Unlocking Big Data’s Potential

Migrating to such an automated approach is an obvious step in the right direction for an industry that sees about $400 billion per year in fraudulent claims. In fact, law enforcement officials have suggested that as many as one in three automotive claims could be fraudulent. With the increase in fraudulent activity, efficiency in the claims process has become all the more critical. Insurers know that to deliver good customer service and meet their other business objectives, they need better technologies to ensure quick speed-to-resolution. This is why there is an imperative need for intelligent fast data.

Companies don’t just want to use past data to inform their decisions, but rather automatically analyse the information as it is coming into the business, as well as apply predictive and machine-learning capabilities to better anticipate what’s coming down the line.

“COMPANIES DON’T JUST WANT TO USE PAST DATA TO INFORM THEIR DECISIONS, BUT RATHER AUTOMATICALLY ANALYSE THE INFORMATION AS IT IS COMING INTO THE BUSINESS, AS WELL AS APPLY PREDICTIVE AND MACHINE-LEARNING CAPABILITIES TO BETTER ANTICIPATE WHAT’S COMING DOWN THE LINE.”

Other industries may see additional benefits in marketing and sales, but at the end of the day, the insurance vertical really needs to process and make sense of the data in the moment, because how efficiently they can do it saves their customers and their business time and money.

To make fast claims processing happen, insurance providers have largely used a rules-based approach to look at submitted case data alongside other information about the claim maker (the customer). However, this method alone isn’t the ideal solution, as it can quickly become complex to maintain as the number of rules grows.

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With the plethora of parameters, such as location and age of claim makers, that follow, the process can quickly become too complicated to monitor with a rules-based approach. Instead, insurance businesses should consider technologies that merge parameters into a standard algorithm and apply machine learning, to better identify anomalies within customer claims and predict fraudulent behaviour.

From there, providers can choose which claims need further manual inspection. This leads to automated, intelligent decision- making, supporting faster approvals and internal flagging for claims identified as needing a second review.

No ‘If’s About It

In 2005, If Insurance began embracing analytics when use cases within the field weren’t well-known or even established. If Insurance used the lessons learned from digital and communications service providers and other industries that were just starting to tap into the full potential of their data, and applied those to help better its own business practices.

The original goal for If Insurance was to use predictive analytics to more efficiently identify patterns within claims, and flag and identify atypical and fraudulent claims.

Processing and analysing information that simultaneously grows in volume, variety and velocity presents huge challenges, technologically and organisationally, so If Insurance chose to tackle a complex problem with smart, systematic and adaptable systems.

By applying this technology, in conjunction with automation, to its data collection and refinement efforts, the promised benefits were soon in the insurance company’s sight. If Insurance is now able to turn most of the submitted claims – those without atypical activity identified – around through the claims process within the first 24 hours. This is because only those claims that are flagged as anomalies or as potential fraud need to be manually processed or investigated. Comparatively, manual processes around these non-atypical claims could previously take up to a week.

With automation, If can also realise time- and cost-savings by not having to drill down into the claims investigation process as often to further analyse atypical or suspected fraudulent activity. This means If Insurance customers are seeing results more quickly, too. Not only that, the insurance provider is more confident in its atypical claims detection. Accuracy has dramatically improved; today, If Insurance sees nearly three times more accurately processed claims than in 2005.

While If Insurance already knows the benefits of intelligent fast data first-hand, the insurance industry as a whole is on the brink of change. Insurance providers need that next-generation customer value add.

Automation and embedded analytics is allowing this industry, as well as others, to tap into an immense pool of unstructured data, give it structure and shift the focus from indiscriminate messages to relevant, contextual information that can lead to accurate, fast and smart actions.

Combining the depth of mediated data and predictive analytics will help the insurance vertical make a positive change on key areas of its business, from customer churn to cost-savings, and drive forward a new era of service innovation.

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