TL;DR: In this paper, the authors investigated the factors influencing the behavioral intention to adopt payments banks services by Indian underbanked and unbanked population, and found that perceived credibility is the strongest influencer of behavioral intention.
Abstract: The purpose of this paper is to investigate the factors influencing the behavioral intention to adopt payments banks services by Indian underbanked and unbanked population.,The proposed model has assimilated factors from the Unified Theory of Acceptance and Use of Technology (UTAUT) along with perceived credibility. The factors of UTAUT include performance expectancy, effort expectancy, facilitation of conditions and social influence. Apart from testing the direct relationships of the model constructs with the behavioral intention to adopt payments banks services, the study has also explored mediating and moderating effects of certain constructs. The research model has been empirically tested using 660 responses from a field survey conducted in New Delhi – the capital city of India – by using the structured equation modeling (SEM) technique. The target respondents of the study are small businessmen and migrant laborers who are either underbanked or unbanked.,The findings of the study reveal that the model is able to explain 67.5 per cent of the variance in behavioral intention. The results indicate that all the factors are direct determinants of behavioral intention. Perceived credibility is found to be the strongest influencer of behavioral intention. The findings also indicate that perceived credibility partially mediates the relationships between “social influence and behavioral intention” and “performance expectancy and behavioral intention.” The relationship between performance expectancy and behavioral intention is also found to be moderated by facilitating conditions and effort expectancy.,As this study is based on a convenience sample of respondents of only one city of India, this could negatively reflect on the generalizability of results across other cities. Moreover, the study has only focused on the perceptions of small businessmen and migrant laborers. This raises concerns regarding the applicability of the results for other segments of the current population that have different demographic characteristics (e.g. occupation, income, education level and technology experience). Modifying the conceptual model presented in this research to include “experience” and “age” as moderators can also be worth considering in future. Although this study has extended the UTAUT to include perceived credibility, the results of the explanatory power of the model indicate that there is still room for improvement. Therefore, including other constructs, e.g. hedonic motivation, perceived risks and trialability, could be a fruitful path forward. Future studies may also examine the factors influencing the actual use behavior of payments banks, rather than just behavioral intention.,The study looks forward to providing the payments banks service providers in India with suitable guidelines for effectively implementing and designing payments banks services. Specifically, the results of this study have provided clues for Indian payments banks service providers about the crucial role of perceived credibility in influencing the behavioral intention to adopt payments banks. Therefore, service providers have to initially be sure that payments banks are able to conduct financial transactions efficiently, securely and within less time, along with the availability of information required by customers to successfully use the services. Service providers should enhance customer confidence and trust by providing secure and reliable services. They should also emphasize on the positive safety measures of the payments banks during any marketing campaign rather than just creating brand awareness.,The study represents a substantial contribution to the existing knowledge regarding mobile payment channels in particular and technology acceptance area in general. In fact, this study presents a worthwhile direction by examining payments banks services, which, so far, have not been well evaluated in the Indian context. To the best of the authors’ knowledge, this is an early attempt toward a holistic and integrative approach to explain adoption of payments banks in India. Although prior studies have addressed mobile banking and mobile payment adoption, the strength of this research lies in combining the UTAUT constructs with perceived credibility. This is evidenced by the high explanatory power (67.5 per cent) of the research model adopted in this study.
TL;DR: In this paper, the authors investigated the impact of machine learning and artificial intelligence in credit risk assessment and found that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard.
Abstract: In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.
TL;DR: A digital transformation is taking place in the financial services industry, with a host of non-bank innovators offering both customer facing and back office financial technology products and services as mentioned in this paper.
Abstract: A digital transformation is taking place in the financial services industry, with a host of non-bank innovators offering both customer facing and back office financial technology products and services. This transformation includes emerging market economies, and in many places offers a viable digital alternative to traditional banks, which have left significant populations underbanked. This note explores the challenges and opportunities that financial technology innovations present for banks in these nations.
TL;DR: In this article, the authors examined the characteristics of unbanked and underbanked consumers, their current use of mobile financial services, and the potential for mobile banking and payments to better integrate these consumers into the financial mainstream.
Abstract: The increased use of mobile devices coupled with the evolution of technologies that enable consumers to conduct financial transactions using mobile phones has the potential to change how consumers manage their finances. Innovations in financial service technologies may also help foster access and inclusion in the mainstream financial system for unbanked or underbanked consumers. Using data collected through the Board’s Survey of Consumers and Mobile Financial Services, this article examines the characteristics of unbanked and underbanked consumers, their current use of mobile financial services, and the potential for mobile banking and payments to better integrate these consumers into the financial mainstream.
TL;DR: According to the World Bank's Global Findex database, about 1.7 billion adults were unbanked in 2017, which means that they lacked an account with a formal financial institution or a mobile money provider as mentioned in this paper.
Abstract: According to the World Bank’s Global Findex database, about 1.7 billion adults were unbanked in 2017, which means that they lacked an account with a formal financial institution or a mobile money provider. Most of the unbanked population is in developing countries. In South Sudan, for instance, only 9% of the adults had a bank account. Similarly, about 70% of the population in Latin America is unbanked or underbanked (Rojas-Torres, Kshetri, Hanafi, & Kouki, 2021). Likewise, according to the International Finance Corporation, over 200 million small and medium enterprises (SMEs) in developing countries lack access to financial services.