A decade ago, straight-through processing was a buzzword, and speed to market was critical. The progress that financial institutions have made in making almost all aspects of their transaction footprint digital has left little to leverage on the transaction side.
Today, while most organizations are busy revamping their policy administration systems, which were long ready to be replaced a decade ago, the companies that will be set apart are those 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 so that over time the premium revenues outstrip the claims experience. Historically, all the 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.
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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 have noticed that now Google can predict what you are searching and what you are looking for based on data it collects on your location, your emails, your past transaction etc. Over time, there will be a cognitive angle to the search capabilities exhibited by Google. If you apply the same rule of thumb to underwriting, insurance pricing and 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 system design can extend 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 toward machine learning and AI-assisted functions. The next wave would lead to a totally autonomous ecosystem.
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, pushing current roles into those that co-exist or radically change them.
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 underwriting and actuarial functions to be supplemented by machines.
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Insurance has traditionally followed and adopted what has been tried and tested in the banking space. For a change, there may be an opportunity for insurance carriers to take the lead 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.
