The BI (Big Issues) with BI (Business Intelligence)
You’d be forgiven if you thought business intelligence (BI) — along with its two close pals, data and analytics — were chief preoccupations for insurance companies. You’d be forgiven because, according to one study we’ve seen, between 2015 and 2020, somewhere in the neighborhood of 80 to 90 percent of property/casualty insurers considered BI and data analytics to be strategic initiatives. And you’d also be forgiven because Information Age published this:
Data analysis is one of the historical pillars of insurance. Actuaries have used mathematical models to predict property loss and damage for centuries. When they sell policies, insurers collect large data-sets about their customers that are updated when those customers make a claim. In recent years, as insurers have sought to become more relevant to their customers and more efficient, they have realised the strategic importance of their data investments. They want to harness data analytics to improve customer experience significantly, whilst cutting claims handling time and costs, and eliminating fraud.
You’d be forgiven … but you wouldn’t be correct.
According to Deloitte’s 2020 Insurance Outlook, “The vast majority of insurer IT spending still goes toward maintaining legacy systems.” So, the fact of the matter is that most insurers haven’t begun serious tactical initiatives to make the most of BI and data-analytics capabilities because they have other, more pressing concerns like the expectations of their agents, their policyholders, and their employees. And if insurers are saddled with legacy systems out-of-date enough to prevent them from meeting those expectations, BI and data analytics slide down the priority list a bit. And there are other considerations.
Most BI and data analytics applications are targeted at, best suited for, and most affordable for large insurers with huge volumes of data. But medium-sized and smaller insurers have affordable options that will at least let them relate claims to premiums, adjust loss-cost multipliers and rates, and weed out bad risks. But trying to layer capabilities like BI and data analytics on to legacy or poorly designed systems — and/or on to inadequate underlying architectures — adds technical challenges that may not be worth the risk and cost.
So, the big issues with BI and data analytics are antiquated systems, poorly designed systems, degrees of technical difficulty, and scarce resources.
Until those big issues are resolved, strategic BI and data analytics initiatives aren’t likely to become tactical, operational initiatives.
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