Our team began by helping the client to rethink its lending process from scratch. A digital interface that delivered instant credit decisions to customers would need to be underpinned by completely new underwriting criteria and risk processes.
At the core of the new model was an automated analytics engine that drew on hundreds of data points about customers—from external as well as internal sources—to inform loan-approval decisions. The other key elements of the model were a zero-based redesign of processes and a smart digital work flow, connecting the elements needed for convenient customer journeys and credit decision-making. To provide instant technology delivery without disrupting the bank’s core IT processes, the team adopted a two-speed IT approach, building a layer of new functionality on top of the untouched legacy infrastructure and making maximum use of existing assets.
Initially, the analytics engine and digital work flow handled all credit applications and processed about 40 percent of them automatically from beginning to end, with 70 percent as the eventual goal. For these fully automated applications, “time to yes” was reduced from a window of 24 to 48 hours to just four minutes. If the analytics engine was unable to approve an application, it did not reject it outright. Instead, it referred it to credit officers, providing detailed analysis that helped them make a better-informed decision more quickly than before.
By using the engine to both approve and support credit decisions, the bank could manage much higher application volumes. The more cases the engine handled, the more robust its decisions became.
With much of their routine work automated, relationship managers and credit officers found they were handling more challenging cases. They were also equipped with better tools and data to help create richer and deeper relationships with their customers as they took on more of an advisory role.