Converting credit leads to customers: How machine learning can unlock lending potential

Industry Insight

February 2020

Artificial intelligence and machine learning are having major impacts in the world of financial services. From chatbots to automating application processes, financial institutions are recognizing the benefits of cutting-edge tools.

These new tools can serve to deepen the relationship between a financial institution and its customers by providing personalized customer experiences. The lending process is crucial to a bank’s operations and involves many customer touchpoints. Due to ever growing number of options that customers have at their disposal, institutions that can provide a superior customer experience are at a distinct advantage.

Efma spoke with leading bankers who work in the lending space to better understand how they are implementing artificial intelligence and machine learning to eliminate processes, better source leads, and reduce fraud. Across the board, banks listed effective lead prioritization as their number one priority when it comes to the lending process. Yet, in their responses, the bankers admitted to limited machine learning implementation in their lead prioritization processes. While there is considerable push to be more sophisticated from top leadership, the speed of change is often slow.

It is clear that many of the methods being used are fairly simple and rudimentary. These vary from online lead origination to tracking customer behavior on bank websites. Banks recognize significant opportunities exist, but full-on adoption and buy-in remains elusive.

Said one executive, “I am always pushing our organization to be more sophisticated in understand our existing customer base and the dynamics of their financial situations. Instead of waiting for the customer in distress to come to the bank saying they need money, the bank should be prompting the customer by offering a readymade, tailored product. We should be helping the customer before they even realize they need help.”

Efma spoke with top executives from banks throughout Europe (UBS, Nagelmackers, Raiffeisen Bank Kosovo, BNP Paribas). As you will see in the coming pages, adoption of AI tools and methods varies across the banking landscape. As a result, there is significant opportunity for those players that choose to embrace the new tools and technologies on offer.

Keywords : AI/Robotics , Credit