The Future of Analytic Applications in Financial Services

Advanced analytics have been a critical tool in financial services for decades, well before our industry’s recent focus on the term ‘big data’. Leveraging credit risk models, fraud detection heuristics, risk simulations and automated trading algorithms have long been essential for any financial institution to remain competitive.

Having worked for software vendors who provide analytic applications to financial services for over two decades, I often think about the impact the recent advances in analytic technology will have on our industry. My definition of an analytic application is a system that leverages data, logic and predictive analytics and applies them to automate a process or service. Examples in financial services include credit origination platforms which enable customers to get a loan and portfolio allocation applications (i.e. robo-advisors) which provide automated investment advice.

Hundreds of billions have been spent on big data and analytics technology in 2016. Many in the financial services industry question whether we have seen the return we expected from these investments. I believe we have not… yet. I am convinced that the industry is on the verge of turning the corner, and there will be an explosion of value created as we learn how to transform what we’ve learned (insights) into ways to better run the business (actions). The secret to this transformation is improving how we deliver analytic applications.

In this blog I will explore five ways in which analytic applications will evolve over the next few years as advances in analytic technology becomes an integral part of the solutions we deliver. Many of these predictions are already happening in thought leading companies, but as these technologies become ubiquitous and available to all companies we’ll see a significant positive shift in the financial services customer experience.


1. Organizations will begin to focus on decisions over analytics

There’s been a lot of focus on the math side of analytics. Using historic data to predict future outcomes is incredibly powerful, but predictors provide only one of many data points required to make the right decisions. In lending, for example, the propensity for a borrower to pay back a loan (often embodied in the FICO score) is a key indicator of the risk for a loan, but as we learned in the mortgage crisis almost a decade ago, that’s not enough information to make an intelligent decision. The right lending decision involves looking at anticipated cash flows for the applicant as well as weighing the profitability of a customer’s lifetime relationship with the institution.

I believe the industry’s focus on predicting outcomes will shift more towards taking intelligent action and we will leverage predictors to automate the decisions that impact consumer experience and company performance. The end result will be a more frictionless experience in financial services with more intelligent, real-time answers at the customer touch-point.

2. Shared data services will level the analytic playing field

It used to be that only the big players had the data needed to create analytic leverage. With the explosion of consortium based data platforms, aggregators and entrepreneurial companies mining publicly available social data, that dynamic is changing. It is now possible to create analytic applications that leverage commercially available data to mitigate risk and optimize performance without internal historic data, and there are a number of FinTech players doing just that. The good news is that this democratization of information will also be a boon for established financial services organizations as they begin to leverage more facets of data about their customers to create a more intimate, profitable and loyal consumer relationships.

The use of online data vendors will continue to expand as financial services organizations strive to get a more complete picture of their customer and their risk profile. To support this, data modeling and metadata technology in analytic applications will evolve to allow data sources to be included in analytics and decisioning without coding changes, as easily as you can pull data into a spreadsheet.

3. Customers will demand frictionless and low risk financial services

A large portion of the $20B+ investments made in FinTech companies in 2016 has been focused on providing a frictionless consumer experience. Getting investment advice, securing a loan, making payments and personal banking are all at various stages in their transformation towards becoming on-demand and on your phone. This is a natural and expected trend as consumers demand the same experience they’ve come to expect with shopping and getting a ride. What’s different in financial services is that the products are more complicated and the cost of getting it wrong is more significant. As a result, customers will begin to expect that the applications they use will ‘cover them’ from a risk perspective and the companies that win will be those who can build that confidence with the consumer.

I predict that there will be increased spending on analytic applications that transparently manage customer risk and guide them in their financial transactions. This will require applications that are more intelligent and have the ability to react to new data that impacts the customer in real-time.

4. FIs will develop a unified and accurate view of their customer

We have come to expect that online retailers can do a good job of anticipating products that interest us and allowing us to re-order items with a click. Unfortunately, this same level of customer service has long escaped most financial institutions. The reason is that customer data in financial services is complex. Getting the system that booked your mortgage to talk seamlessly with the tool your investment advisor uses to manage your portfolio has been historically non-trivial.

Advances in analytic technology is bridging this gap. The next generation of analytic applications will be much more aware of a unified customer view. Underlying this evolution are tools that can discover relationships in data (ontologies) and create perspectives on complex data without forcing applications to transform it. These technologies were developed to ease the extraction of data from heterogeneous data sources for building analytics, but are now also being leveraged to enable smarter real-time applications.

5. Custom applications created from best of breed services will displace vendor software

This is perhaps a more controversial prediction, but I am convinced that a natural outcome of the other four trends will be a move back towards custom analytic application. Experience has taught me that it’s hard for software vendors to develop out-of-box applications that meet the unique needs of each customer. We would often promise a standard solution, only to require hundreds of thousands or millions of dollars in configuration to deploy the software, often limiting flexibility in the process. Fortunately, the same technologies that are driving innovation in advanced analytics are also making it easier for financial institutions to assemble custom applications that are tailored to their business requirements without significant software development cycles.

Entrepreneurial FinTechs have been showing the way, with many bypassing traditional software vendors due to high cost and lack of flexibility. Instead, these innovators are rapidly creating custom solutions by leveraging online data services, analytic application platforms and powerful user interface frameworks, often with very small teams. As a result, they are able to deliver more tailored products quickly and at a lower cost than established players.




As with any technology transformation, there will be winners and losers amongst both the disruptors and the established players. The resulting focus on more intimate customer relationships, frictionless user experiences and improved analytic integrity will advance the financial services industry in a way we have not seen since the widespread adoption of computers in the 1960s. It has been encouraging to see a tremendous collaboration between new FinTech players and veteran financial services organizations through innovation labs, meetups and incubators and I am more bullish on the future of financial services than I have been through most of my career.


About the author:

Tom Tobin has been developing commercial software products for the financial services industry for over 25 years and is now the founder of Modelshop, a platform that enables financial services companies to rapidly build analytic applications based on their unique requirements.

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