Why do we need Big Data Testing in the Insurance Industry?

Several accounts of people leaving lucrative jobs in order to work for themselves have popped up repeatedly over recent times. Marketplace aggregators such as Uber have ensured that these dreams could be achieved by the average person. Concepts such as ‘ride sharing’ enable consumers to commute to their destination by paying per trip and using the Uber application, which connects them to owners of cars that would be willing to drive to the requested destination, by getting paid for their services through Uber. This implies an income of nearly USD $150,000 per year after putting in about 70 hours of driving every week. (Source)

The overriding concern of most of these cab drivers, however, is the safety of their vehicles among uncertain roads and the strangers that continuously hop in. Marketplace aggregators have therefore recognized the need to partner with insurance companies in order to provide auto insurance and motor insurance policies to their drivers.

Leading insurance providers offer an excellent array of insurance coverage including life, property, along with automobile or auto insurance. The insurance industry can clearly be said to be data-dependent. Data capture in the insurance sector remains crucial owing primarily to the following reasons:

• Facilitate and manage better the various businesses (such as Uber)
• Improve the overall efficiency
• Getting to know the customer better

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Information management and data analytics play a crucial role for insurance companies, in ensuring that strategies that aim to expand portfolios, in order to reduce the risk of not sustaining business, are implemented effectively. Data storage and analyses, especially over a prolonged period of time, helps business analysts arrive faster at more assured conclusions.

However, traditional computing techniques cannot be utilized while testing big data datasets. They rely heavily upon structured frameworks, a variety of techniques and a range of tools. Cigniti reiterates that the key to successful big data testing lies in effectively understanding the 4 nouns of big data:

• Volume
• Variety
• Velocity
• Value

Internal insurance processes consisting of insurance activities and their supervision have evolved over the course of the past decade, often adhering to more applicable principles and standards. Internal control is a chief area for concern, focusing on benefits for both, policyholders as well as shareholders through higher security standards. The insurance industry continues to adopt to big data analytics at a slower rate than other industries, such as marketing and finance. The chief reason for this is the lack of skilled personnel that can make use of the internal and external sources of data, with adequate knowledge of both, business analytics as well as the insurance sector. The competitiveness ensures that only the companies that make use of such data possess an edge over those that still do not.

With the rise in increasingly updated technology, the insurance industry has evolved over time with respect to new business models and innovation. The figure below illustrates how new industry entrants express that merely sliding by with incremental improvements is simply not enough anymore.

Source: http://www.pwc.com/us/en/insurance/publications/assets/pwc-top-issues-the-insurance-industry-2016.pdf

Overall, end-to-end internal systems of insurance companies need to be monitored using modeling platforms, as they include copies of enterprise data. “Improving Insurer Performance with Big Data”, a whitepaper written by Oracle Enterprise, states that the following are the areas that insurers would prefer to focus upon:

• Improving risk management
• Improving policy pricing
• Earlier and faster fraud detection
• Faster Claims processing
• Overall marketing

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Conclusion: Far-reaching advances in computing technology, and unpredictable widespread usage and storage of digital data over the past two decades, has forced the core discipline of insurance to be slowly, but wholly reinvented. Cigniti Technologies promises to aid global insurers in the challenges associated to such a digital transformation by providing state-of-the-art deep-domain expertise and Proprietary IPs and tools. Cigniti also recently helped a leading insurance client to improve its overall reliability of end-to-end internal system with hybrid monitoring model that increased the operational efficiency to 95%.

For further information, please see the following Cigniti web pages: https://www.cigniti.com/insurance, https://www.cigniti.com/bigdata-testing.