By Ayan De, Chief Product Officer, Zopper
The financial services and insurance landscape is transforming fast, driven by technological and AI advancements, evolving consumer expectations, and innovative business models such as embedded finance and insurance. A report by Nasscom last month cited that Indian consumers lead the world in demand for contextual and embedded insurance from various financial institutions. The insurance industry is poised to leverage the latest technologies, including artificial intelligence (AI), to drive innovation and shape the future, ultimately enhancing the product lifecycle and customer experience. AI is evolving at pace, and there’s a lot to learn. That isn’t deterring insurers as revealed in the KPMG global tech report 2023 where 52 percent of respondents picked AI (including machine learning and GenAI) as the most critical technology in helping them achieve their ambitions over the next three years, as reveals KPMG report
All these data reflect a gradual but entirely positive shift by Indian customers from traditional to innovative insurance product consumption. Technology and AI have helped brands, particularly financial institutions, enhance customer experiences.
Here are a few cohorts that have embraced innovation and changed the way of perceiving insurance:
Customer-Centric Product Development
Earlier, insurance was sold primarily as a retail product.
Think about 15 years back, when credit cards became very popular in the market, and home loans started picking up. Based on the fact that many of these financiers, those selling credit cards and home finance programs, have started realising that they also need to be careful about the insurance of the customer who is taking that credit card or that home loan finance. That was the beginning of the entire embedded insurance program in the Indian market about a decade and half back. We can now see an absolute proliferation of insurance options within each similar product. You can bundle insurance while buying anything in the market today, tangible or intangible. The entire approach of embedded insurance today has become blue-sky thinking. Similarly, AI is not just about Chatgpt – it has evolved quite dramatically over the last 12 months. AI models are getting built to now scale for a better and contextual CX experience. The challenge for the insurance sector is that many of our existing tech stacks need to be upgraded quickly, where possible, and leverage the potential of AI and cloud. Cloud and AI per se are scalable technologies. It is now time for the insurance industry to decide how quickly it wants to move away from its old core systems and adopt newer technology stacks. Then, they will be in a better position to absorb these new technologies at scale. You cannot give a bespoke personalised CX experience without the power of AI running in the back end.
Decoding Decades of Business Logic
One of the industry’s challenges has been a very traditional outlook. There have been different phases in which technology has evolved in this industry. The challenge is that the oldest stack in this framework is the core policy admin system, which is akin to the CBS in the banking arena, essentially the nucleus of the entire system. Over the years, a lot of business intelligence has been built into it. The challenge is that while one can extract data and present it for consumption and ingestion by AI, how does one decipher the embedded business logic and algorithms that have been built over decades? None of the AI available for commercial use today is imaginative or intuitive enough to read beyond the data points and then extract the business logic and intelligence embedded within them. For example, when launching a new product line, channel, or exploring a new geography, those decisions typically result from the combination of two key components: data and business algorithms. However, the insurance industry has figured out ways, thanks to technology, to get the data out, democratise that entire process, and make it available for new AI engines. But building an AI for a specific use is a different case. So organisations, especially in the financial sector, need to create or own their own responsible, ethical AI models or explainable AI models. These can be curated to incorporate the necessary specifications, especially in terms of compliance and data security, for these models and their customers.
Cloud – The Real Disruptor In Product Development
At the click of a button, the infrastructure managers today can expand their compute environments, get more CPUs, and add more compute resources. Everything is set to auto-scale, so the programs are written in a way that allows for automatic expansion of infrastructure resources. This means that the moment there is a spike in requirements or a sudden need for different resource types from the server, the system responds automatically. A clear correlation can also be established between the cost of technology and cloud adoption, which enables business expansion. We can now have businesses run marketing campaigns whenever they see an opportunity – daily, in the middle of the week, at the end of the month, or whenever they demand; the infrastructure is always available.
Summing up:
As the AI technology riding on cloud expands in geometric progression, the industry is finding newer use cases to harness these in tandem. From deploying more innovative cognitive techniques to issuing smart policy contracts running on blockchain to using deep learning to uncover seemingly unrelated data connections to writing all program codes using AI tools, the insurance industry is experiencing a technology revolution never seen before. There are all indications that this might only be the beginning of a new machine era.
