Strategic Innovations in Auto Insurance Industry courtesy of Big Data-driven Predictive Analytics

Emerging big-data technologies have ushered tremendous opportunities to reform businesses across many industries and geographies. The high-volume, high-velocity and high-variety of information that Big Data encompasses is not very beneficial to the businesses in its native state. The true potential of Big Data can be unleashed by using powerful predictive analytics technologies that can navigate through the expanse of data from disparate sources in real time to discover actionable insights.

Before we proceed further, let us spare a moment to understand Predictive Analytics.

Over the past several years, the major application of Analytical techniques was to investigate data to identify trends & patterns or deduce hypotheses that would summarize the current business situation in the form of reports and dashboards. Predictive Analytics is the next step in the evolution that employs an assortment of data visualization, modeling, machine learning and data mining techniques to develop models that forecast the future of a business with an acceptable level of reliability.

Let us now delve into our topic of how Big Data and Predictive Analytics are transforming the Auto Insurance business.

Insurance companies today are awash with data from varied sources, which include, but not limited to policies, claims trends, sales quotes, user demographics, social media content, competitor products etc. The ability to analyze all this information in real or near-real time allows building more sophisticated & accurate predictive models that are free from clustering anomalies and providing innovative data points by correlating tangible information from external systems with current and historical data from core systems.

In today's highly competitive Auto Insurance market place, the biggest challenge for companies seeking sustainable growth is to make premiums better reflect the company’s current risk and be able to price market segments with higher perceived levels of fraud that they are not currently reaching. This can be accomplished through better risk assessment, efficient claims processing and introducing creative product strategies.

Risk Assessment:

With Insurance companies continually challenged by strict regulations, emerging risks and market instabilities, Actuaries are progressively embracing Predictive Analytics to improve underwriting quality and optimize pricing by

  • Generating fraud propensity scores through innovative techniques like pattern and graph analysis, social network analysis, logistic and linear regression modelling etc. to expeditiously identify instances of insurance fraud.

  • Building precise Price Optimization Models based on book of business, customer-sentiment analysis on premium adjustments, up-to-date market demand feeds from online channels etc. to identify lucrative pricing structures that allow price maximization without impacting customer retention.

  • Underwriting policies in disaster-prone high risk regions by building behavioral models based on compiled customer data cross-referenced with satellite data and weather patterns to better understand and evaluate prospective risk.

Claims Management:

Recent technological advances in Big Data have prompted wide-spread adaption of Predictive modeling in Claims Management as well for

  • Comparing factors of ongoing claims like the insurance coverage, claimant demographics, injury specifics, treatment options, incident venue, fraud indicators, etc. against past claims with similar fact patterns to provide insights in more accurately formulating settlement values.

  • Accelerating claim settlement times with fewer resources by, uncovering correlations to better match available time & talent to claim tasks and automating steps in the decision making process.

  • Assessing historical claim data using Stochastic modelling for proactively identifying claims that have high probability of developing into substantial losses over time. This allows claims professionals to moderate cost escalation by allowing them time to effectively pool resources and apply optimal claim settlement strategies.

Product Strategies:

Big Data transformation is also empowering insurance companies to conduct business in dramatically new ways like

  • Comparing comprehensive data on customers driving practices captured through telematics devices with aggregate data from other sources like policy, profile, claims, actuarial etc. to offer usage-based insurance with competitive premiums based on driver’s habits, history, and degree of risk.

  • Intelligently develop content and promotions by leveraging the exhaustive heat maps generated from the website click stream data to improve customer engagement and drive online sales.

  • Mining the comments, interests, tweets, connections etc. from social media profiles to identify potential customers, popular promotions, product reviews and feedback etc. without the need for elaborate surveys.

Big data revolution and the seemingly limitless applications of Predictive Analytics are evidently leading the Auto Insurance industry into a new era. The growing popularity and the visible impact of Big Data technologies on the productivity & profitability is prompting Insurance companies to expand the usage to other operational areas like marketing, branding, sales etc. to gain competitive advantage.

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