Financial innovation and additionality: The power of economic analysis and data analytics

As public and private financial institutions innovate and expand the range of financFinancial Educationial products that households and firms use, questions about how these services are affecting consumers, providers, and the economy as a whole have become central. A new policy brief by Abraham, Schmukler, and Tessada explores how evaluating the “additionality” of financial services can help answer such questions.

A financial service is considered additional if it creates value for the economy. In other words, their net effect on the overall economy after weighting its direct and indirect effects is positive, benefiting both consumers and producers of these products. A financial service can produce additionality in multiple ways, whether it is by targeting excluding groups, reducing costs, creating new jobs, or increasing innovation. Examples of additional financial services include when traditional banks and fintech firms use nontraditional data to assess clients’ risk and provide or facilitate access to lending to credit-constrained individuals. Similarly, online platforms that bring together small firms, their large suppliers, and private banks to promote factoring operations can produce additionality by allowing small firms to obtain working capital.

Measuring additionality is not straightforward: it requires identifying and quantifying the financial and economic effects of a financial service on its users, nonusers, and financial intermediaries. However, several approaches help identify the possible implications of financial services and provide insights as to their additionality. One approach is case studies, which are mainly qualitative and try to answer basic questions about a financial service (such as how the service addresses market failures or who uses the service). A more data-driven approach is to examine data for the entire financial sector and analyze how different financial institutions are providing valuable services. For example, analysis of loan and credit bureau/registry data could help determine whether the emergence of new fintech firms allows new individuals and firms to obtain loans. A more focused approach based on data analytics consists of quantifying the effect that a financial service has on its users. The idea is to compare users of a financial service (the “treated group”) with a similar group of nonusers (the “control group”), and identify whether users benefit in a way that is not observed for nonusers. This approach, commonly known as impact evaluation, includes methods such as difference-in-differences, discontinuity analysis, and randomized control trials.

Successfully adopting additionality imposes challenges such as analysts and practitioners changing the way they think about financial innovation, increased data analyses, and coordination between financial sector stakeholders. Despite these challenges, measuring additionality facilitates better evaluations of the impact of financial innovations and thus helps improve the overall allocation of both public and private resources and services in the financial sector. As such, the public and private sectors would benefit from starting to discuss and introduce additionality measures.