How Can Predictive Analytics Help Your Bank or Fintech Company? Posted on : Jan 26 - 2017

Predictive analytics encompasses a powerful set of methods that uses all the available data an organization can gather to answer key business questions. By enabling financial institutions to make data-driven business decisions, predictive analytics helps drive profit and increase efficiency. In the rapidly changing banking industry, customer insights can also fuel initiatives to improve customer satisfaction and loyalty. In this brief article, I’d like to outline a few new ways predictive analytics is being used in financial services.

Banks and credit unions struggle with the very real possibility that they are becoming a commodity, with little—if any—differentiation. In this environment, it is vital for them to understand their existing customer base. Predictive analytics, particularly clustering and segmentation, can enable them to see previously unidentifiable patterns of behavior. This gives those organizations a starting point. They can gain a better grasp of how customers use banking products, and where there may be unaddressed needs.

Fintech, the growing category of companies pioneering new financial technology, is using analytics as a core part of their offering. For example, personal financial management software tracks how a consumer or small business spends money. With predictive analytics, that fintech service can now offer highly tailored alerts. What was once just a dashboard or static report, is now supplemented by personalized advice on how to save money or invest more wisely. Fintechs providing advice-as-a-service are using machine learning and predictive analytics to deliver tangible value.

Another challenge that banks face is a slow erosion of customer satisfaction. The Consumer Financial Protection Bureau has received over 1 million consumer complaints in their 5-year history. There were 28,700 complaints received in August 2016 alone. Two-thirds of the complaints relate to debt collection, mortgage, and credit reporting. Looking in the rear-view mirror, banks and credit unions can assess the issues raised by their customers. But a proactive, thoughtful, approach backed by data and analytics can spotlight issues before they escalate to the CFPB.

As an example, one of our banking clients used predictive analytics to track customer pain points and identify more than 200 emerging customer experience issues. The models we built leveraged text analytics, allowing the bank to analyze unstructured data from emails, banker notes, survey responses, call center transcripts, and other text sources. The bank then developed a customer experience strategy to make improvements to their online and mobile banking services, email and print communications, and other customer touch points.

To gain more meaningful insights about a company’s customer experience, predictive analytics enables businesses to leverage a broader range of customer data, including:

Interactions – email and chat transcripts, call center notes, web click-streams, and in-person dialogues

Attitudes – opinions, preferences, needs, and desires gathered through survey results and social media

Descriptions – attributes, characteristics, self-declared information, and demographics

Behaviors — orders, transactions, payment history, and usage history

Historically, financial services companies used analytics primarily for underwriting and risk. In today’s rapidly evolving industry, predictive analytics is at the core of many of the innovations that are beginning to emerge. Source