Banking analytics and data insights

Crowe can help your bank with its specific data needs.

Put your banking analytics data to work in innovative ways

Crowe banking analytics specialists can help banks like yours gain deeper insights into customers’ behaviors, sales and marketing efforts, and efficiency of operations. The Crowe data science team is continually developing new machine learning and artificial intelligence (AI) solutions that use predictive and prescriptive banking analytics to address critical issues.

Predictive and prescriptive banking analytics for your business

Our data science team uses machine learning and AI to help banks improve their most critical business operations. We can help banks recognize likely events or challenges and suggest operational changes or other solutions to help you achieve a better outcome.

Customer insights

Our 360-degree views of customer-bank relationships provide:

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

Portfolio management

Profitability analysis and a 360-degree view of branch performance and products offer:

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

Marketing and sales

Website and mobile app customer activity tracking helps you:

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 to assist you with:

Increased effectiveness of fraud detection through improved cognitive models
Improved efficiency and effectiveness of anti-money laundering (AML) investigations through advanced network relationship generation and entity resolution
Predictive models for forecasting stress testing

Operations

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

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