“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.
As 2021 came to a close, we couldn't help but notice how what was inevitable in the future was becoming necessary now. While a normal practice would be to predict what lies ahead of us in 2022, we decided to take a different approach this year. In our second edition of the Aureus yearbook, we will take a closer look at what has delivered superior CX (Customer Experience) in the insurance industry this past year. While there is nothing new about focusing on your customers, we shall examine the role data has played in assisting companies up their CX game.
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 1 of a 2-part series "Trust: The Key Ingredient for a Successful Insurance Customer Journey." Today, everyone in the business world is talking about the customer journey and experiences starting from E-commerce, banking, and many other industries. So, what is customer experience? What is new about it?
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.
In our earlier blog, Insurance Agents - Will They Disrupt or Perish?, Aureus' Life Insurance SME Arun Agarwal shared his views on why insurers will remain invested in the agency channel by identifying the successful agent. It was a pleasure to have Tarannum Hasib, Chief Distribution Officer at Canara HSBC OBC Life share her expertise on the current state of insurance distribution as well as strategies to optimize it for the next stage of evolution.
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?