Sandbox Tool Aims to Help Credit Unions Build Analytics Models

Financial institutions of all sizes, including credit unions, are constantly trying to stay ahead of rapidly changing consumer behaviors but they face unique challenges including limited data sets, analytics tools and resources.

In an effort to help credit unions, fintechs and financial institutions of all sizes stay ahead of ever-evolving consumer behaviors by leveraging rich data sets and advanced analytics, Experian announced new configurations of its Ascend Analytical Sandbox. With access to a one-stop source for better decisioning, Experian project users from small to large size financial institutions can use the solution for model development, benchmarking, forecasting and much more.

In addition to thousands of scores and attributes, Experian explained the Ascend Analytical Sandbox offers users industry-standard analytics and data visualization tools like SAS, R Studio, Python, Hue and Tableau, all backed by a network of industry and support experts to drive the most answers and value out of their data and analytics.

Less than a year post-launch, Costa-Mesa, Calif.-based information services company said 15 of the top financial institutions globally use its solution. Experian’s Ascend Analytical Sandbox was the first environment built on the Experian Ascend Technology Platform. launched in 2018.

“Our innovations like the Ascend Analytical Sandbox and Experian Boost help transform the way businesses operate and consumer’s thrive in today’s society,” Alex Lintner, group president, Experian Consumer Information Services said. “We understand the unique challenges smaller financial institutions face, including limited data sets, analytics tools and resources. With the new customizable configurations of our Ascend Analytical Sandbox, financial institutions of all sizes can access world-class advanced analytics, AI and machine learning tools to make faster, more informed decisions.”

Experian’s Ascend Analytical Sandbox contains analytics tools such as R, Python, H2O, Tableau, SAS as well as access to SAS Viya via an exclusive agreement with Experian. Through the platform, data scientists and business intelligence teams can leverage up to 18 years’ worth of depersonalized credit and alternative data and analytics tools to answer questions, such as “what happened,” “why did it happen” and “what should I do next/?”

“This can now be a relevant solution at a price point that will be reachable for credit unions, regional banks, anyone really that wants to leverage the data and the analytical tools we have to do more advanced analytics,” Greg Wright, chief product officer for Experian’s consumer bureau.

The Ascend Analytical Sandbox viewable and sharable through interactive dashboards and customizable reports pulled in near real time, provides additional use cases including:

• The reject inferencing – refine models, scorecards and strategies by analyzing trades opened by previous applicants rejected or approved but not moving forward.
• Prescreen campaigns – design prescreen campaigns, evaluate results and improve strategies.
• Cross-sell – identify cross-sell opportunities for existing customers and identify how they may be working with other lenders.
• Collections strategies, stress testing and loss forecasting – build stronger models to identify customers that have ability and willingness to pay debts, stress test and forecast loss.
• Peer benchmarking and industry trends – compare current portfolio against peers and the industry.
• Recession planning – identify areas to adjust your portfolio to prepare for an economic downturn.

The secure hybrid-cloud environment allows users to combine their own data sets with Experian’s exclusive data assets, including credit, alternative, commercial, auto and more. From there, users can shape and test models across various phases of the lending cycle, including originations, prescreen, account management and collections, and put their models into production. Experian’s Ascend Analytical Sandbox also allows users to benchmark their portfolios against the industry, identify credit trends and explore new product opportunities.

“This was a unique solution in the marketplace, pulling together the right pieces of the puzzle to create the platform,” Wright said. He added, by bringing all of that together it gives credit unions and other community financial institutions a cutting edge, analytical solution. It also pulls in other data and tools like H2O.ai (creator of a leading open source machine learning and artificial intelligence platform). “We can preload those into the sandbox environment, so our clients can really see the full breadth of data that we bring.”

Wright noted, “Not only is there more data, but also it enables them to use more advanced analytical techniques. And we actually work with our clients to help them figure out how to do machine learning and advanced analytics and build models through our sandbox environment,” Wright added.  “What’s unique about this is it’s a full analytical environment where they can do anything from simple, model building and regression models to advanced analytics.”

Wright proposed credit unions and community banks may want to refine their own models. “a lot of clients will have either fairly simple or fairly complex targeting and response model approaches. And in this environment, you can take those same models and tweak them and change them and see how your prescreened audience could perform.”

In North America, OneMain Financial turned to Experian to improve its risk modeling and credit portfolio management capabilities with the Ascend Analytical Sandbox. According to OneMain Financial, the Ascend Analytical Sandbox shortened the process to less than two weeks from up to 180 days.