysis of the data submitted will also help speed up the claims process and settlements.   
    2. Predictive Modelling is a technique that is often relied upon across the insurance processes- premium auditing, fraud detection, claims management, target [marketing] campaigns, and many others.
    3. Understanding that frauds can occur at any source point and identifying those is crucial. Be it claims, policy maturity, premium or other third party related frauds. Since the traditional face-to-face interactions are no longer an option, having an enterprise wide solution will help the insurers identify and restrict frauds.
    4. Data integration is one of the biggest advantages of analytics. By integrating internal data and third party data with predictive analysis, insurers can assess the request for authenticity. The automation in the system helps separate the fraudulent applications, especially in medical insurance space. 

With experience in the field of AI and digital transformation, Neutrinos has enabled insurers to make successful AI adoption and integration. Having been at the forefront of digital transformation, at Neutrinos, we use the power of ML, AI and other IOT based capabilities, including connected wearables to increase efficiency of process and resources, thereby assuring high levels of customer experience are achieved.

Infact, PwC in their Global Economic Crime and Fraud Survey 2018 to understand the current state of digital fraud prevention across the globe discovered 42% of insurance companies said they had increased investments in combating frauds and crimes, while 34% felt their current initiatives were not very effective. The one thing they all agreed upon was that Artificial Intelligence and Machine Learning in combination with predictive analytics is the only way to tide over this struggle. 

Among the various solutions Neutrinos has initiated, one of them is on Analytics. A South African financial services provider’s merchant on-boarding process was heavily paper driven, and the company was looking to automate this process thereby reducing the time taken and increasing efficiency. Neutrinos introduced automation into this onboarding process with the help of IBM BPM tool. The MOB process performs Risk Assessment checks as well before creating the account number for the products apart from which uses e-signatures, which help curb frauds.  

Undeniably, there are still a few challenges that the insurance sector is ironing out, restrictions based on infrastructure, spend, expertise, cost of training employees, and the sheer number of channels available today for them. This is a topic we can go on and on about.  

Bottom-line being, the industry has understood the power of big data and analytics, including predictive modelling and incorporating these tools into their systems with the help of companies like Neutrinos. To see how you can prevent frauds, reach out to us now!.

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