Big Data and Crowdfunding – Is this the Wild West for Credit Evaluation, the Logical Evolution of Credit Scoring or Both?

Margaret Miller, World Bank and Ivo Jenik, CGAP

At the 7th Annual Responsible Finance Forum held in Xi’an China, the role of data was highlighted in the context of crowdfunding and credit risk analysis. Crowdfunding platforms (including P2P) spoke of their ability to access a vast array of consumer data, often described as “big data.” Sometimes these data are facilitated through affiliations with other businesses, such as e-commerce sites, and relate directly to standard credit analysis methodologies such as cash-flow and gross sales receipts. However they can also include information gleaned from social media such as lifestyle data which are less clearly linked to creditworthiness. The use of big data for credit analysis is one of the innovations crowdfunding sites tout to explain their success.

The predictive power, however, of credit scoring models based on these new types of data has yet to be widely tested. Traditionally, payment histories of consumers, from formal lenders as well as from alternative sources (e.g. such as utilities providers, telecommunication companies, education providers and government agencies) have driven the predictive power of credit scoring models such as the FICO score used extensively in the US market. Credit bureau data are available to crowdfunding platforms in the U.S. but that seems to be unusual. In most countries access to bank-generated data on credit and other pertinent data on payments and customer identification found in credit bureaus are limited to the banking sector. As a result, crowdfunding platforms have no option but to rely on alternative data. In an expanding economy assessing credit risk can seem much easier – and results can appear more positive – than in one that is experiencing contraction; crowdfunding ventures, and their underwriting technologies, have yet to be tested through a prolonged downturn. More research is needed to understand the value of these types of alternative data and whether the credit assessments they enable are robust to market cycles.

In addition to questions on robustness of results, the use of big data for credit evaluation raises a number of privacy and data protection concerns, including transparency (what data are being used and where did they originate), consent (was permission provided for use in credit analysis) and access / redress (can the consumer see their own data and request that errors be corrected). While these issues may be addressed for the data in credit bureaus which originate with banks and other formal lenders and service providers, the same protections do not typically extend to big data that are amassed from a combination of private commercial transactions, government sources and publicly available information such as social media posts. In jurisdictions where access to credit bureau data are limited to banks, regulators may inadvertently be incentivizing non-banks such as crowdfunding platforms to find sources of more personal – and less tested – data for credit evaluation, with a potential outcome of restricting competition and adding risk to the market. Developing a practical approach to consumer protection for big data, which balances privacy and consumer protection with commercial applications that can facilitate commerce and even access to credit is a challenge that remains to be met.