“Software is eating the world”. It is a phrase that we often see written, but sometimes do not fully understand. More recently I read derivations of that phrase that posits that “analytics are disrupting the world”. Both phrases have a lot of truth. But why? Some of the major disruptions in the last 5 years can be attributed to analytics. Most companies that serve as an intermediary, such as Uber or AirBNB, with a business model of making consumer and supplier “connections” are driven by analytics. Pricing surges, routing optimizations, available rentals, available drivers, etc. are all algorithms to these “connection” businesses that are disrupting the world. It could be argued that analytics is their secret weapon.
It is normal for startups to try new and sometimes crazy & risky investments into new technologies like Hadoop and analytics. The trend is carrying over into traditional industries and established businesses as well. What are the analytics uses cases in industries like Financial Services (aka FSI)?
Established Analytics Plays in FSI
Two use cases naturally come to my mind when I think of “Analytics” and “Financial Services”; High Frequency Trading and Fraud are two traditional use cases that have long utilized analytics. Both are fairly well respected and written about with regard to their heavy use of analytics. I myself blogged recently (From Kinetic to Synthetic) on behalf of Equifax regarding the market trends in Synthetic Fraud. Beyond these obvious trends though, where are analytics impacting the Financial Services industry? What use cases are relevant and impacting the industry in 2016 and why?
The insurance industry has been experimenting with opt-in programs that monitor driving behavior for several years. Insurance companies have varying opinions of its usefulness, but it’s clear that driving behavior is (1) a heavy use of unstructured data and (2) a dramatic leap from the statistical based approach using financial data, actuarial tables, and statistics. Telematics is the name given to a set of opt-in programs around usage-based insurance / driver monitoring programs. Telematics use in insurance companies has fostered a belief that has long been used in other verticals like fraud that pins behavior down to an individual pattern instead of trying to predict broad swaths of patterns. To be more precise, Telematics is looking to derive a “behavior of one” vs a “generalized driving pattern for 1K individuals”. As to the change of why this is different from past insurance practices, we will draw a specific comparison between the two. Method One – historical actuarial tables of life expectancy along with demographic and financial data to denote risk vs. Method Two – how does ONE individual drive based upon real driving data as received from their car. Which might be more predictive about the expected rate of accidents is the question for analytics. While this is a gross over-simplification of the entire process, it is a radical shift of the types of data and the analytical methods of deriving value from the data available to the industry. Truly transformational.
The insurance industry has been experimenting with analytics based on past performance data. The industry has years of predictive information (i.e., claim reviews along with actual outcomes) based on past claims. By exploring this past performance data, Insurance companies are able to apply logistical regression algorithms to derive weighted scores. The derived scores are then being analyzed to determine a path forward. For example, if scores greater then 50 amounted to claims that are evaluated and then almost always paid by the insurer, then all scores above 50 should be immediately approved and paid. The inverse is also true that treatments can be quickly rejected as they are often not appealed or regularly turned down under review if appealed. The analytics of the actual present case was compared against previous outcomes of the corpus of past performance data to derive the most likely outcome of the case. The resulting business effect would be that the workforce that reviewed medical claims would only be given those files that needed to be worked. The result would be a better work force productivity. Labor Arbitrage with data and analytics being the disruptor of workforce trends.
Know Your Customer
Retail Banking has turned to analytics as they have focused on attracting and retaining their customers. After a large trend of acquisitions in the last decade, retail banks are working to integrate their various portfolios. In some cases, resolving down the identity of all their clients on all their accounts isn’t always as straight forward as it sounds. This is especially hard with dormant accounts that might have maiden names, mangled data attributes, or old addresses. The ultimate goal of co-locating all their customer data into an analytics environment is a customer 360. Customer 360 is mainly focused on gaining full insights around a customer. This can lead to upsell opportunities by understanding what a customer’s peer set and what products a similar demographic has a strong interest in. For example, if individuals of a given demographic typically subscribe to 3 of a company’s 5 products, an individual matching that demographic should be targeted for upsell on those additional products when they only subscribe to 1 product. This is using large swathes of data and companies own product adoptions to build upsell and marketing strategies for their own customers. If someone was a small business owner and personal consumer of the retail bank, the company may not have previously tied those accounts together. It gives the bank a whole new perspective on who its customer base really is.
Why are these trends interesting? In most of these cases above, people are familiar with certain portions of the story. The underlying why or what, might often get missed. It is important to not only understand the technology and capabilities involved with transformation, but also the underlying shift that is being caused. EMC has a long history of helping customers through those journeys and we look forward to helping even more clients face them.
Tags: analytics, EMC, Hadoop, source:etb