BigTech and the changing structure of financial intermediation

Frost, Jon; Gambacorta, Leonardo; Huang, Yongqiang; Shin, Hyun Song; Zbinden, Pablo

doi:10.1093/epolic/eiaa003

Cited by 290 publications

(171 citation statements)

References 18 publications

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“…Financial technology (FinTech) is revolutionising the financial services industry at an unrivalled pace (Frost et al 2019). From mobile payments, robo-advising, app-based investing platforms, to online banking solutions, FinTech developments have impacted upon financial planning, financial well-being and economic inequality (Frame, Wall, and White 2019).…”

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confidence: 99%

Financial literacy and responsible finance in the FinTech era: capabilities and challenges

Panos

Wilson

2020

The European Journal of Finance

135

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A growing body of evidence suggests that financial literacy plays an important role in financial well-being, and that differences in financial knowledge acquired early in life can explain a significant part of financial and more general well-being in adult life. Financial technology (FinTech) is revolutionising the financial services industry at an unrivalled pace. Views differ regarding the likely impact that FinTech is likely to have on personal financial planning, well-being and societal welfare. In an era of mounting student debt, increased (digital) financial inclusion, and threats arising from instances of (online) financial fraud, financial education and enlightened financial advising appropriate policy interventions that enhance financial and overall well-being. This special issue engages in this important academic and policy agenda by presenting a set of seven new papers emanating from four parallel streams of literature related to financial literacy and responsible finance. ARTICLE HISTORY

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confidence: 99%

Financial literacy and responsible finance in the FinTech era: capabilities and challenges

Panos

Wilson

2020

The European Journal of Finance

135

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“…Sources: Frost et al (2019); World Bank; Forrester Research; GlobalData; iResearch; Mercado Libre; Nikkei; Worldpay; national data; BIS calculations.…”

Section: Mobile Payments and Bank Accountsmentioning

confidence: 99%

“…In Germany, De Roure et al (2016) find that fintech credit serves a slice of the consumer credit market neglected by German banks. In Argentina, Frost et al (2019) show that 35% of the small borrowers from Mercado Libre, a big tech e-commerce platform, would not be eligible for bank credit based on their credit bureau score. Looking across countries, these authors find that big tech credit is more prevalent when there is a low density of bank branches relative to the population.…”

Section: Widening the Tent: Fintech For Financial Inclusionmentioning

confidence: 99%

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The Economic Forces Driving FinTech Adoption across Countries

Frost

2020

SSRN Journal

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BIS Working Papers are written by members of the Monetary and Economic Department of the Bank for International Settlements, and from time to time by other economists, and are published by the Bank. The papers are on subjects of topical interest and are technical in character. The views expressed in them are those of their authors and not necessarily the views of the BIS. This publication is available on the BIS website (www.bis.org).

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“… SeeÓskarsdóttir et al (2019) orFrost et al (2019) for a general discussion about the value of big data for credit scoring. In this article, we limit ourselves to the use of machine learning algorithms with "traditional data" for credit risk analysis without any reference to "new data" (social or communication networks, digital footprint, etc.)…”

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confidence: 99%

Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds

et al. 2020

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Decision trees and related ensemble methods like random forest are state-of-the-art tools in the field of machine learning for credit scoring. Although they are shown to outperform logistic regression, they lack interpretability and this drastically reduces their use in the credit risk management industry, where decision-makers and regulators need transparent score functions. This paper proposes to get the best of both worlds, introducing a new, simple and interpretable credit scoring method which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with couples of predictive variables are used as predictors in a penalized or regularized logistic regression. By modeling such univariate and bivariate threshold effects, we achieve significant improvement in model performance for the logistic regression while preserving its simple interpretation. Applications using simulated and four real credit defaults datasets show that our new method outperforms traditional logistic regressions. Moreover, it compares competitively to random forest, while providing an interpretable scoring function.

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BigTech and the changing structure of financial intermediation

Cited by 290 publications

References 18 publications

Financial literacy and responsible finance in the FinTech era: capabilities and challenges

Financial literacy and responsible finance in the FinTech era: capabilities and challenges

The Economic Forces Driving FinTech Adoption across Countries

Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds

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