Big Data Under the Microscope

Big data seems to be all the rage these days. The term refers to the rapid accumulation of data generated by an explosion of new electronic sources. But the real conversation should be about the value of being nimble. This is where big data and high performance analytics (HPA) meet.

The amount of data we have to analyze is expanding exponentially, including social media, sentiment data, blogs, sensor data, transactional data, third-party data and other big data sources. Insurers analyzing scenarios need to make decisions in minutes, or at most hours — not days or weeks. Plus, it’s very time-consuming to create, test and evaluate every analytical model prior to production. As a result, there can be one validated, production-ready model from each modeller daily. HPA solves these problems.

HPA provides insights from big data in shorter reporting windows by using analytical capabilities executed in highly scalable, in-memory distributed architecture. Customers can prepare, explore and model multiple scenarios using data volumes never before possible. They can process complex analytical algorithms faster, quickly delivering better answers for decision-makers.

HPA is the next generation of analytical focus. IT gleans relevant data quicker than earlier analytical models and delivers it in real time. For insurers, the adoption of HPA can mean cost savings, increased revenues, lower expenses/losses, improved forecasting and accurate decision-making. But how does it do this?

Before HPA, many insurance companies relied on sampling data to run analysis. By using HPA, insurers can now run robust, precise analysis on all their data more quickly. They can also incorporate external data (such as Google maps, GPS, credit scoring, social media, etc.) to supplement the results.

Currently, insurers use a handful of variables to support segmentation and pricing. Because it operates much faster, HPA helps insurers increase the number of variables used in “what if” analysis to find the one with the biggest positive impact on profitability.

HPA-SUPPORTED INSURANCE BUSINESS

Claims Analytics

Fraudulent activities are increasing. But if not detected immediately, the insurer may never know fraud occurred. HPA helps insurers analyze organizational data for unusual behaviour and incorporates external data, such as social media, increasing the likelihood of detecting fraudulent activities before claim settlement.

One of HPA’s greatest value propositions is helping to dramatically reduce the analytical life cycle. Instead of spending weeks or months developing models that take days to run, HPA can be used to run many iterations in a matter of minutes. This capability changes the way analytics are typically performed moving toward a more agile analytics environment. Insurers can react quicker to ever-changing fraudulent activities by organized crime syndicates.

Telematics

The adoption rate of analytics is dramatically increasing. A study by ABI Research shows the number of telematics users will increase from less than 2 million in 2010, to almost 90 million in 2017.

Insurance companies are going to be inundated with data from these in-car recorders. Many insurers are already struggling to analyze existing data, so how will they handle this additional information? The answer is HPA so insurers can analyze billions of data records in a fraction of the time required by traditional computing environments.

Ratemaking and price optimization

Today, many insurers are using advanced analytical techniques such as generalized linear modelling for ratemaking and product pricing. A recent survey by

Towers Watson showed that 70% of U.S. insurers are using predictive modelling for personal auto insurance. However, actuaries often rely on using a subset of historical data to run pricing models since it is too time-consuming to prepare the data and run the models. To combat these problems, insurers are turning to HPA to process the data faster.

Customer intelligence

As customer interactions in insurance move from in-person to digital channels, insurers must react faster and predict future behaviour better. Using HPA, they can detect changes in customer behaviour in real time during digital interactions. Insurers can also improve customer experiences and make relevant, real-time offers with higher acceptance probabilities. Faster analytics deliver predictive modelling results more quickly and identify the best future action to take while considering both financial and organizational constraints. This results in the best opportunity to grow revenue at the lowest cost, leading to increased ROI.

Cat modelling

It has been reported that 2011 may end up being a record year for catastrophe losses, placing a significant impact on the financial stability of insurers. Carriers need to evaluate their loss exposure and financial position to meet liquidity requirements, often in a real-time environment. However, many are unable to achieve this because of restrictions within their existing IT and analytical environments. Last year proved how crucial cat modelling systems can be — HPA can help.

Insurance companies are well-equipped to manage the potential losses associated with claims from individual fires and automobile accidents. They have a wealth of historical data associated with such losses, so actuaries can determine future losses with a high degree of confidence. However, since catastrophic events are relatively infrequent and historical data is limited, it’s virtually impossible to reliably estimate potential future catastrophe losses using standard actuarial techniques.

THE BOTTOM LINE

HPA is not the answer to all insurance problems, but it speeds up the iterative business process of data acquisition, data analysis, variable selection, modelling and model assessment. As a result, reports run in seconds or minutes, not hours or weeks. The question becomes “How can insurers use those extra hours or even days to benefit their business?”

Many believe that HPA will revolutionize and change the way the world works. Before HPA, some data is stored, some is analyzed periodically, and calculations take days to run. With HPA, every relevant big data variable is collected, analyzed and used to predict outcomes in near-real time.

In many ways, we’re just starting to fully realize the power of data. The exponential growth of data presents both challenges and opportunities for businesses. Organizations that compete by using analytics will survive these uncertain economic times better positioned than those that do not.