How Big Data Improves Claims Process
For a long time, the insurance industry has struggled with the claims process. Manual verification of claims, processing of claims amount, and segmenting policyholders before claims are made to avert undesirable outcomes have all been cumbersome for insurance companies. Thankfully, data analytics have come to the rescue of insurance companies like the proverbial knight in the shining armor. With all that data available today, it has only become easier for insurers to carefully segment policyholders and provide better products customized for individual needs. This has helped not only to cross-sell and up-sell insurance products but also to enhance customer satisfaction. In addition, Big Data has helped insurance companies to process claims quickly and efficiently. While Big Data is inherently vast and contains extremely useful information, it is also its nature to be superfluous and chaotic. Too much information and data can actually cause difficulties for insurance companies which often seek specific information and data about customers and insurance trends. This is where data analytics comes riding on horses. In this article, let us take a look at how Big Data improves claims process and saves the day for insurers. Why do we need Big Data Analytics in claims process? Because claims is a complicated business. As an insurer can vouch for it, claims processing is no easy business. Most insurance professionals consider the processing of claims the most arduous and difficult part of their professional duties. Yet, it is also the most important and crucial aspect of policy handling and processing. Processing of claims consists of four important steps: Intimation or communication: The policyholder communicates his claims to the insurer Registration: The insurer makes note of this communication, and begins the process of approving or disapproving the claim Handling: In this step, the insurer has to verify and assess the nature of the claim, and its validity Settlement: If the claim is found valid, the settlement is made, and payments are processed While it may seem simple on the outside, it is a gnarly and prickly business for those who are actually involved in the claims process. This is because care needs to be taken that customers do not feel offended at any point and that each sub-step is smooth and transparent. We must also remember that each of these four steps have multiple ramifications for the insurer, intermediaries if any, and the claimant. The claims process and the four sub-steps involve a number of decision points all of which are based on verification of data and analyzing what is already known and predicting certain outcomes. These outcomes involve operations, management of risk, settling the final amount, and ensuring that customers remain loyal to the insurance brand. Claims analytics makes sure that all these steps in claims process are easily handled, and processed quickly and efficiently, without any errors. Claims Analytics to the rescue Claims Analytics is a unique technology that uses Big Data Analytics, Predictive Analytics and programming to make sense of structured and unstructured datasets during all the four steps of claims processing. Predictive analytics helps in recognizing trends and predicting outcomes, while prescriptive analytics helps insurers to take decisions quickly. Claims Analytics as a tool can be customized for each insurer so that their tool is perfectly tailor-made for their unique product and market requirements. Claims Analytics helps pick and choose relevant datasets from a seemingly chaotic Big Data, to arrive at solutions automatically. Claims Analytics helps insurers to : Detect fraud: Insurers no longer have to worry about unpleasant conversations, and wasted man-hours in trying to assess the veracity and authenticity of claims made. Claims Analytics can be programmed to automate the process, the verifications and detecting fraudulent claims. Track renewals: Insurers can quickly renew automatically and track when policies are not being renewed so that reminders can be sent. This step also involved predicting future risks and assessing if a policy is worthy enough of being renewed. Predict outcomes: This has a variety of implications. Predictive analytics helps insurers to predict if a customer is going to be high-risk or a desirable customer. It also helps to predict market trends and claim outcomes. Gain business and market insights: Market and sales forecasting are very important for insurers to gain a competitive edge. Big Data analytics helps insurers to look at the macrocosm of the insurance market and gain business insights, so that they serve their customers better, and also grow profitable. In which areas can analytics enhance insurance claims data? Claims Analytics can help insurance industry in a number of ways when it comes to enhancing insurance claims data. Let us take a look at some of the areas that are currently being supported by Claims Analytics. Fraud: Predictive analysis uses advanced statistics and programming to make use of Big Data and derive analytics. Fraudulent claims can be identified quickly at every step thanks to algorithms, data mining, and other methods. Subrogation: Insurers can initiate subrogation processes to claim losses caused by a third party to the claimant if the situation allows for it. Claims Analytics helps wade through medical and police records, adjuster notes, social conversations, etc. to identify subrogation opportunities. Sooner these opportunities are identified, the lesser the insurer’s losses will be. Predictive analytics helps identify such opportunities quickly. Settlement: Claims Analytics helps in analyzing claim histories effectively and shorten the cycle of processing. This enhances customer satisfaction and reduces insurers’ labor costs. It also has ramifications in claim settlements made. Loss reserve: Claims Analytics can also be used to predict the magnitude of a claim that is made. Similar claims made elsewhere can be compared with current claims, and losses and expenditure can be estimated. Activity: Claims Analytics comes empowered with powerful data mining techniques which help in assigning importance to claims so that each claim can be assigned to an adjuster appropriate for the situation. This helps avoid assigning seemingly complex claims to the most experienced adjusters, only to find out the claim could actually have been processed automatically. Litigation: Litigation
