How In-Memory Computing can help insurance companies thrive in the face of rapid change

By Nikita Ivanov | 29 November 2017

Along with most industries that rely on technology, the insurance industry faces great challenges and opportunities. Technology-powered capabilities, such as online and mobile apps, smart contracts, Robotic Process Automation (RPA), and telematics, offer new ways to engage customers, streamline internal processes and improve risk assessment. At the same time, insurance providers are challenged to meet evolving consumer expectations for instant turnaround on quotes, underwriting, and claims processing – using their digital channel of choice. Increased competition, along with economic, political, regulatory, and climate uncertainty, add to the pressure to operate efficiently and strategically.

Successfully navigating these changes require real-time system performance, streaming capabilities, and sophisticated analytics to help insurance providers quickly monitor and analyze the many data streams now relevant to their businesses. And they must be able to easily and cost-effectively scale these capabilities in the face of continued rapid growth.

To achieve the required level of performance, scalability, and analytical sophistication, forward-thinking companies need to consider taking advantage of in-memory computing as part of the insurance technology (or “InsurTech”) mix that they deploy. Already a mainstream technology in the financial services, fintech, and payments services industries, in-memory computing offers insurance companies the ability to cost-effectively add next-generation performance and scale without having to consider a costly and time-consuming rip-and-replace strategy that would certainly undermine competitiveness. Read “Empowering Insurance Companies with In-Memory Computing,” a new white paper by GridGain System, for a deeper discussion about this topic.

Here are some of the key technologies and strategies that in-memory computing can help insurance providers employ:

Robots and artificial intelligence. Many insurance providers are turning to techniques such as Robotic Process Automation (RPA), artificial intelligence (AI), and machine learning to automate complex processes, including risk assessment and claims processing. For example, Lemonade, the app-based home and renter insurance provider, advertises “90 seconds to get insured” and “3 minutes to get paid” – promises that were unthinkable just a couple of years ago.

Blockchain. Blockchain, the distributed, peer-to-peer technology behind digital currencies like Bitcoin, is also being used across a variety of industries to create smart contracts that automate many administrative processes and lower operating costs. In the insurance industry, for example, Etherisc is leveraging the Ethereum Blockchain to offer low-cost crop insurance for rural farmers in developing countries and to provide a decentralized flight delay insurance application that can autonomously issue policies and pay out valid claims.

IoT. The Internet of Things is revolutionizing risk-management, enabling companies to use sensor-enabled, data-transmitting devices to measure in real time risk parameters related to people, cars and homes. Often referred to as “telematics” or “usage-based insurance” (UBI), this strategy is currently being employed by auto insurers to monitor risk-related data, such as miles driven, times of day driven, locations visited, airbag deployment, and acceleration and braking patterns. The National Association of Insurance Commissioners has reported that approximately 70 percent of all auto insurance carriers are expected to use telematics by 2020. With wearable IoT devices and smart homes becoming more common, telematics is also starting to make an impact on life & health (L&H) and property & casualty (P&C) insurance. For example, John Hancock Life Insurance has a program that involves giving Fitbit bands to policy holders – to encourage wellness – in exchange for discounted life-insurance coverage.

Closing the performance gap with In-Memory Computing

As insurance providers strive to implement these new capabilities and provide always-on real-time services, many are finding their legacy data systems overwhelmed by the avalanche of new data streams. As a result, they are looking for high-performance, state-of-the-art data technologies to close the “performance gap” – that is, they are looking for the ability to process and analyze enormous amounts of data in real-time.

In-memory computing platforms provide parallel, distributed processing across a pool of RAM deployed on a computing cluster – eliminating the latency of disk-based systems. The technology, composed of open source software and commodity servers, can be inserted between the application and data layers of existing systems, adding the tremendous speed and scale insurance companies need without rip-and-replace. Simple, low-cost scaling is achieved simply by adding new nodes to the cluster. Until recently, the high cost of RAM meant in-memory computing was economically feasible only for very high-value applications. But today, a steady drop in the price of RAM has made in-memory computing platforms economical for a much wider range of use cases, including in the insurance industry.

For new insurtech strategies, an in-memory computing platform can provide response times that are up to 1,000x faster than traditional disk-based approaches, and can maintain this performance and low latency as the system scales. In-memory computing also makes it feasible to build systems based on hybrid transactional/analytical processing (HTAP) architectures. HTAP can eliminate the need for a separate OLAP system and allow companies to standardize on a single architecture. For insurance companies that rely on rapidly analyzing huge amounts of data, HTAP can result in dramatic decreases in hardware, software, development and maintenance costs, reducing total cost of ownership and improving return on investment.

The rapid changes in the insurance industry in recent years demand a new level of transactional speed and analytic power. Fortunately, in-memory computing is now a mature technology. Combining an open source framework with the use of commodity hardware, in-memory computing platforms provide high performance, high scalability and cost savings across a range of industries. Fully supported, enterprise-grade in-memory computing platforms are also now available, providing a comprehensive, reliable and secure way for insurance providers to cost-effectively achieve the high-performance edge they need.

To prepare for tomorrow, insurance providers should begin today to explore how in-memory computing can help them adapt to today’s rapidly changing, always growing, information-centric, technology-driven reality. 

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