In Europe and North America, market-based credit-risk indicators suggest a robust “repayment recovery” is underway, with models that gauge default possibilities nearing pre-pandemic levels as of mid-June 2021, and signs of smooth sailing well into 2022.
But credit-rating agency Standard and Poor’s warns that conventional risk-assessment metrics may not be adequate for particular market segments. The reason? Measurements of suitability for consumer and B2B lending have lost much of their predictive power for businesses and individuals impacted by pandemic-related work stoppages. This makes it hard to distinguish temporary setbacks from the advent of new normals for would-be borrowers in some industrial sectors.
The answer to this seemingly intractable problem? Automated credit-risk management, which can process more inputs and render more decisions faster and more accurately than ever before.
“Credit risk refers to the chance of loss from a borrower’s failure to repay debt,” says Dmitry Voronenko, co-founder and CEO of TurnKey Lender, a top lending software provider. “In turn, credit-risk management refers to measures taken to mitigate losses by understanding the adequacy of a bank’s capital and loan loss reserves at any given time as well as measures taken in credit ‘decisioning’ and loan origination to make applicants’ financial situations more transparent to lenders.”
Learn about TurnKey Lender’s Decision Engine in detail to outperform any and all competitors and reduce credit risk.
Credit-risk controls impacted by internal and external pressures on lenders
Automated, or digitally enabled, credit-risk management is a form of gatekeeping that incorporates big-data inputs and artificial intelligence to help lenders make better decisions faster than ever before. Additionally, it helps lenders:
- Tap into unbanked and underbanked consumers — 7.7% and 17.9% of US adults respectively, according to the Federal Reserve
- Achieve automatic compliance around anti-money-laundering and know-your-customer regulations
And while the coronavirus pandemic quickened the pace of digital adoption for the sake of social distancing, the shift to automated credit-risk management was underway before Covid-19 quickened the pace of digital adoption for the sake of social distancing.
“External and internal pressures are requiring banks to reevaluate the cost and sustainability of their risk-management models and processes,” McKinsey says in a 2016 report called “The value in digitally transforming credit risk management.” The pressure came from regulators, emerging fintech competitors, company-stock holders, and the banks’ own customers, the report adds.
Due to more stringent capital requirements in the wake of the 2008 Financial Crisis, higher fines for noncompliance, and lagging cost efficiency, the share of risk and compliance in “total banking costs” went from about 10% in 2012 to a projected 15% for 2017, McKinsey reported in 2016. “This puts sustained pressure on risk management, as banks are finding it increasingly difficult to mitigate risk through incremental improvements in risk-management processes,” according to the consultancy.
Another trend changing the face of lending centers on evolving external and internal expectations.
Externally, customers want mobile and digital solutions. The global digital lending market, valued at $4.87 billion in 2020, is expected to expand at a compound annual growth rate of 24.0% from 2021 to 2028, says Grand View Research. Already by 2018,73% of consumers were using online banking channels at least once a month, according to Deloitte.
High indebtedness plus systemic disruption calls for better risk controls
Meanwhile, executives and business strategists within lending organizations have come to expect timely and accurate credit-portfolio reports to help them spot emerging troubles, improve efficiencies, calibrate marketing initiatives, and inform pricing.
McKinsey’s prescription is short and sweet. “Banks need to digitalize their credit processes,” the company says. “Lending continues to be a key source of revenue across the retail, small and medium-size enterprise, and corporate segments.”
While most of McKinsey’s assessment still holds water, digitalized credit-risk management has emerged as a must-have not just for banks, but also for a growing array of non-bank organizations — from retailers to capital-gear providers, medical practices, and nonprofits — that seek to bypass the middleman and provide white-label, software-based lending and loan management directly to customers. And this trend was well in evidence when the pandemic took hold.
Pre-Covid, lenders had become alarmed about rising debt levels in the US — $14.1 trillion in February 2020, spurred mainly by mortgages and credit cards. Now, with households under pressure from lost wages and other pandemic-related financial hardships, lenders are monitoring and identifying would-be borrowers who are under financial strain in their due diligence.
Fortunately, credit-risk-management tools are more robust than ever
“At the same time, however, lenders equipped with the right lending technology can now look to alternative credit scoring to get more holistic views of applicants,” according to lending-tech expert Voronenko. “In fact, lenders can use alternative scoring to safely extend credit where — using only traditional means derived from applications and credit-bureau reports — such probing wasn’t feasible before.”
With artificial intelligence shedding light on otherwise impenetrable data sets to include factors established as behavioral finance “tells,” lenders can compare permission-based inputs around spending and bill-pay habits to find low-risk applicants among some that would be considered high-risk using only traditional scoring methods.
Companies eager to leverage advances in integrated lending software to establish their own credit facilities may be familiar with traditional and other third-party lenders, but a close comparison of old-school processes with the existing state of the art can be impactful.
Decision time
- Traditional underwriters can take up to nine days to collect and analyze all relevant data and make a final crediting decision.
- With advanced, cloud-based lending software, decisions can be made — at the barest minimum — twice as fast. Often decisions can be rendered in a matter of minutes or in many cases instantly once scoring and decisioning are finetuned.
Scalability
- Using manual and other traditional procedures, it can be hard to scale due to operational expenses that grow as new customers are onboarded.
- With advanced and fully-supported lending software, risk scores and loan decisions are automatic — virtually “hands-free” from a staffing perspective.
Human error
- Everybody makes mistakes. But in the realm of risk management, mistakes can impede revenue flow and hurt your brand.
- Automation — especially when it’s as comprehensive as the leading lending-tech vendors — makes human error much less common and much less costly. Underwriters can review applicant information in a workspace designed to set such errors in sharp relief and suggest quick and easy remedies.
In-person requirements
- In old-school configurations, would-be borrowers have to show up in person, wait in line, answer questions, and generally waste time.
- Scorecards linked to automated risk-management systems quickly reject low-quality applications, saving both sides a great deal of time and trouble.
“The pandemic hasn’t triggered the need for automated credit-risk controls, not in isolation,” says TurnKey Lender’s Voronenko. “But it is showing — and with more clarity by the day — that new risk-assessment tools, drawing on functionalities as diverse as machine learning, dynamic workflow analysis, and advanced software integration, enable lenders to make better, and sometimes more inclusive, decisions.”