of certain products, business models, and data may not be feasible in all markets. Their adoption will require thoughtful consideration of systemic and institutional factors as well as consumer protection. The borrowers referred to in this chapter include the small and medium businesses that make up the majority of enterprises providing jobs in most emerging economies,1 as well as households and micro entrepreneurs. These groups find it challenging to access formal credit even when the economy is sound and growing, and all have been significantly affected by the pandemic. Nonetheless, the credit needs of small businesses differ from those of microentrepreneurs, for example, and they are often served by different financial services providers, offering different solutions.
Solving the COVID-19 risk puzzle: Risk visibility and recourse Beyond its profound impacts on the credit risk of households and businesses, the pandemic significantly impaired the visibility that lenders have into a borrower’s capacity and willingness to repay a loan, and it limited lenders’ options for recourse in the increasingly likely event of a default. Policy responses to help alleviate the impacts of the pandemic reduced near-term risks, but further reduced visibility into and certainty about the underlying viability of borrowers. The protracted effects of the pandemic on the economy and the financial sector may over time affect the liquidity and capital of finance providers, diminishing even more their willingness and ability to take on risk. A lender’s decision to extend credit and the associated terms reflect the amount of risk the lender is willing to take based on estimates of both the borrower’s probability of default and the anticipated loss in the event of a default. The ability to assess the likelihood of repayment depends on the available information about the borrower and the context of the loan (visibility), whereas estimates of loss in case of default are based on the market for collateral or the enforceability of guarantees (recourse). As noted in earlier chapters, the pandemic and associated lockdowns had a profound impact on economic activity, affecting borrowers (businesses and households) directly and increasing credit risk. For some sectors and businesses, the impact was transitory and it diminished as lockdowns were lifted. For others, the effects will last longer. For example, in Rwanda business sectors that rely on in-person work (such as construction and accommodation and food) were more affected by the lockdowns than sectors that could transition some of their activities to remote working. Once lockdowns were lifted, however, construction quickly recovered well above precrisis levels, but for the accommodation and food sector, where face-to-face interactions with customers are necessary, the crisis dragged on.2 When lenders confront uncertain conditions, they typically respond by tightening credit standards and reducing credit supply, shifting to safer assets. If lenders lack solid information with which to assess risks, they reduce credit not only to insolvent businesses and households, but also to everyone else because they are not able to distinguish between the two groups. Although uncertainty has always been part of lender business models, before the pandemic finance providers were better able to determine a borrower’s ability and willingness to repay and the probability of default by taking into account credit and payment histories, income, or assets; nonfinancial information (such as home address, relevant sector of the borrower’s business, and length of banking relationship) that can act as a proxy for income; the purpose of the loan (home loans or loans for business equipment have a different risk profile than loans for consumption or working capital); and the time horizon for the loan (visibility tends to be higher over shorter time horizons). For business loans, lenders would rely on heuristics and models to take into account sector or demographic norms (such as typical inventory turns or balance sheet ratios for a given industry). The significant structural break caused by the crisis diminished, however, the value of past data and heuristics. Traditional credit data sources are largely backward-looking. But with so many sectors,
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