![]() When back-tested for model development, the target variable performed in the same way as typical credit-related information would perform for banks. The data enabled the company to devise a proxy target variable that it could use to train its credit model. In response, the company turned to its customer-usage data-specifically, data on mobile bill payments. The challenge was that the company had little credit information available to develop the offering. The company launched an unsecured cash-loan product to serve customers lacking access to formal credit. This strategy-a joint venture with companies that have complementary data about consumer segments-may be particularly suited to lenders with a regional presence.Īn approach taken by one telecommunications company is instructive. For certain types of lenders, acquiring needed data through partnerships may be an avenue worth exploring. These include external data from sources such as retailers, telecommunications companies, utility providers, other banks, and government agencies. Respecting all applicable privacy regulations and guidelines, lenders can seek to employ data from further sources. Nonfinancial companies have other internal sources of customer data, such as product usage, interactions with customer-relationship management, call records, email records, customer feedback, and website navigational data. These include loan information from lenders, deposit data with banks, other current-account information, and point-of-sale transaction data. Some traditional categories of credit behavior and demographic data are widely available, particularly for established financial institutions. They can make up for any lack of credit expertise by capturing diverse data, including data that they own exclusively. To model credit risk, new-to-market lenders will need to aggregate data from a broad range of sources. Utilize data from a wide range of sources To that end, new-to-market lenders could follow a four-part framework (Exhibit 1).ġ. By doing the advance work required to establish a credit-decision platform, lenders can move quickly while still taking the right level of credit risk. These lenders will need to actively manage credit-risk decisions and also the enabling technology. ![]() New-to-market lenders could include traditional banks expanding market share as well as nonbank financial institutions. While they develop these capabilities, they will need to take a structured approach to manage the risk of this business. They likely lack the appropriate lending infrastructure, credit-risk models, and reference data. Unlike incumbents, these new-to-market lenders may not yet have consumer lending operations and may not be serving consumers with credit history. They can move from concept to fully developed offering in two or three months, compared with one to two years for incumbents. New entrants can design new offerings quickly and are unencumbered by legacy processes or infrastructure. Many potential customers would like innovative, tailored solutions that are not always cost-efficient for traditional banks. New-to-market lenders can identify the gaps in lending coverage and try to bridge them. The resumption of the cycle also offers a window for new entrants such as utilities, insurance companies, and other nontraditional lenders to join the market.Īlthough banks provide financing solutions to a significant share of the global population, large segments of consumers are underserved or not served at all. As these markets slowly resume normal activity, a new credit cycle will begin, offering innovative lenders a rare opportunity to expand into credit markets and win market share. The global COVID-19 pandemic touched off economic effects that essentially ended the previous credit cycle in most markets.
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