Risk Management is a core function within the insurance industry. It is a vital responsibility of the underwriting team. Insurance companies collect data scattered across different business units in various formats – some of which are paper and digital, most of which are typically unstructured. The underwriting team doesn't have immediate access to the information required for internal and external decision-making, resulting in delays in making decisions and costly mistakes.
Most insurers offer similar products and services, which makes it challenging to attract new customers and retain them. As an industry, insurance is low-touch, and insurers seldom interact with their customers. A report shows that the top companies have an average customer retention rate of 93 - 95 percent, while insurance companies have an average of 84 percent.
“Change is not only likely, it’s inevitable.” – Barbara Sher What is Data Shift or Data Drift? Given human nature, it is very natural that the data we collect will change over time. Changes in data such as behavior and preferences are fast and drastic. It is even more relevant today with the impact of the Covid-19 pandemic making unprecedented changes to businesses. For a data science practitioner, the stability of data and its source are salient to develop and maintain robust ML (machine learning) solutions. Changes or drift in data will degrade the performance of predictive models.
This is part 2 of a 2-part series "Trust: The Key Ingredient for a Successful Insurance Customer Journey." In Part 1 of our blog series, we discussed how important it is for an insurer to display and build trust in any customer experience. The trust factor has continued to rise and is becoming much more significant. The buyer's journey is to garner trust. Buyers prior to 2010 usually rated insurance companies' trustworthiness and confidence level based on their responses to their inquiries. He validated information he collected from friends and relatives that were already existing customers. During that time, there was very little information about the insurer's finances in the public domain, so he had to rely on hearsay. In that process, the buyer zeroed in on a particular insurer or seller.
In recent years, Machine Learning (ML) algorithms have advanced and are now capable of learning accurate and complex patterns provided large and labeled data samples are available. However, many ML implementations fail to generalize when new data points are encountered, especially data points with different and unseen patterns or conditions from training samples.
This is Part 3 of our blog series, "Data Science Use Cases in Insurance." The insurance industry isn’t the same as it was 20 years ago. It has become much more competitive as tech companies come into the picture with new and innovative ways to compete in order to gain a foothold in the insurance industry. Consumers want to save money and will make their decisions based on the lowest price available. Some websites will help the consumer compare carriers’ prices and offerings to choose the best deal. Unfortunately, this is causing insurance companies to make price their priority over quality and customer satisfaction.
This is part 2 of the blog series, "Data Science Use Cases in Insurance."
This article is Part 1 in a 5-part series titled "Data Science Use Cases in Insurance." Today everyone is talking about Artificial Intelligence (AI). But what is AI? AI is a science that enables computers to think like human beings. Is AI a new concept in the field of computer science? No, AI has been around for more than half a century now. (The term “Artificial Intelligence” was coined in 1956).
The past two decades of the insurance industry has seen a lot of experimentation of distribution models: Max Life starting with a multi-level marketing model, Aviva trying to build both agency and bancassurance channels simultaneously, Canara HSBC starting with only bancassurance model and experimented with an agency in between, Pramerica Life & HDFC Life replicated agency sales channels to target a cluster of consumers formed, basis occupation or usage of common services.
In the post-CoVID-19 era, a tremendous amount of focus, time, and energy has been invested in understanding the customer or policyholder based on the insurer's proprietary data and the data collected by many other research agencies. The customer is deeply analyzed and offered customized or personalization, as we call, solutions and offerings. However, the investment made by insurers has only been focused on 5% of the number of policies sold or approximately 11 Lacs policyholders, that are sold by direct sales, web aggregators, and online sales. This begs the question, why are insurers only investing in distribution channels that represent 5% of the number of policies sold?
While the world around us is changing so rapidly, adapting to this change is no longer an option but a necessity to survive. The Indian Insurance Industry, too, as a whole, has proven its resilience in the current dynamic context but nevertheless continues to face various challenges to be dealt with as it navigates through this evolving landscape. As innovators, we at Aureus Analytics felt the urge to aid these giant strides with a little insight from the who's who of the industry. Thus, came about the metamorphosis of 'Aureus Insights' from a weekly blog to a periodically published yearbook in India that encapsulates this changing trend in the inaugural edition, the AI landscape, and a bird's eye view of what we can expect on the road ahead.