Guest blog by Christopher Fernandes, Business Architecture / Strategy Director.
A decade ago straight through processing was a buzz word and speed to market was critical. The progress financial institutions have made in moving almost all aspects of their transaction foot print digital has left little to leverage on the transaction side.
In today’s day and time while most organizations are busy revamping their Policy administration systems which were long ready to be replaced a decade ago, what will set companies apart will be the organizations that start considering Machine Learning and Artificial intelligence(AI) for their core systems.
If you look at the fundamentals of any kind of insurance, at the core, insurance offerings are about risk pooling and the ability of the insurer to price products in a manner such that over time the premium revenues outstrip the claims experience. In every type of insurance product the claims experience influencing the pricing and risk aggregation decision making done by the insurer. Historically all this analysis has been done by people and rightly so as we lived in a world that was not connected and human intervention to analyze outside factors was critical.
Fast forward to current time, all the data is on some kind of digital medium and more often than not connected and accessible. What is missing is the Machine Learning and Artificial Intelligence integration into the different facets of the insurance life cycle and the software platforms that are used to manage and maintain the data . The amount of data that needs to be analyzed and the patterns that are needed to be determined are so humongous that relying on data analysis by a person alone may not be the best approach.
If you use google often you would have noticed that now google can predict to certain level of accuracy what you are searching and what you are looking for based on data it collects on your location, your emails, you past transaction etc. Over time there will be a cognitive angle to the search capabilities exhibited by google. If you apply the same thumb rule to underwriting, insurance pricing , risk aggregation, why would we not want to leverage machine learning in a similar manner. For this to happen we need to start building software systems with not just automation in mind but also a consideration of how can system design extend the realms of machine learning. If the dots are connected and the data patterns understood and logic applied there are certain decision making aspects that can move away from people to machines and over time evolve to largely autonomous ecosystem.
What will differentiate the Market leaders from the laggards is investment in this aspect. These changes will come in the next decade or maybe even sooner and the Underwriting and Actuarial aspects will lean to machine learning and AI assisted functions and the next wave would lead to a totally autonomous eco system.
The picture simplistically highlights the possibilities of embedding Machine learning in the software ecosystem that we see in today’s insurance landscape. This is a generalized view agnostic of the domain or line of business. Insurance carriers would need to start thinking out of the box to translate this into software platforms of the future , enhancing changing and pushing current roles into those that co exist and or radically change them as we know them today.
So before we set the drones to fly and change the commercial insurance ecosystem, Machine learning and AI need to be adopted into mainstream core software platforms. The emerging market in the foreseeable future will be opened to the players that will NOT be consumed with dev-ops and pushing the realms of delivery automation but by those firms investing in infusing Machine learning and artificial intelligence into core platforms enabling UW and actuarial functions to be supplemented by machines.
Insurance has traditionally followed and adopted what has been tried and tested in the banking space. For a change this may be an opportunity for insurance carriers to take the leads and beat the banks and other financial institutions to set free the machines and change the way products are conceived and priced and premiums calculated.
Disclaimer:- The views expressed are based on independent research and do not represent a point of view by my current or past employer(s).
Originally posted here.
About the author:
Christopher Fernandes has worked in insurance companies managing business operations as part of start up operations and subsequently moved to technology consulting helping insurance & health care companies improve process controls and innovate business operations.
