How Predictive Analytics is changing the world of Insurance
Increasingly, Predictive Analytics is becoming the “weapon of choice” of insurance firms. The reasons are many, ease-of-use is one, the other is because of advances in Artificial Intelligence (AI) and Machine Learning (ML), deployment is easier and faster.
Data analytics overall has started to play a big role in almost all areas of the insurance sector such as risk management for companies, maximizing their returns on investments, and improving customer service, including client retention; while Predictive Analytics specifically has become the number one defense for insurance firms against fraud.
According to the Coalition Against Insurance Fraud, fraud is one of America’s largest white-collar crimes and leads to, hold your breath, about the US $80 billion in fraudulent losses each year. With Predictive Analytics insurance companies can better detect and “flag” potential fraudulent claims.
So why is analytics rapidly gaining ground with insurance firms now, after all these years? It’s not as if data analysis is something that’s brand new to this sector. What has happened of late, though, is the rapid advancements in IT and data science technology, that has brought analytics within the easy grasp of players in this sector. Ease of use and understanding, reduction in cost barriers, and the availability of the Cloud, along with some other factors have all combined to help it along in the data-heavy world of Insurance. Specifically, where Predictive Analytics is concerned, with easier dashboards and even DIY kits, insurance companies have finally started to understand how to operationalize the output from this form of analytics.
That being said, besides fraud, Predictive Analytics is also used by insurance firms for other activities, one of them being marketing. The aim is to acquire customers and then, of course, to retain them. Predictive Analytics helps target the right customers and to predict customer churn.
Another area where Predictive Analytics is deployed is underwriting. In the world of insurance, underwriters play a vital role because it’s their job to determine whether an insurance agency should undertake the risk of insuring a client or not. They determine the risk and exposure of clients, and how much insurance should be accorded to a client, among other things. Most of the underwriting was done by humans all these decades but now analytics propelled by ML and AI has started taking over, making the task not only so much easier since they help “predict” risk better than humans, but even far more accurate
Predictive Analytics modeling leads to better pricing of policies, eventually ending in better profitability for insurers. Clearly, advances in AI and ML have accelerated the ability of insurers to predict risk. Algorithms can find trends and patterns that help forecast the probability of a risk situation recurring.
Yet, this is just the start of the insurance sector’s journey with Big Data. For now, data sources are being explored in administrative systems, claims, medical records, credit scores, to name a few departments. But the day is not too far off when analytics will be used to transform their business model, expand customer relationships, and improve internal performance management.
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