The Power of Data-Driven Decisions in Lending

Underwriting and credit approval decisioning are two of the most important and difficult responsibilities for financial institutions. Important because the ability of individuals and small businesses to access capital is essential to the growth of the global economy. Difficult because every loan a bank grants exposes it to the risk inherent in lending.

After the 2008 global financial crisis, banks were more cautious about lending, preferring to limit their risk and their exposure. However, thanks to new technologies that make data more robust and accessible than ever before, financial institutions can now make better, smarter, and safer decisions.

In the past, credit decisions were largely based on siloed data points. A subjective process such as this would be risky in any industry, but it’s especially dangerous in financial services. Andrew McAfee, a principal research scientist at MIT, agrees. He studies how digital technologies are changing business, the economy, and society, and has written frequently about the idea of “HiPPOs”—i.e., Highest-Paid Persons’ Opinions. “HiPPOs,” he writes, “might be good for some things, but their crystal balls just don’t work very well. The soulless output of a data-driven, mechanistic algorithm is demonstrably and significantly better, in domain after domain. So HiPPOs should become an endangered species.”

While I wouldn’t go so far as to call any aspect of banking “soulless,” I agree with McAfee about the power of data analytics. A banker could be considered a HiPPO based on his or her power, prestige, and experience. Rather than root for extinction and eliminate intuition, we should instead strive to complement these soft skills with hard tools such as technology and data.  

One way financial institutions can accomplish this is by housing their loan origination system, underwriting, and portfolio management in one place, so they can effectively understand and analyze their portfolios and disseminate data to the front lines. How does this work in real life? Imagine you’re a relationship manager pricing a loan. As you work, you have customer profitability, strategic industry data points, probability of default, loss given fault, deposit analytics, and likelihood to prepay at your fingertips. This gives you the ability to make a strategic, data-driven decision that aligns with and supports your institution’s goals. 

While most financial institutions leverage some level of loan portfolio data and access to multidimensional analytics, the ability to analyze and make use of that data for actionable insights is not yet the norm in banking today.  

To take full advantage of these tools, financial institutions must standardize their data-gathering practices across all units of their business to promote reliability of data. At the same time, they need to ensure that their customers have a positive experience with the bank. The need for data, while important, should not be gathered at the expense of the customer. Data collection must be efficient and painless, while also complying with security and privacy regulations.

Every financial institution is focused on three things: profitability, soundness, and growth. By providing actionable insights within the loan origination workflow and providing a deeper understanding of customer profitability, loan loss forecasts, and application analytics, financial institutions will have the right information at the right time, enabling them to make better business and risk decisions while offering their customers a superior experience.

By Emily Bogan, Vice President of Product Enablement and Go To Market, nCino

nCino is a Gold Sponsor and exhibitor this year at RMA’s Annual Risk Management Conference, October 27–29, and will be presenting this topic at one of our conference breakout sessions. To hear more from Emily on this topic, you can register for the conference here.

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