By Martyn Kyle, head of insurance, SAS UK & Ireland

Insurance companies have to manage their data to achieve a profile of risk that is as accurate as possible, not an easy task for organisations with potentially global risk exposures. Martyn Kyle of SAS UK & Ireland discusses how organisations can manage data to not only provide accurate policies but also capitalise on a hidden economic opportunity.

Insurance companies are facing an increasing challenge in developing accurate risk profiles in the modern era. Increasing media coverage of catastrophes and exponential growth of data places a huge burden on insurers’ risk modelling. If an insurance company is going to remain competitive in the modern market without over exposing themselves to risk and at the same time bring parity to a balance sheet, managing data is essential.
For insurers to achieve proper visibility of their capital in reserve to ensure that the balance sheet is balanced they need a complete view of their risk profile. Insurers must collate all the information they have on existing insurance policies and exposures, and utilise this to model a full risk profile.

There are further benefits to managing data to improve daily business processes, and that is extracting the economic potential from ‘big data’. Big data refers to the massive amounts of organisational data that most companies possess but do not search and analyse efficiently. Through better analysis of this data, insurance companies can encourage business growth, job opportunities and product innovation.

Unlocking the value of Big Data
In March 2012, SAS published research conducted by the Centre for Economic and Business Research (CEBR) looking at the value of Big Data across various industries and its collective value for the UK economy. In total the research found that there is an economic opportunity to the value of £216 billion over the next five years if big data is harnessed to its full potential.

The report identified that the insurance industry can contribute up to £4.6 billion between 2012 and 2017 to the UK economy. This contribution can be achieved in three main areas; business efficiencies, business innovations and job creation.

The business efficiencies identified in the report have the potential to unlock up to £3.1 billion of economic opportunity. In particular, improved customer intelligence could deliver the most revenue, which according to CEBR would be £1.1 billion.

Customer intelligence is a simple way for insurance organisations to generate more value from their databases when the data is harnessed correctly. Customer data is the most valuable asset belonging to any organisation and insurers are no different in this respect. By creating a comprehensive single view of the customer, insurers can build advantages through business innovation, developing products and services tailored to the needs of individual customers. These innovations are expected to deliver a further £772 million in revenue, as better products have a higher take-up rate by the public.

The final area where economic opportunity can be extracted through business efficiencies is risk management. If insurers improve their risk appraisals through more accurate data analysis they will be able to alleviate the large costs are associated with systemic shocks. Furthermore using high-performance analytics to assess the huge amounts of data in their possession, insurance companies can improve fraud detection to save money on fraudulent claims payouts.

Insurers can also generate up to an extra £1 billion, according to CEBR, through the creation of new business opportunities and the extra benefits that come from increasing employment in the field.  Creating new jobs in research and development, sales, and customer management will cultivate an increase in the number of products sold and an increase in income through ongoing consultancy and support to existing and new clients.

High-performance analytics
High-performance analytics is a technology solution used by businesses to solve complex problems that require analysis of big data at a detailed level. The technology is specifically designed to deal with high volume, variety and velocity data. High-performance analytics can deliver results in real-time using in-memory analytics processing. This means that data is stored locally in a database and is accessed in-memory using parallel computing power. This therefore enables complex jobs that routinely take hours or days, such as firm-wide risk reports, to be done in seconds or minutes.
Chartis, a world-leading general insurance organisation serving more than 70 million clients in over 160 countries and jurisdictions, used high-performance analytics from SAS to become more competitive globally, make better decisions and ultimately increase profitability. Chartis’ newly constituted Science Team leveraged data in three specific areas: executive liability insurance, catastrophe (CAT) modelling and financial accounting. High-performance analytics enabled Chartis to improve profitability through better pricing, expedited refined claims service programmes and enhanced statutory and compliance reporting.

Chartis identified that analysing big data has become a critical objective for making better business decisions. As a major catastrophe insurer, Chartis prides itself on being a leader in risk assessment and by deploying high-performance analytics it can deliver the best quality service that its customers expect.
Chartis’ innovative use of CAT models helped it to project hurricane storm paths to accelerate the preparedness of claims adjusters. Chartis built a patent-pending system called CERT to identify gaps in field assistance and automate reports for claims adjusters. In addition, Chartis built a SAS data warehouse and reporting platform for CAT liability analysis. Through improved budgeting and underwriting, Chartis increased its exposure reporting accuracy and reduced reinsurance expenses.

Take action now
The challenge facing insurers is how to make their data work for them, both in customer intelligence and risk profiling. In order to do this, insurers need the tools necessary to be able to extract relevant information at a speed that will not hold them back as a business. High-performance analytics meets all of these challenges head on. Not only can it perform the technological aspect of aggregating and analysing enormous packets of data but it also gives an insurance company transparency on the essential internal organisational data such as balance sheets and risk profiling.   At the same time, high-performance analytics provides customer profiling in such a way that will allow improved customer service and targeted up- and cross-selling.

In the same way that Chartis has, other insurance organisations must tap into the rich resources at their disposal if they are to remain competitive.