The importance of Alternative credit data for institutional lenders in the wake of coronavirus
As more Americans join the ranks of nontraditional employment status, lenders are struggling to find metrics for new loan approvals. Alternative data such as ‘bill payment records’ are opening new doors.
In this article we will discuss:
- Post-pandemic economic recovery and nontraditional employment
- Limited loan access proves to be a major roadblock
- How alternative data can drive change
Post-pandemic economic recovery and nontraditional employment
The recent pandemic has thrown a large majority of the world into economic recession led by a slump in consumer spending, a decline in retail, hospitality, and leisure activities which has led to unusually high unemployment rates.
In order to fuel economic recovery individuals and businesses will need credit lines, loans and other lending-based products. Governments’ stimulus packages, if and when they come through, will help. But real recovery will only be achieved when banks and financial institutions can lend with confidence.
This last point is a big issue as many Americans are currently unemployed, or working under nontraditional employment circumstances. Individuals who do not have salaries may also include:
- Self-employed individuals
- Independent contractors
- Temporary replacements (‘temps’)
People who are dismissive of this segment of the population should think again as according to a survey conducted by Freelancing in America some 57 million Americans do some form of freelance work, producing $1 trillion in Gross Domestic Product (GDP).
Source: Freelancing in America
Limited loan access proves to be a major roadblock
The major foreseeable challenge for financial recovery is that many nontraditional workers, even if they have a very high income are not eligible for loans. Loans which have laid the foundations for personal and business prosperity for generations.
- Loans for further education
- Lines of credit for business development and expansion
Banks tend to be very weary of individuals who do not have any ‘fixed income’ sources (99.9% of the time this means salaried employees). Historically banks have been very insistent on risk aversion by using traditional data sets to assess a borrower’s capacity for repaying loans. These include:
- Credit scores (Such as FICO – Fair, Isaac and Company Credit Scoring Services)
- Income-debt ratio (‘income’ is almost exclusively defined as ‘salaried wages’, excluding large swaths of the population from eligibility)
- Credit history (often problematic for young people or new immigrants who have little to no transactions on record).
How alternative data can drive change
Alternative data offers banks and lending institutions another layer of information with which they can assess the risk of a certain transaction as well as the likelihood of repayment versus defaulting. Here are some examples of the types of alternative data sets which early adoption financial institutions have started using to assess risk and approve loan requests for non-waged individuals:
- Cashflow data – This is indicative of a potential borrower’s ability to manage his or her affairs on a day-to-day basis
- Loan applications – The First National Bank of Omaha, for example, gathers information on how long it takes a loan applicant to fill out online forms. If the time gap is significantly lower than the average, these applicants are especially scrutinized by compliance teams for potential fraud attempts or use of bots to fill out fake loan applications.
- Utility and phone bill data – Banks are considering it reasonable to assume that if an individual has historically met all of his or her financial commitments, such as rent, automobile payments, utility bills and the like, he or she will continue to do so with a new debt burden.
The bottom line
As the entire planet looks for ways to overcome the trials and tribulations posed to our health but also to our economy, the financial industry would do well to expand its loan assessment models to include alternative sources. This is not only as a result of the fact that this pandemic has all but proved that current employment is no guarantee for the future as well as showing that classic data sets are insufficient for performing accurate risk assessment when making large-scale lending decisions.