Last Tuesday, FICO’s Matt Stanley and Tom Dehler presented an overview of best practices for the use of analytics in banking to help organizations respond to the challenge of the Covid-19 crisis. This session was the first breakout session in our month-long virtual event entitled Building Resiliency—Adapting to the Challenges of Today.
In this new age where face-to-face interaction is severely limited and the view into consumer credit risk can be cloudy, financial institutions need accurate analytic insights across the customer lifecycle in order to predict future behavior more than ever.
This is especially true for community banks and credit unions that often have less sophisticated analytics in place today when compared to the sophisticated Machine Learning and AI solutions used at the largest banks.
Most would agree that increasing the use of advanced analytics for highly accurate risk decisions makes perfect sense. The problem is that these analytics solutions can be expensive, require valuable IT resources or non-existent data science resources, and take six or more months to design, test and implement. We currently don’t have that kind of time.
In real-time, financial institutions are furiously working to formulate new policies and procedures to deal with the crisis, centered on getting a handle on their risk profile and loss provisions, while providing new levels of customer service and better account management. This requires rethinking customer treatment strategies and outcomes, especially for areas like collections. Meanwhile, the importance of shifting to digital customer experiences has accelerated dramatically as call center staff have been required to work from home, and in-person customer service is likely to be more limited going forward.
To succeed in this new digital age, one of the most straightforward ways to add intelligence to existing systems is through the use of predictive scores. Custom scores compliment standard scores (such as the FICO® Score) to enable organizations to better manage their risk, create new revenue opportunities and personalize their offerings to create a better customer experience.
Custom scores and predictive analytics have benefits across the entire customer lifecycle, including:
- Marketing: Drive new revenue and optimize your marketing spend.
- Origination: Better gauge risk exposure and reduce your “bad rate.”
- Account Management: Increase profitability and customer satisfaction.
- Collections: Do more with limited staff and reduce your charge off amounts.
- Fraud: Reduce application fraud losses without negatively impacting the customer experience.
Community banks and credit unions need cost effective, fast time-to-value solutions so they can compete effectively with big banks. Similar solutions can also be used within larger banks to accelerate analytic maturity, in particular department or line of business that may be earlier in its analytic maturity and need a solution fast.
How can you be successful in this new world? This session shared four best practices and detailed two case studies on how smaller community lending organizations are pivoting to be successful in this new environment. Please check out the full session to learn more about these best practices than can help your organization be successful in this challenging environment.