Software will eat the world,” US investor Marc Andreessen said this famously in a Wall Street Journal article long ago. Of course, he is right. Software changed almost every aspect of industry and business forever.
Insurance sector is no exception and is also eaten by software a long time ago. Even in an economy of Bangladesh which has a very modest size of $150 billion, there are 31 insurance companies and all of them are using software in running their business.
The problem is even though these 31 insurance companies- which by the way experts concerned dubbed as too many players in a too small marketare using software, the quality and the efficiency of the software are needed to be put under scanner.
While the insurance software in many developed countries have used machine learning exclusively to bag more customers and to bring more companies under insurance network, insurance companies in Bangladesh still heavily relies on underpaid insurance agents in doing those for them.
That probably explains why-despite having 31 insurance companies- still less than one per cent of the country’s population is insured and the sector’s contribution to the economy is still below one per cent.
So, what basically machine learning can garner in Bangladesh’s insurance sector’s favor? To have a clear notion about that, we first need to understand about actuarial table.
Actuarial tables are a cornerstone of insurance and are a very basic form of machine learning. Take, for instance, mortality tables. These tables produce highly specific and shockingly accurate projections of how people born within a certain period will live. These projections don’t just materialize out of thin air. They are formed and refined by millions of data points and real life examples inputted into the tables and then evaluated on an aggregate level.
To overly-simplify: if 1,000 people died when they were 65, the insurance industry will project that out to the population. In this example, all of those data point ages were entered into software with one specific purpose: to crunch the numbers and predict at what age say Mr Jasim Uddin will probably die.
Think; how that will aid the life insurance company in deciding premium for certain insurance.
Data is the king
The point is, though insurance companies in Bangladesh are using software, they basically have been using those in the form of keeping transactional records and that usage of software making it just a mere repository for keeping insurance data, nothing else.
Besides, there are only a handful of insurance technology systems customized by the insurance companies in Bangladesh and they rely on the old way of doing software: input data, data gets chewed up, an answer gets spit out (leaving behind all sorts of valuable information). The old way of doing software was created to simply drive efficiency and complete workflows.
In reality, though, it is inefficient and leaves vast amounts of valuable data and information untouched because there is no machine learning component at its core to harvest or utilize that information for purposes other than the original intent.
Intelligent software with machine learning ability however is designed to do way more than that and fortunately local developers in Bangladesh are now being able to design such software. So how does this intelligent software make a difference?
Let’s put it simply-it takes variables, do the “math” and spit out results that you desire. So what results you desire depend on what you want at the first place. If you want your software to find the able car owners in Dhanmondi neighborhood who haven’t got their cars insured yet, then the software will bring you that list provided that you have given appropriate data as input.
There is no problem of data in Bangladesh as people don’t think about much in giving out their personal data. The concept of data privacy is still in its infancy in Bangladesh.
The insurance companies in Bangladesh have to understand that software is an efficient, electronic mouse trap; software is basically a systematic way to collect data and efficiently process the data for a specific, linear purpose.
Machine learning can change everything
The problem is, the insurance companies still feel jittery in investing in IT. This is because they fear that the true digital environment hasn’t started here in Bangladesh. Investing on machine learning might not yield the desired result.
Google is one of the best examples of creating a business model for transforming software usage to business insights through machine learning. Google created Gmail so that your email – the data that you type – could be turned into actionable insights. Google provides a free and convenient service and, as a happy (for Google) byproduct, gets to use a vast amount of your data, harvested from your emails and user habits.
The insurance industry should pay attention. Bangladeshi insurance companies house an overwhelming amount of raw data. Unfortunately, for many companies, it’s just stuck. Mr Anwar Ullah’s car insurance application resulted in car insurance, but what about other uses for the information on Anwar’s application? What if there are a thousand Anwar Ullah Car Insurance Applications stuck in a database somewhere, containing valuable information? Doesn’t anyone want to use that information to chart trends? What if all of that information could be used to predict and prevent fraud? Or to provide more cost-effective products?
This is the part where machine learning will eat software and the data contained in these applications.