The Original Practitioners of Data Science
May 25, 2016 by Anne-Frances Hutchinson
Writing in his blog, Applied AI’s Michael Crawford postulates that insurers are the original practitioners of data science. Citing Edmond Halley’s analytical work in the 1690s as the mathematical cornerstone that would lead to the development of sound actuarial principles in the 1700s, Crawford notes that the basic techniques used to calculate premiums and measure a company’s fitness, while computerized, are still in use today. That’s an enlightening perspective -- even without cobbling together a link between comet spotting and the very first actuarial tables.
Most life insurance companies have tremendous amounts of stored data to parse, analyze and transform; in theory, machine learning can make those daunting tasks child’s play. Yet, a risk-averse industry culture is keeping a good majority of insurers firmly rooted in the speculation stage, watching and waiting for technological concepts to be proved, and proved again.
In an attempt to better understand life insurers’ extreme caution when it comes to this game-changing technology, KPMG’s Gary Richardson pardons the industry for keeping their enthusiasm under wraps. “Truth be told,” he writes, (machine learning) almost seems like something out of a 1980s sci-fi movie: Computers learn from human mistakes and adapt to become smarter, more efficient and more predictable than their human creators.”
From cutting claims processing time to providing a host of competitive advantages, there’s no doubt that machine learning algorithms can improve insurance business processes. Richardson uncovers an understandable motivation for insurers to resist this aspect of progress: data hoarding. “Until recently, common wisdom within the business world suggested that those who held the information also held the power,” he writes. “Today, many organizations are starting to realize that it is actually those who share the information who have the most power. As a result, many organizations are now keenly focused on moving toward a “data-driven” culture that rewards information sharing and collaboration and discourages hoarding.”
The life insurance industry is built on caution, and, despite cries from analysts that their traditional slow pace is risky, what we are seeing is a process at work. Richardson warns that tech adoption shouldn’t be viewed as an arms race. “The winners will probably not be the organizations with the most data, nor will they likely be the ones that spent the most money on technology. Rather, they will be the ones that took a measured and scientific approach to building their machine learning capabilities and capacities and – over time – found new ways to incorporate machine learning into ever-more aspects of their business.”
Richardson suggests that insurers develop pilot programs in which machine learning algorithms can be applied in a risk-free environment. Algorithms can be tested thoroughly and improved, and give workers an opportunity to understand and get comfortable with machine learning. “Only once the proof of concept has been thoroughly tested and potential applications are understood should business leaders start to think about developing the business case for industrialization (which, to succeed in the long term, must include appropriate frameworks for the governance, monitoring and management of the system).”
Ultimately, life insurers must decide how to derive the greatest value from machine learning, and they will. The industry arose from heavy-duty technological developments that were as enthralling, mysterious and daunting in their time as data science seems today. At Captricity, we are on the cutting edge of technological innovation, helping insurers of all sizes bring value to their organizations. Watch our latest video for an introduction to our guided machine learning processes.