On fintech and financial inclusion

Focus

Innovation in the financial sector (fintech) could disrupt existing business structures, change how existing firms create and deliver products and services, or widen access to financial services. Yet fintech also poses significant challenges to privacy, regulation and law-enforcement. It could also worsen some forms of discrimination. A key question is whether the potential efficiency gains that fintech could bring will be shared equally or lead to a rise in inequality.

Contribution

This paper first investigates whether the rise of fintech has pushed down the unit cost of financial intermediation. If financial innovation over the last years has improved efficiency in the financial sector, this should manifest itself in lower costs. In a second step, the paper asks whether the potential gains from fintech will have distributional consequences. Will fintech increase access to financial services for previously unbanked or under-banked individuals? Or will it increase inequality by favouring some groups more than others? The paper also investigates the role of machine learning and big data.

Findings

The paper shows that the unit cost of financial intermediation has fallen since the Great Financial Crisis, concluding that fintech has made the financial sector more efficient. It then develops a simple model of robo-advising, showing that fintech's net effect on welfare crucially depends on the type and size of fixed costs it entails. Even if there is an overall increase in participation, various income groups might be affected differently. Finally, the author analyses the impact of big data on discrimination. Based on a model that features a new technology to analyse non-traditional consumer data, the author concludes that big data and machine learning will probably reduce human biases against minorities, but at the same time erode the effectiveness of existing regulations.

Abstract

The cost of financial intermediation has declined in recent years thanks to technology and increased competition in some parts of the finance industry. I document this fact and I analyze two features of new financial technologies that have stirred controversy: returns to scale and the use of big data and machine learning. I argue that the nature of fixed versus variable costs in robo-advising is likely to democratize access to financial services. Big data is likely to reduce the impact of negative prejudice in the credit market but it could reduce the effectiveness of existing policies aimed at protecting minorities.

JEL codes: E2, G2, N2

Keywords: fintech, discrimination, robo advising, credit scoring, big data, machine learning