Artificial intelligence, Big data analytics and Cognitive computing; the ‘ABC’ of
new-age technology are helping redefining the contours of the insurance industry
Historically, the insurance industry has always been a slow adopter of technology, as compared with, say, the banking industry. While the technology platforms of banks have evolved from ledger posting machines to branch banking systems and core banking platforms to now becoming API-enabled digital banking, their financial services cousins – the insurance companies – have been rated to be a bit slower both in adopting and adapting to new technology in the past. However, necessity gives birth not just to invention, but also to redefining conventions. And the insurance technology may have just hit that cornerstone.
When micro-duration insurance products are needed on-demand, driverless cars necessitate a new risk assessment framework, or for that matter the real-time data pumped in from wearable devices make a plethora of health insurance offers possible, technology finds its way to fill in the gaps. Just as banks and wealth managers have seen disruptive technologies changing the rules of the game, the opportunities of creating newer products and the ability to measure risk more accurately have got the insurance players a whole new game ahead.
The advent of technology can create new ways of addressing latent demand from a customer standpoint, potentially redefining the very basics – the ABC of the industry, as they are proverbially addressed. The entire digital disruption has reduced the distance between the product factory and the end consumer massively, thereby almost eliminating the role of the intermediary. The $4.5 trillion insurance market has always been an intermediation business. With insurance firms increasingly connecting with the customers directly, the potential driven by artificial intelligence, big data analytics and cognitive computing (arguably the new ABC of insurance technology) has now brought in new opportunities and a new paradigm shift in the making. Here is how.
Consider this. When an insurer offers you cover for exactly the time you need in the specified category and can price the product accurately, what sits behind this is the mobile on-demand insurance technology driven by artificial intelligence (AI). AI has become the public face – offering interactive decision-making with chatbots helping with informed decisions. Coined as ‘micro-duration’ insurance, the machine learning tools allow for the customers to be able to pay the ‘right premium’ for the risk they are exposed to at that point for their respective profile. This pay-as-you-go pricing structure is driven by realtime assessment of the risk for the individual, rather than being based on a mass-average-pricing model that has always been the approach. No more buying a standard health or travel insurance, and no more dependence on the friendly neighbourhood insurance broker. The app on the mobile just offers you exactly what you need, precisely when you need it. Well, that is a big paradigm shift for a monolithic one-size-fits-all approach. Industry estimates suggest 75% of people on this planet will have a mobile in their hand by 2020. This is as big as it can get.
Medical data has just exploded manifold, thanks to storing and tracking of millions of data points related to each patient, and correlating with their demographics, geo-location and wearable biometric devices driven data feeds. Industry reports estimate the AI market for heath-care applications alone to reach $6.6 billion in the next three years, which obviously has a direct impact on the insurance sector.
Originated and supplied by billions of sources including IoT devices and advance sensors pump in data points every moment and everywhere, with more than 50 billion devices estimated to be a part of the internetconnected world by 2020. The data that is produced and stored up in the cloud, is estimated to cross 40 trillion GB in the next couple of years. Quite obviously, it has its key implications on all applications, including insurance. While the typical insurer’s decisions are driven by structured analysis for estimating losses and pricing risk, the complementing of this process with unstructured data driven actionable insights is already commonplace.
In the final analysis, what matters in the business of risk measurement is data-driven analytics. The larger the data surface and more accurate its veracity, the higher the quality of risk assessment. Not to forget, there are always malicious players looking to influence decisions with falsified or manipulated data. From another perspective, any compromise to the quality of data accuracy is likely to result in inappropriate pricing and risk modelling, and would immediately impact the very existence of the enterprise. This, ultimately, is the bedrock of a new-age insurance paradigm, and inevitably is a double-edged sword
Big data and cognitive analytics easily qualify to be the proverbial Siamese twins. While the trillions of data sources provide the basis for analysing patterns, cognitive analytics help in designing products that are intuitive, and predict the risk with much higher accuracy. Faster and accurate risk measurement allows for quicker decisions and personalised premiums. An interesting example of this can be the ability for a technology simulated prediction of how healthy a person would be and how would his/her appearance be 10 years from now, purely based on a smartphone clicked picture, juxtaposing the exercising, smoking, drinking and eating habits of the individual – the data for all being already available, and processed with cognitive analytics.
A key implication from a technology architecture standpoint would be the insurer’s agility to offer micro-services that are point solutions, on most occasions offered by new-age fintech service providers. Application needs are also likely to be modular, as APIs allow connecting of discrete service offerings without compromising on scalability.
Also, with insurance firms finding competitive benefits through connects with digital eco-system partners, the need to exchange both data and insights will only increase as we move along. Establishing an interactive mode of data flow among partners and pivoting of microservices would be a key area of focus for forward-looking insurance players. Another key and obvious implication would be the efficacy of the hardware infrastructure as speed and agility are the new name of the game. A cloud-first strategy would be quite a natural corollary for any insurer embracing big data, cognitive computing and predictive analytics.
From a customer’s standpoint, however, there are at least four distinct and evident changes that would be experienced, and this is only likely to accentuate further:
- Micro-products Insurance products would no longer be the standard off-the-shelf policies. Micro-products – meant to be short, sharp and specific – would make it fit for purpose based on the customer’s individual needs and the ability of the insurer to price such products accurately.
- Self-service Reliance on intermediaries will significantly reduce, with personal, customised products being now easily available at your fingertips, literally. This also means insurance will be directly bought more by the customers, rather than being pushed by the feet-onstreet brokers and agents.
- Automated decisions Underwriting would no longer be dependent on manual talent – artificial intelligence and predictive analytics would make the decisions quicker, easier and more transparent. Insurers who do not invest in automation would most certainly, and inevitably, soon be extinct.
- Cost effectiveness Finally, with reduced intermediation, assisted and automated decision-making, and having the ability to avail laser sharp micro-products, the cost structures of insurance are also bound to change, and change for the better. Customers have a good reason to smile.