The management of risk in financial services is about to be transformed. A recent McKinsey paper identified six structural trends that will reshape the function in the next decade. Five are familiar — they concern regulation, costs, customer expectations, analytics, and digitization — but one is less so: debiasing. That means using insights from psychology and behavioral economics, combined with advanced analytical methods, to take the bias out of risk decisions. The institutions pioneering this approach have seen tremendous benefits: for instance, banks adopting psychological interventions in consumer collections have achieved a 20 to 30 percent increase in the amount collected.

The interest in debiasing is growing as psychological research uncovers more and more subconscious effects that influence our decision making. Meanwhile, an explosion in data availability is providing businesses with an abundant flow of information for their analytic engines. Not all the data theoretically available can be exploited, for legal and privacy as well as technical reasons. But institutions still have a massive amount of underused data that they can mine, using an increasingly sophisticated array of advanced analytics techniques, to develop behavioral segmentations and predictive models. With these foundations in place, they can go on to design powerful interventions to tackle bias.

Take the example of a bank using a recursive neural network to extract customer profiles from credit-card transaction data. One profile that emerges is of a cardholder who clocks up dozens of low-value transactions at a convenience store every week. The customer’s habit of making multiple repeat visits at odd hours — seemingly for only one or two items at a time — suggests a lack of forward planning. Seen through a psychometric lens, the customer seems to be exhibiting poor impulse control and a lack of conscientiousness, traits that are likely to determine which types of decision bias this customer can be expected to manifest.

Compare this profile with that of a cardholder who completes one big supermarket transaction at more or less the same time every Friday evening, with little or no evidence of convenience-store shopping in between. That profile is indicative of a well-organized person who plans ahead. It’s likely that the first customer would benefit from financial products designed to help customers who struggle to meet their financial obligations — such as a credit card with weekly rather than monthly payment installments — whereas the second customer would probably have no need of them. And if, say, the bank is considering ways to motivate cardholders to pay off delinquent credit, its knowledge that customers with the first cardholder’s profile are likely to prioritize immediate consumption over clearing their debts will help it design suitable incentives to counter this tendency.

Analytics-driven psychological insights like these can be a spur to tremendous value creation. This article considers some of the most common biases in business decision making and looks in detail at three areas where debiasing can reap rich rewards: credit underwriting, consumer debt collection and asset management.

A quick guide to common biases

Biases are predispositions of a psychological, sociological or even physiological nature that can influence our decision making. They often operate subconsciously, outside the logical processes that we like to believe govern our decisions. They are frequently regarded as flaws, but this is both wrong and unfortunate. It’s wrong because biases are an inevitable side effect of the mechanics our brains need to achieve their astonishing speed and efficiency in making tens of thousands of decisions a day. And it’s unfortunate because the negative perception of biases leads us to believe we are immune to them — a bias in itself, known as overconfidence, exhibited by the 93 percent of US drivers who believe themselves to be among the nation’s top 50 percent.

Even if we accept that biases may influence our decisions, we might assume that successful organizations have developed processes to keep them in check. But experience indicates otherwise. For example, academic research has found that ego depletion materially affects the work of judges, doctors and crime investigators, and our own research has revealed how it affects credit officers’ decisions, manifesting itself in tangible business metrics such as credit approval rates. When financial institutions work to counter bias in judgmental underwriting — in small business credit, for example — they can typically cut credit losses by at least 25 percent and even as much as 57 percent in one case.

For lenders, an area particularly ripe for debiasing is debt collections, where biases can shape the behavior of collectors and customers alike. Consider how collectors handle calls with recalcitrant customers. Over the course of a call, they need to make numerous split-second decisions that expose them to the full gamut of biases, such as anchoring and over-optimism, as well as somatic effects and ego depletion. Whether they persist in trying to elicit a promise to pay or give up and move on to the next delinquent account may partly depend on the time of day. The effectiveness of collectors’ calls dwindles over the course of the working day as ego depletion sets in (Exhibit 1). The good news is that companies aware of this phenomenon can make adjustments in collectors’ working environment to help counter it.

And when it comes to customers with overdue accounts, leading financial institutions are harnessing a plethora of psychological insights to encourage payment. This often means making targeted interventions that increase customers’ motivation to pay, help those with low self-control to keep their commitments, and respect individuals’ need for agency (and thereby avoid triggering what psychologists call “reactance”). A credit-card provider could, for instance, present high-risk customers with a late-fee waiver or a gift card from a favorite shop that they would lose if they didn’t make a payment. Framing the offer as a loss for a payment missed, rather than a reward for a payment made, enlists the help of the loss aversion bias and can double the effectiveness of the offer.

To give a sense of what can be achieved when these techniques are applied in practice, let’s now examine what leading institutions have been doing to take bias out of credit underwriting and consumer debt collection. And looking beyond lending, the sidebar “Debiasing asset management” describes how firms in an adjacent industry uncovered bias in their investment decisions.

Commercial credit underwriting

Most credit officers possess a strong professional ethic and have honed their skills over years, if not decades. Yet evidence indicates they are just as susceptible as anyone else to decision bias.

One bank with poor performance in its commercial credit underwriting made a retrospective assessment of the predictive value of its judgmental credit ratings using Gini coefficient measures on a scale from zero (no predictive power) to 100 (perfect prediction). The analysis examined 20 dimensions stipulated by the bank’s credit policy, such as management quality and account conduct, and compared judgments made by credit officers with actual defaults observed over the following 12 months. One dimension (account conduct) stood out with a relatively high Gini of 45, but most dimensions had much lower scores (Exhibit 2). By way of comparison, comprehensive best-practice models for rating small businesses can achieve a Gini of 60–75.

In fact, half of the dimensions in the bank’s rating model achieved a Gini score of 7 or lower — little better than a roll of the dice — yet the bank was paying them just as much attention as it gave to dimensions with genuine predictive power. For instance, despite scoring a Gini of just 1 in back-testing, shareholder composition was usually discussed in depth in credit memos and relationship managers were even prompted to ask customers follow-up questions about it. Factoring in such irrelevant dimensions anchored credit officers’ overall rating in randomness, dragging it down to a Gini of just 22.

In order to debias its commercial underwriting, the bank had to separate the wheat from the chaff — a systematic process combining analytics with psychological insights. First, the bank replaced fuzzy concepts with carefully chosen sets of proxies for which more objective assessments could be developed.

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