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Marketplace lending has substantially changed since the first peer-to-peer lending platforms emerged in 2006. The industry is now an alternative to bank lending, predicted to total $70 billion for consumer and business loans worldwide by 2030. Marketplace lending is often deemed less safe than bank loans, mainly due to these portfolios' high degree of hidden information. These include needing more information on borrowers and potential correlations between them, which might lead to higher risk than is apparent at first glance. Deterministic processes cannot capture tail risk appropriately, so platforms and lenders should employ stochastic processes. This chapter introduces a Monte Carlo simulation and machine learning (ML) process to evaluate and monitor portfolios. For marketplace lending to become a viable and sustainable alternative to bank lending platforms, they must better evaluate, monitor, and manage tail risk in marketplace loans and develop tools to monitor and manage financial risk losses.

This chapter reviews new technologies, new types of players and new types of financial products that together are fundamentally affecting supply and demand dynamics in the financial sector and contributing to the emerging digital financial landscape. The aim of this chapter is to set a common understanding on the underlying forces of digital disruption in the financial sector before exploring the challenges to monetary and financial stability that are arising. In later chapters, the book will examine how central banks might deal with the challenges and help shape the emerging digital financial landscape.

After completion of the case study, the students will be able to explore the challenges involved in growing a business during its early stages inorganically, discuss the challenges faced by start-ups in their quest for growth in an emerging market, investigate the reasons behind the mergers and acquisitions, comprehend the issues in the merger of a start-up and a conventional bank, identify the various possible synergies out of the merger and examine the growth strategies that a troubled start-up such as Slice can follow to survive and expand its business operations.

The case study discusses the challenges that Slice, a modern fintech organization, and North East Small Finance Bank (NESFB) face due to the changing business and regulatory environment. After working tirelessly to earn the trust of India’s banking regulator, Slice got the approval for its merger with NESFB. While Slice and NESFB got a new lease of life after the approval of their merger, Rajan Bajaj, founder of Slice, needed to make the merger a success by leveraging on the strength of the combined entity and meeting all the lending and other regulatory requirements applicable to small finance banks.

This case study is suitable for MBA/MS/BBA/BS students.

Teaching notes are available for educators only.

CSS 11: Strategy.

This chapter looks at challenges that are arising from emerging business models and those that are related to digital finance in general. This chapter first looks at the four challenges relating to new business models, i.e. walled gardens, shadow banking, monetary sovereignty and singleness of money. The chapter then looks at the four challenges relating to digital finance in general, i.e. consumer's data rights, AI ethics, cybersecurity and financial exclusion.

The purpose of this paper is to explore two financial inclusion measures adopted within the local welfare context of the city of Milan, Italy, examining their functioning and underpinning representations. The aim is also to understand how such representations take concrete shape in the practices of local actors, and their implications for the opportunities and constraints regarding individuals' effective inclusion. To this end, this paper takes a wide-ranging look at the interplay between the rise of financial inclusion and the individualisation and responsibilisation models informing welfare policies, within the broader context of financialisation processes overall.

This paper draws on the sociology of public action approach and provides a qualitative analysis of two case studies, a social microcredit service and a financial education programme, based on direct observation and semi-structured interviews conducted with key policy actors.

This paper sheds light on the rationale behind two financial inclusion services and illustrates how the instruments involved incorporate and tend to reproduce, individualising logics that reduce the problem of financial exclusion, and the social and economic vulnerability which underlies it, to a matter of personal responsibility, thus fuelling depoliticising tendencies in public action. It also discusses the contradictions underlying financial inclusion instruments, showing how local actors negotiate views and strategies on the problems to be addressed.

The paper makes an original contribution to the field of sociology and social policy by focusing on two under-researched instruments of financial inclusion and improving understanding of the finance-welfare state nexus and of the contradictions underpinning attempts at financial inclusion of the most vulnerable.

Financial technology (fintech) refers to using new technology to improve and automate the delivery and use of financial services. This chapter provides a brief introduction to fintech. It also includes the book's purpose, distinguishing features, intended audience, and structure. A synopsis of Chapters 2 through 23 is offered. The chapter concludes that fintech is constantly evolving and is reshaping finance. Fintechs offer a new paradigm of growth.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

In this article, we assess the impact of inflation on the current account balance in the case of Tunisia, covering the period 1976–2022.

The study utilizes a threshold regression approach proposed by Hansen (2001) in a bid to identify inflation threshold values.

The results show two inflation threshold values (3.87% and 8.41%), which determine three inflation regimes in the case of Tunisia. In lower inflation regimes, inflation has a positive and statistically significant impact on the current account balance. However, in higher inflation regimes, where inflation rates exceed 3.87%, there is a negative and statistically significant correlation with the current account balance, resulting in a deficit.

The research suggests the need for a new policy approach that considers these threshold levels to address high inflation rates, which currently stand at approximately 11%, and aims to restore them to the targeted rate of 4%. This necessitates coordinated monetary and fiscal measures.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.