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Importance of Advance Analytics and Technology in Insurance Fraud Detection

Importance of Advance Analytics and Technology in Insurance Fraud Detection

Rise of Technology & Implications

The reach and ease of insurance industry has gone through sea change by the digitization wave. The growing number of smartphones, gadgets and social media platforms has led to a digital revolution, which is revamping the business landscape in all sectors including the insurance. With evolving technology and increasing reach, insurers are introduced to new opportunities that can help them connect with their customers in a better way. At the same time, this same technology also opens up traditional businesses to sophisticated fraud techniques and consequently invite the regulatory wrath if they fail to protect customers’ interest.

Extent of Insurance Fraud

Motor insurance, Health insurance, Life insurance are some of the largest domains where fraud is quite rampant. Digitization has made it all the more easier to overcome the challenge of physical boundaries. According to a report by KPMG, Motor insurance frauds to the extent of USD 1.6 Billion are paid annually towards inflated and fraudulent claims. Traditional methods of countering fraud have only limited success. The biggest challenge of using these methods are:

  1. Inherent retrospective approach
  2. No real time analysis capabilities
  3. Long lifecycle from identification to action

Advance Analytics on Big Data sets holds the key to helping insurers battle the rising fraud.

Structure of Insurance Fraud

Fraud in insurance arrives in the form of inflated bills of claims, wrongly made claims, a completely fictitious claim with non-existent incident and/or character, a nexus of insured and intermediary, nexus of local authorities and fraudsters, hospitals and TPAs, and even the nexus of loss adjusters and fraudsters. In matured market investigative agencies are employed for surveillance of fraud claimant, proxy incidents, plaintiffs’ nexus with professionals, and doctor’s deliberate inducement among many other feasible fraud related activities.

Fraud Analytics Framework

Fraud analytics is an intricately detail oriented, functional logic and score based continuous investigative exercise.

fraud-analytics

The analytics framework in any fraud prevention mechanism needs advanced data capabilities to process and churn multiple data structures and sources. Advanced statistical techniques help in churning the multi-variate to create meaningful variables to identify fraudulent behaviour based on business rules.

With an exponential increase in the volume of data, current and future fraud analytics will have to run on big data infrastructure and advanced statistics to be able to process and deliver meaningful insights. Runningthrough a faster Big Data cluster and multi-variate models will help churning varied data and logic structures without time lag and usable prompts will come much faster to act on the fraud analytics plugins to act sensibly and arrest the mala fide intention well ahead of actual fraud being perpetrated.

Deep Dive Fraud Analytics Logic

Assimilating diverse types and volumes of data characteristics from various channels and creating sensible predictive analytics around this intricately complex cob webs definitely get enhanced with the application of the Big Data capability. The analysis as seen today can be scaled up to real-time prediction of fraudulent activities with fundamentally strong knowledge, legal judgement and functional rules based inter-weaved fraud libraries to back up the analytics around frauds. One of the biggest challenge of fraud is the frequently changing nature of fraud related activities across different domains, which keeps the insurance professional and analytics experts on their toes. However, the frauds have a common thread of deceiving the real event with a fuzzy incident to structure and envelop that fraudulent activity, and hence, fraudsters’ line of actions can often be predicted from previous experiences.

Advanced Technology in Fraud Analytics

More and more fraud analytics will move towards predictive time based solution, rather than seeing them as a retrospective analysis. To save stakeholders’ money in pre-emptive way, advanced analytics will emerge more faster in Fraud Analytics, as this is much diverse in nature and spread out across multitude of activities depending on the insurance portfolio, which it attacks. Advanced analytics and superlative technology can tie all those loose ends to bring a cohesive approachable solution to Fraud Analytics on ground.

Fraud Analytics in Health Insurance – Case 1

Health insurance fraud analytics should tie the threads and generate insights from a wide array of interlinked phenomenon like fraudsters’ social media profile alerts, their activity strings, modus operandi, generation of text analytics from the hospital prescriptions and bills (viz. scanning doctor’s handwriting!), nexus of third party administrators with the medical service providers and insurance companies’ technical team intervention to name a few operational cues.

Motor Insurance Fraud – Case 2 

For example, a motor insurance purchased in one state, driven in other state and reported accident in a third location can definitely raise questions for fraudulent claims. Definitely there is a clear advantage of big data based non-SQL query search around structured, semi-structured and unstructured input formats from myriad of data points, and applications of advanced statistical methods over them can only become realisable solution with latest technological advancement in the field big data technology.

Fraud analytics is surely going to see the new lights of predictive methods with less latency time through big data technology enabled advanced analytical services.

 

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About Kamal Kishore

Kamal Holds a PGDM from IIM -Calcutta and a B.Tech from IIT Kharagpur. Kamal brings with him an incredible 13+ years of experience and have worked with companies like Asian Paints . Philips Carbon , and ICICI Lombard. His area of specialization is Business Analytics, ecommerce and CRM for General Insurance.

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