Data visualization, where information is presented in a pictorial or graphical format, is helping insurance professionals see things that were not obvious to them before.
Insurance companies analyze historical data - which includes information from policy administration solutions, claims management applications and billing systems - to forecast and predict future losses.
The digital age has brought with it a quantum increase in the amount of data available, but it is not just the quantity of data that sets apart this time in history.
The speed with which data reach organizations, the variety of their form and the insights they contain are completely changing everything we have known about the collection, analysis and management of data. Insurers face the challenge of determining how to take advantage of all this data to price better, expand markets and improve the business of underwriting risk and handing claims.
Extracting value from this data remains elusive for many insurance companies. The optimist's vision for this tidal wave of data is that organizations will be able to harvest and harness every relevant byte of it to make supremely informed decisions. The pessimist's view is one where organizations are drowning in a sea of data. For many insurers, the pessimist's view is a more common reality.
Many decision makers - whether data analysts or senior-level executives - struggle to draw meaningful conclusions in a timely manner from the array of data available to them. Hence the true value of big data lies not just in having it, but in being able to use it for fast, fact-based decisions that lead to real business value. Insurance companies sometimes need to make decisions in minutes or hours. To address these challenges, insurers are turning to data visualization tools.
The science of extracting insight from data is constantly evolving. Tools are more readily available and industries are beginning to invest in the technology that supports big data. As the competency levels of firms continue to move along the big data continuum, increasing value will be realized.
Companies taking advantage of data visualization are able to extract maximum value from their data and take their businesses to new heights.
ART AND SCIENCE
Data visualization is an art and a science unto itself and there are many graphical techniques that can be used to help insurance executives better understand the story their data are telling. However, one of the biggest challenges for non-technical and business users is deciding which visual should be used to represent the data accurately. Auto-charting determines the most appropriate visualization by understanding the data and its composition, what information insurers are trying to convey visually and how viewers process visual information. A picture is worth a thousand words - especially when trying to understand and gain insights from data. It is particularly relevant when one is trying to find relationships among thousands or even millions of variables and determine their relative importance. Imagine if power insurers could harness the insights hidden within that vast sea of structured and unstructured data.
Below are several areas where data visualization can positively impact insurer profitability:
Catastrophe losses can have a significant impact on the financial stability of an insurance company. Carriers need to evaluate their loss exposure and financial position to meet liquidity requirements, often in a real-time environment. By using data visualization, insurance companies can create geographical risk exposure reports by augmenting existing policy data with geospatial data to assess and monitor loss exposure by geographic region.
It is estimated that, on average, five per cent of claims that should go to subrogation, do not. By using data visualization and text mining techniques, insurers can minimize the number of missed recovery cases by recognizing known and unknown subrogation indicators in the claims information. In fact, one leading European insurer was able to improve its recovery rate by over four per cent, representing millions of dollars per year added to its bottom line.
Fraud detection. Fraudulent activities are on the rise. Unfortunately, if the fraudulent behaviour is not discovered quickly, it may never be detected by the insurer. Data visualization and high-performance analytics enables insurers to analyze data within their organizations, in order to detect unusual behaviour.
Insurers can also use data visualization to look at external data, such as social media, to increase the likelihood of detecting fraudulent activities prior to a claim being settled.
As usage-based insurance becomes more widely available, data visualization software will let people visually explore billions of records/journey points and seek correlation on data sets to develop predictive models for accurately determining risk factors for pricing and claims. For example, insurers are beginning to see a direct correlation between loss experience and driving behavior - especially braking habits.
Actuaries have often relied on using a sampling of historical data to run pricing models because the time it takes to prepare and run the models was too time-consuming. Today, actuarial departments are using data visualization on the growing volumes of available data to enable more frequent variable exploration for finding subtle and non-intuitive relationships that can influence product pricing. For example, a regional workers' compensation insurer found that account size and number of judgments or liens were very meaningful in terms of which companies would eventually file claims.
Insurers have long seen data as a source of competitive advantage. But data alone is worthless - it is insights derived from the data that matter and, with the emergence of big data, the possibility for deriving insights is increasing dramatically. Unleashing the full power of big data and business analytics should be on the short list for every insurer and - as more and more insurers are discovering - data visualization is becoming an increasingly important component in the age of big data.