fintech content Banking has become more convenient than ever, with mobile capabilities nearly universal. According to banking analyst Ron Shevlin, “As of May 2021, mobile banking penetration has grown to 95% of Gen Zers, 91% of Millennials, 85% of Gen Xers, 60% of Baby Boomers, and 27% of Seniors.”1

We can manage our accounts from anywhere, transfer money via the bank or credit union’s app, and make a deposit with a quick snap of a check. To most customers, mobile banking convenience is now the norm. In that case, where can banks and credit unions turn to create differentiated experiences?

This is where predictive analytics in banking can help. Financial institutions can use predictive analytics to understand customer behavior, retain customers, and attract new business.

See below for predictive analytics examples in banking as well as tips for developing and deploying predictive models in retail banking.

Predictive analytics can help address key questions in the banking industry

Predictive analytics uses all the available customer data a bank or credit union can gather to answer key business questions, such as:

  • How likely is this customer to accept a marketing offer?
  • What is the future lifetime value of a customer relationship?
  • What factors impact cost and margins?
  • How can we detect and prevent fraud?
  • How do we reduce churn and retain the most valuable customers?

You can use structured and unstructured datasets with predictive models

Traditionally, data capture and analysis for banking customer insights has relied on market research surveys. To gain more meaningful insights about a financial institution’s customer experience, predictive analytics enables businesses to leverage a broad 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

Unstructured data can improve predictive models

As an example of working with unstructured text data, Beyond the Arc worked with a top 5 U.S. bank to deploy predictive analytics in retail banking to track and address consumer pain points. The bank identified more than 200 emerging issues by analyzing unstructured data from customer emails, banker notes, survey responses, call center transcripts, and other text sources.

Based on predictions generated by machine learning models, the bank then developed customer experience strategies to make improvements to their online and mobile banking services, email and print communications, and other customer touch points. The work resulted in increased customer retention and customer satisfaction scores.

Predictive analytics can also provide retail banks and other businesses with a clear view into cross-sell and up-sell opportunities, better risk and complaint management, customer satisfaction trends, even ways to increase operational efficiencies. Decision makers can spend less time searching for information and taking action based on best guesses.

Making data-driven business decisions helps financial institutions deliver a more satisfying customer experience that helps increase retention and profitability. These insights can also give businesses an edge ahead of the competition—a crucial advantage in the rapidly changing banking industry.

1. Mobile Banking Adoption In The United States Has Skyrocketed (But So Have Fraud Concerns)

Image source: Shutterstock. Article updated 12/22/2022

Predictive analytics examples in banking

  • Customer segmentation: Using predictive analytics to identify customer segments based on behavior, not just demographics.
  • Fraud detection: Utilizing predictive analytics to detect suspicious transactions (such as potential account takeover) and prevent fraud.
  • Credit scoring: Utilizing predictive analytics to assess the creditworthiness of customers and make informed lending decisions.
  • Cross-sell and upsell: Utilizing predictive analytics to detect opportunities to offer existing customers new products and services.
  • Risk management: Utilizing predictive analytics to identify and predict potential risks based on anomalies in customer or employee behavior.
  • Loan origination: Utilizing predictive analytics to optimize loan origination processes.
  • Personalized marketing: Utilizing predictive analytics to deliver highly targeted marketing offers.
  • Customer churn: Utilizing predictive analytics to identify customers at risk of churning and proactively intervene.
  • Credit card spend behavior: Utilizing predictive analytics to better understand customer spending patterns and tailor rewards and marketing offers accordingly.

How Beyond the Arc can help with predictive analytics in the banking industry

Our team includes experts in data science and machine learning, passionate about making decisions and taking action based on data. With experience working with both large national banks and smaller community banks, they specialize in delivering actionable business insights in retail banking. 

Interested in learning more about how to deploy predictive analytics? Let’s start a conversation.