Working Paper: CEPR ID: DP14221
Authors: Thomas Philippon
Abstract: The cost of financial intermediation has declined in recent years thanks to technological progress and increasedcompetition. I document this fact and I analyze two features of new financial technologies that have stirredcontroversy: returns to scale, and the use of big data and machine learning. I argue that the nature of fixedversus variable costs in robo-advising is likely to democratize access to financial services. Big data is likely toreduce the impact of negative prejudice in the credit market but it could reduce the effectiveness of existingpolicies aimed at protecting minorities.
Keywords: Fintech; Financial Inclusion
JEL Codes: E2; G2; N2
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technological advancements in fintech (O14) | efficiency gains in financial intermediation (G00) |
| efficiency gains in financial intermediation (G00) | democratize access to services (I24) |
| decrease in fixed costs associated with robo-advising (G31) | increase in access to investment services for lower wealth households (G59) |
| big data and machine learning (C55) | reduction of unwarranted human biases against minorities in credit markets (J15) |
| big data and machine learning (C55) | more equitable lending practices (G21) |
| adoption of big data and machine learning (C55) | diminishment of existing regulations effectiveness (L51) |