Many people credit the rise of predictive analytics to the technological advances of the last 50 years. However, The history of predictive analytics starts in 1689. Its true that record keeping standards, relational databases, faster CPUs, and even newer technologies such as Hadoop and MapReduce have made predictive analytics an accessible tool for decision making. However, the history of predictive analytics show that it has been used for centuries. One of the first applications of predictive analytics was in underwriting back when shipping and trade was primarily conducted traveling the seas. Lloyd’s of London, one of the first insurance and reinsurance markets ever established, was a catalyst for the dissemination of key information needed for underwriting. The name underwriting itself came from the Lloyd’s of London insurance market. Financial bankers that would accept the risk on a given sea voyage in exchange for a premium would write their names under the risk information that was written on a Lloyd’s slip created for this purpose.
Edward Lloyd had established the Lloyd’s coffee house in 1689, which became popular with sailors, merchants, and ship owners because he delivered reliable shipping news that assisted the community in discussing deals, including insurance. Eventually the place became so popular that after his death they carried on the arrangement and eventually formed a committee that became The Society of Lloyd’s. For the next two centuries, the Society of Lloyds would be primarily engaged in the dissemination of information across the industry and become the world’s leading market for specialist insurance.
Today underwriting is still the backbone of the insurance, banking, and real estate industries. However, access to new and better information has improved the underwriting process as well as given way to other types of predictive analysis, including customer retention, upselling, and cross selling applications. For example, a bank can take the same information it uses to underwrite loans, including capital, collateral, and credit history, as well as demographics, geographics, and psychographics from their CRM, and generate a model that can predict who has the most propensity to take on a new mortgage loan. So not only can you qualify who the best mortgage loan candidates are, you can also predict who is ready for mortgage loan or ready to refinance their current mortgage. This not only saves time with your underwriters, but also optimizes the efforts of your customer service and sales teams.
Although relational databases, faster CPUs, and new tools have paved the way to bring predictive analytics to the masses, its important to remember the history of predictive analytics. It is not a new science. However, its also important to remember that there is still a human element involved in the process. Like the sailors, merchants, and ship owners before, experts in your business must be tapped for their knowledge and experience to validate your predictive models, whether you’re underwriting risk or analyzing the propensity of your current customers to purchase. Without validating that the models reflect reality, we cannot trust them to make decisions.
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