Bank Data Analytics and Insights

Putting Bank Data to Work in New Ways

Looking for deeper insights? We can help you get more value out of your bank data.

Drawing on a combination of financial services experience and deep technical expertise in advanced bank data analytics, our data specialists can help you gain deeper insights into your customers’ behaviors, your sales and marketing efforts, and the efficiency of your operations. We can help you recognize likely events or challenges and suggest operational changes or other solutions to help you achieve a better outcome.

At Crowe Horwath LLP, our data science team is continually developing new machine learning and artificial intelligence (AI) solutions that use predictive and prescriptive analytics to address critical banking issues, including:

customer retention

360-degree view of customers and their relationship with the bank

  • Predictive attrition models to target high-value customers whose behaviors indicate possible churn
  • Customer sentiment from online sources and contact center that can predict behaviors of life events
  • Improved segmentation of customers based on dynamic behavior indicators
portfolio management

Profitability analysis, 360-degree view of branch performance and products

  • Predictive models that can increase customer attraction and increase account openings
  • Predictive models to assist with branch rationalization and planning
  • Prescriptive models to determine propensity to open account and make cross-sell recommendations
marketing and sales

Monitor customer activities on website, mobile app to view historical trends

  • Target the specific customers who will most likely respond to marketing campaigns
  • Determine which offers customers will find most valuable through increased channel optimization
  • Improve cross-sell and up-sell initiatives through better customer targeting
risk and compliance

Compliance with financial crime, stress-testing, and other regulations

  • Increased effectiveness of fraud detection through improved cognitive models
  • Improved efficiency and effectiveness of AML investigations through advanced network relationship generation and entity resolution
  • Predictive models for forecasting stress testing

Peer analysis and benchmarking against historical best-in-class performance

  • Scorecards and predictive models that use alternate sources and big data to improve approval rates for loans
  • Budgeting and forecasting models to optimize branch performance
  • Robotic process automation (RPA) to provide increased automation efficiency and reduced risk
Request for Proposal

Contact Us
Christopher J. Sifter
Managing Director