Many excluded and underserved consumers – those most likely to take digital loans – lack credit records, documented income, or bank accounts. Credit reporting systems often exclude retail purchases on credit, utilities and rent payments, and small loans. In the absence of traditional credit data, lenders and credit scoring providers use sophisticated algorithms and generative AI models to assess credit risk based on alternative data, as discussed above.
Reforms in credit reporting systems have helped make them more inclusive and fair. One method has been to expand the scope of credit-related data beyond financial institutions to include microcredit, store credit, BNPL, utility and rent payments, etc. This enables more precise risk assessment while also allowing invisible or thin-file customers to build a credit record and increase their access to finance. Another trend is toward full-file reporting, i.e., allowing or indeed requiring creditors to report positive information on borrowers in addition to the traditional negative entries. Positive credit reporting may bring in other types of data such as account balances, number of inquiries, debt ratios, on-time payments, credit limits, account type, guarantees, debt maturity structure, and pattern of repayments. The use of comprehensive, including positive, data is empirically associated with lower incidence of extension of credit to bad debtors, and increased credit to debtors with little previous credit experience.1
Another approach to inclusive credit reporting is to adjust minimum threshold amounts for credit reporting and minimum periods for retaining credit records. Several jurisdictions have eliminated minimum thresholds, thus opening the system (or mandating its use) for any amount of credit. Since 2008, Indonesia, Tunisia, Brazil, Azerbaijan, Bangladesh have done so.2This enables small borrowers to start building a credit record when they would otherwise have remained invisible and thus shut out of affordable credit.
On the other hand, there may be a need to limit certain negative information in the record that might unduly prejudice those same borrowers. For this reason, some regulators have shielded vulnerable first-time borrowers and those experiencing hardship by setting thresholds designed to keep negative information on de minimis amounts out of the system or by restricting information on items such as delinquent medical bills. Some have set maximum retention periods for credit information, giving consumers a clean slate after a period of years – important for small borrowers blacklisted due to past negative credit history (e.g., South Africa).3In other cases, retention periods are kept relatively short for data related to financial setbacks that have been resolved. Such a policy should in principle enable distressed borrowers to remove black marks and rebuild their credit records.
Recommendation: Methods to enhance financial inclusion in credit reporting include:
- Expanding the scope of credit-related data beyond financial institutions to include microcredit, store credit, BNPL, utility and rent payments, etc. – to help thin-file clients build a credit record.
- Adjusting minimum threshold amounts for credit reporting and minimum periods for retaining credit records to avoid blacklisting.
1. Izaguirre et al. 2025 forthcoming [link]
2. https://subnational.doingbusiness.org/en/data/exploretopics/getting-credit/good-practices#GP-CI4
Country Examples
RBI’s digital credit guidelines require digital lenders – including their partner platforms – to report all lending to the credit bureaus “irrespective of its nature/tenor.” RBI also issued warnings and in some cases penalties to credit bureaus that failed to keep their information accurate and current.1
The Central Bank of Kenya’s digital credit regulations provide that no negative credit information may be submitted to a credit bureau for any loan where the outstanding amount due is KES 1,000 (USD 7.50) or less. This protects “lend to learn” digital borrowers from being blacklisted for small delinquencies.1
