Even before the pandemic, financial institutions had been investing in digital transformation and artificial intelligence (AI). Yet McKinsey finds that 70% of digital transformation programs fail to achieve their goals. In addition, successful deployment of AI is less than 10% in many organizations, according to the International Institute for Analytics.
Last year we partnered with Harvard Business Review Analytic Services to survey senior leaders to examine the most beneficial use cases for AI and the challenges preventing businesses from capitalizing on their AI investments. One key finding from the report was that operational decisions are an overlooked area and missed opportunity. Jim Marous, Co-Publisher of The Financial Brand, reiterated this finding in arguing that “digital transformation cannot occur without rethinking of the back-office processes” including how to streamline operations and integrate new data sources.
What Spilled Milk Can Teach Financial Institutions
After months of living with COVID-19, the need for financial institutions and other businesses to reexamine back-end operations is clearer than ever. A non-banking example shows why.
Early into the enactment of shelter-in-place policies, many consumers were confused and upset about not being able to find milk at the grocery store yet hearing that milk was being dumped by the truckload. The mismatch between supply and demand was driven by there being two distinct supply chains within the dairy industry — one for food service and one for retail. Nationwide closures of restaurants, schools and businesses drastically shifted the demand to retail without the dairy industry able to easily divert one supply chain to the other.
What this challenge revealed is that agility is not inherent in efficiency. While financial institutions don’t have to worry about supply chains, they are as prone to falling for the fallacy that efficiency is king.
Automation Is Not the Same as Optimization
In consumer lending and financing, fintechs have reimagined the branch experience for a digital environment. Processing an application and producing a credit decision used to take hours if not days of manual paperwork, but now only takes seconds — thanks to automation.
If you look under the hood of many financial institutions, regardless of whether they’re a traditional bank, you’ll see the same kind of automation — namely a set of rules that have been hard-coded into the financial institution’s business application. Typically the rules involve pulling a credit score, or pulling external data from various sources to generate a score, and applying a threshold for decisioning (e.g. “approve applicants with score > X”). This setup is fairly straightforward and when implemented is highly efficient. However, as with dairy supply chains, just because an automated system works well today doesn’t necessarily mean it will work well tomorrow.
Predicting the Future Based on the Past Is Not Foolproof
In order to understand the weakness of common automated credit decisioning systems, we must take a closer look at credit scores. Whether purchased through a credit bureau or developed internally, credit scores are a reflection of the past. They take into consideration the credit history, characteristics and behaviors of applicants to predict their probability of defaulting on the loan, credit, or financing that they’re applying for.
When economic conditions are stable, relying on a credit score to make credit decisions is sensible, and automating the decisioning process not only enables a financial institution to gain cost efficiencies but also drives increased revenue as more applications can be processed in a shorter period of time. While there will be variability from individual to individual, on the whole, how consumers behaved last month is most likely how they will behave next month.
However, when businesses, schools and institutions close en masse as we have seen with the pandemic, today looks different from yesterday and tomorrow is uncertain. Therefore, the data used to power credit scorecards and the scorecards themselves are no longer reliable.
As a result, many financial institutions have scaled back on new customer acquisition and focused on managing their existing portfolio while data scientists are working behind the scenes to recalibrate models. Even so, the challenge that many financial institutions face is in implementation. Their systems were designed to automate and support a specific credit decision process. Integrating new data sources, swapping out scorecards and modifying business rules is as “straightforward” and as “easy” as pivoting the dairy wholesale supply chain to retail.
Disruption Can Come from Anywhere, Anytime … Will You Be Ready?
Few people could have foreseen that 2020 would be the year of COVID-19 nor predicted the global economic shutdown that followed. Yet in the last 100 years we’ve had four flu pandemics, which suggests that another flu pandemic is likely. And while financial institutions can’t predict when the next pandemic will be, they can prepare by reengineering systems and infrastructure for efficiency and flexibility so that they can respond quickly to disruption.
Earlier this year, one of our healthcare clients that offers in-house financing options to their patients realized that one of the data sources they were using in their patient credit risk assessment was going to significantly increase in price. Cutting costs became an imperative as COVID-19 rolled around and patient appointments were delayed. Because their credit decision flow is deployed on Enova Decisions Cloud, our decision management platform, we were able to work with them to not only test alternative data sources but also modify the scorecard, integrate the winning data source and go to production in a few weeks — in time for them to start seeing their patients again.
Disruption can come from anywhere. Banks and credit unions must be able to adapt to survive and overcome the challenges that are ahead. According to Accenture CEO Julie Sweet, businesses can’t just reduce costs but must “accelerate to new levels of digital performance” and “pivot to new growth opportunities.”
When evaluating what new systems and technologies to build or buy and new processes to develop, efficiency and agility must be top-of-mind. This is why we are conducting a survey with The Financial Brand to examine how financial institutions have been navigating COVID-19. The survey will explore the challenges, opportunities and what’s next for financial institutions.
This sponsored post was brought to you by Enova Decisions.
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