Big Data analysis is revolutionizing underwriting as humongous amount of real time digital customer data is now readily available to underwriter for gaining meaningful insights to perform risk analysis.
As per KMPG research, “Currently, more than 4 trillion gigabytes of data is present in the world, and it’s forecasted to reach 44 trillion gigabytes by 2020. (2)” It’s hard to fathom but the ability to harness this data will give insurance companies an edge over their competitors.
In today’s era when companies are getting more customer centric by personalizing their product portfolio as per the customer needs & behavior, it becomes essential to understand the consumer behavior.
Let’s focus on how underwriters in insurance industry can leverage the power of GPUs?
What is GPU?
“GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications. A simple way to understand the difference between a GPU and a CPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.(1)”
Data grows to a size that makes other tools slow to a crawl. GPUs will help organizations to interact with up to billions of records and changes can be seen in seconds. GPUs have the ability to query their largest datasets and render the results in milliseconds. That’s how powerful the GPU is.
With the emergence of GPUs, underwriters will be able to perform more accurate & more informed risk assessments in a fraction of time it currently requires.
The need for GPUs is clear, where Market Researcher Forrester predicts, “getting insights from BIG data can earn up to $ 2 trillion by 2020 (4)”.
The biggest opportunity lies for the organization is to turn this BIG data into insights. For example analyzing the driving of individual driver, or drivers in a particular region, comparing the riskiness of drivers in different region, Correlating risk factors (harsh braking, etc.) with claims & accidents, analyzing the risk of particular roads or neighborhoods, etc. But being first to market with ground-breaking and market-shaping applications is not so easy, especially for organization locked into traditional application development methodologies.
Automated underwriting continues to be the top priorities for insurers. Underwriters in the future will be able to make informed decisions in minutes by leveraging gigabytes of data.
Impact of BIG data on entire insurance value chain: