Abstract

Fintech revolutionized the traditional banking business models in emerging countries. The effect of fintech on banks’ operating efficiency and risk-taking behavior is still inconclusive. The study is aimed at exploring the effect of fintech products on banks’ operating efficiency and risk-taking behavior. The study used a quantitative research approach by collecting secondary data from annual reports of 50 commercial banks from emerging countries, namely, China, India, Pakistan, and Bangladesh, for the period 2014 to 2021. The study used panel data for path analysis and structural equation modeling (SEM) to test the theoretical mediation model by using STATA. The results show that the fintech product reduces the bank’s risk-taking behavior by enhancing the bank’s operating efficiency. The path analysis results show that operating efficiency mediates the relationship between fintech products and bank risk-taking behavior in emerging countries. The paper offers useful recommendations for central bank and commercial bank policymakers. The study is also beneficial for commercial banks that use fintech solutions to increase operational effectiveness and reduce risk. The study is the first empirical investigation into the connection between the growth of fintech products, bank operational effectiveness, and risk-taking behavior in developing nations.

Introduction

The global financial industry has been rapidly changing with the advancement of financial technology over the last few years. New emerging technologies such as Internet finance, blockchain, and mobile banking are penetrating, forming the fintech that revolutionized the whole financial industry. It offered lower-cost services with more choices for consumers to buy financial services that shift their financial behavior to digitalization. The rise of financial technology (fintech) has brought about significant changes in the financial industry, including the way banks manage risk. Fintech has introduced new tools and technologies that enable banks to more accurately assess and manage risk, as well as new business models that challenge traditional approaches to risk-taking.

The pace of fintech development in developed and emerging economies countries is different. In developed countries such as the UK, USA, and Europe, fintech extended the financing transaction methods through algorithmic trading, helping the manager to build better compliance systems to manage risk, and it also improves the electronic payment systems for both domestic and cross-border payments [1]. However, in developing countries, fintech adoption has been slow despite having growth and expansion opportunities in the digital economy (Zhang et al., 2020). Fintech has expanded financial services digitally across countries, particularly during the COVID-19 pandemic. During the pandemic, the usage of fintech services and products has increased tremendously as identified in research [2].

Conclusion and Recommendations/Implications of the Study

The evolution of digital technology has changed the world drastically. The COVID-19 pandemic changed the whole business structure. The customers’ increasing demand for fintech products increases the competition in the financial industry. To retain their customers and remain competitive in the industry, traditional banks adopted fintech products. So that they can deliver efficient, accessible, and low-cost financial services to their customers with the help of technology. Fintech has facilitated the banking industry with better user experience and lower costs by using technological advancement.

The primary objective of the study is to explore the relationship between fintech products and bank risk-taking behavior with mediating role of a bank’s operating efficiency using a sample of commercial banks from China, India, Pakistan, and Bangladesh. The empirical evidence shows that the bank’s risk-taking behavior decreases, and bank stability is improved with the development of fintech products. Fintech products improve the bank’s operating efficiency. The effect of fintech products on bank risk-taking is more pronounced in countries such as China and India because they are extensively using fintech products in their operations. Banks are adopting fintech products such as artificial intelligence blockchain, mobile banking, and Internet banking. The results show that the banks adopting fintech products in their initial phase increase the bank risk-taking behavior, and then later on, it decreases the bank risktaking behavior by improving their operating efficiency.

Abstract

     Lending business crowdfunding (LBC) is an innovative financing tool for small and medium enterprises (SMEs) and is especially useful in vulnerable and risky markets. To date, little is known about the information transparency and management dynamics of these new platforms, partly as a result of a lack of harmonization in the crowdfunding sector. This article draws on a qualitative multicase approach to map and classify the main characteristics of the credit supply provided by nine LBC platforms. The database is unique and makes this analysis particularly original. The study found some limitations to the information transparency on services offered to SMEs who could access LBC. Alongside this, some advantages emerged, such as the timeliness of the service, and some critical points, such as the high interest rates and the numerous fees applied to the crowd-borrowers. The study also has practical implications, allowing SMEs to carefully assess the costs and benefits of the LBC model.

Introduction

     The uncertain economic scenario aggravated by the COVID-19 pandemic is worsening firms’ conditions, compromising their financial sustainability and limiting access to bank credit (International Monetary Fund, 2020).

     Digital innovation in finance is playing a fundamental role in supporting companies in difficulty; in fact, traditional banks joined by FinTech, BigTech, and TechFin are expanding and diversifying their credit offer through technological platforms (Consob, 2018). These digital players are introducing new business models (Stulz, 2019) penetrating into market segments typically underserved by incumbent banks (Jagtiani & Lemieux, 2018) or in countries characterized by inefficient financial systems and regulatory vacuums (Ding, Kavuri & Milne, 2021). FintTechs are changing the structure of financial services through digital models, such as peer-to-peer lending (P2PL) (Frost, Gambacorta, Huang, Song Shin, & Zbinden, (2019). Funds granted through these new instruments can improve the financial sustainability of small and medium enterprises (SMEs), encouraging the implementation of innovative and sustainable projects.

     Specifically, in a postcrisis economy, the architecture of entrepreneurial ecosystems must evolve and focus on new financing alternatives (e.g., Crowdfunding and P2PL), ensuring the survival of successful businesses (Cicchiello, 2019a). Crowdfunding (hereafter ‘CF’) is intended as an “alternative” financial circuit to the traditional banking system, useful for economically supporting small firms (Fenwick, McCahery, & Vermeulenm, 2018; Havrylchyk & Verdier, 2018). Some studies highlight CF as a driver to encourage investment in new technologies (Brema, Bilgram, & Marchuk, 2019) or as a valuable tool to support social initiatives related to sustainability (Bento, Gianfrate, & Thoni, 2019).

Conclusions, limitations, and implications

     Using an original empirical framework, this study explores the characteristics of lending CF and creates a comparative map of the services and information that LBC platforms offer to crowd borrowers and crowd investors. Information asymmetries in the LBC model reduce the trust in the tool by potential users and, consequently, slow down the development of new financial products in the credit market.

     The focus of this study is on FinTech platforms in the Italian credit market, which showed growth from 2016 to 2021, considering the data from the PoliMi Report. The current nine pure for-profit LBC platforms in Italy were analyzed as case studies. The results show that the LBC is an alternative financial circuit to bank credit, even though banks in Italy still play a crucial role in the economy. As smaller and riskier companies are subject to rationing, FinTech in the credit segment is therefore an innovative, complementary, and useful solution to finance entrepreneurial, ethical and/or sustainable projects.

     Some specificities of credit platforms emerge from our original map. First, all platforms are subject to banking supervision and adopt segregated liability to protect customers. The players are concentrated in Northern Italy, highlighting a greater knowledge of the instrument by professionals and investors and the financial development of the area. It is not known whether this affects the geographic origin of customers, as there are no data on the matter and digital technology crosses geographic boundaries.

Abstract

Cyber threat attribution identifies the source of a malicious cyber activity, which in turn informs cyber security mitigation responses and strategies. Such responses and strategies are crucial for deterring future attacks, particularly in the financial and critical infrastructure sectors. However, existing approaches generally rely on manual analysis of attack indicators obtained through approaches such as trace-back, firewalls, intrusion detection and honeypot deployments. These attack indicators, also known as low-level Indicators of Compromise (IOCs), are rarely re-used and can be easily modified and disguised resulting in a deceptive and biased cyber threat attribution. Cyber attackers, particularly financially-motivated actors, can use common high-level attack patterns that evolve less frequently as compared to the low-level IOCs. To attribute cyber threats effectively, it is necessary to identify them based on the high-level adversary’s attack patterns (e.g. tactics, techniques and procedures – TTPs, software tools and malware) employed in different phases of the cyber kill chain. Identification of high-level attack patterns is time-consuming, requiring forensic investigation of the victim network(s) and other resources. In the rare case that attack patterns are reported in cyber threat intelligence (CTI) reports, the format is textual and unstructured typically taking the form of lengthy incident reports prepared for human consumption (e.g. prepared for C-level and senior management executives), which cannot be directly interpreted by machines. Thus, in this paper we propose a framework to automate cyber threat attribution. Specifically, we profile cyber threat actors (CTAs) based on their attack patterns extracted from CTI reports, using the distributional semantics technique of Natural Language Processing. Using these profiles, we train and test five machine learning classifiers on 327 CTI reports collected from publicly available incident reports that cover events from May 2012 to February 2018. It is observed that the CTA profiles obtained attribute cyber threats with a high precision (i.e. 83% as compared to other publicly available CTA profiles, where the precision is 33%). The Deep Learning Neural Network (DLNN) based classifier also attributes cyber threats with a higher accuracy (i.e. 94% as compared to other classifiers).

Introduction

Cyber threat attribution facilitates the identification of an attacker or his/her intermediary. This can be used in subsequent (forensic) investigation by organizations or prosecution by law enforcement and other relevant stakeholders. For example, the U.S. Congress enacted the Cybersecurity Information Sharing Act (CISA) into law in 2015 [1], which mandates organizations (including financial institutions) involved in cyber data breach incidents to share cyber threat intelligence (CTI) with other relevant stakeholders, particularly their customers [2]. The interpretation and practices of cyber attack attribution have evolved with time. In the earlier days, the focus of attribution was to locate the attacker(s) or intermediary(ies) launching distributed denial of service (DDoS) attacks and to help stop malicious traffic via IP traceback. Thus, cyber attack attribution refers to source trace-back techniques that work backwards to geographically locate the origin of IP packets via router traversal record [3,4]. Source trace-back techniques cannot truly attribute cyber attacks, partly due to the intrinsic limitation of IP address spoofing and anonymization. A resourceful attacker can, for example, compound the challenges of attribution by using reflection hosts, small Time To Live (TTL) values, employing botnets as stepping stones, and launching attacks over wider time frames.

This study compares the factors that determine consumer acceptance SMS (Short Message Service), NFC (Near Field Communication) and QR (Quick Response) mobile payment systems, in addition to determining the principal factors which influence the adoption of these mobile payment systems as means payment. A comprehensive review of the scientific literature has justified the development of a behavioral model that explains intention to use of mobile payments. The results and novelty of this research lies in the formulation of a different behavior according to the use given by users to each of the proposed payment tools. The conclusions and implications for management provide alternatives for companies.

Introduction

In recent years, payment systems have evolved from simply cash or credit card transactions to different types of mobile payment systems. This transition has taken place due to changes in the economy, technological developments on the Internet, the proliferation of social networks, and increased use of mobile devices. Since smartphones are nowadays a pervasive commodity, consumers are benefiting from the ease and convenience of paying for goods and services when approaching this new payment channel. Mobile payment systems have adapted not only to a mostly digital and mobile free reality, but also to a new business climate, facilitating business transactions anywhere, anytime and for anyone. According Tecnocom (2015), while electronic or online payment systems have experienced significant growth, mobile payments have not met the initial expectations (Anil et al., 2003; Liang and Wei, 2004). The reasons are diverse: the strong competition between the various parties involved in the financial ecosystem (major technology companies, FinTech companies, startups, banks, etc.), the simultaneous development of FinTech industry and the challenge that implies the adoption of new FinTech systems by consumers, which is greatly impacted by the scarce knowledge regarding mobile payments (LiébanaCabanillas et al., 2015), the user confidence doubts (Sorkin, 2001; Yu et al., 2018), the complexity of the systems, privacy concerns and a lack of security (Hwang et al., 2003; Qin et al., 2017), among other reasons.

1- Fintech: An introduction

2- Emergence of fintech

3- Fintech ecosystem

4- Fintech business models

5- Investment decisions for fintech projects and real options

6- A real options approach

7- Challenges facing the fintech sector

8- Summary

References

Abstract

Fintech brings about a new paradigm in which information technology is driving innovation in the financial industry. Fintech is touted as a game changing, disruptive innovation capable of shaking up traditional financial markets. This article introduces a historical view of fintech and discusses the ecosystem of the fintech sector. We then discuss various fintech business models and investment types. This article illustrates the use of real options for fintech investment decisions. Finally, technical and managerial challenges for both fintech startups and traditional financial institutions are discussed.

Fintech: An introduction

Financial technology (fintech) is recognized as one of the most important innovations in the financial industry and is evolving at a rapid speed, driven in part by the sharing economy, favorable regulation, and information technology. Fintech promises to reshape the financial industry by cutting costs, improving the quality of financial services, and creating a more diverse and stable financial landscape (‘The FinTech Revolution,’ 2015). The technological developments in infrastructure, big data, data analytics, and mobile devices allow fintech startups to disintermediate traditional financial firms with unique, niche, and personalized services. According to PwC (2016), 83% of financial institutions believe that various aspects of their business are at risk to fintech startups. Due to fintech companies already having a significant impact on the financial industry, every financial firm needs to build capabilities to leverage and/or invest in fintech in order to stay competitive. The growth of investment in fintech has been phenomenal. According to Accenture (2016a), global investment in fintech ventures in the first quarter of 2016 reached $5.3 billion, a 67% increase overthe same period the previous year, and the percentage of investments going to fintech companies in Europe and the Asia-Pacific nearly doubled to 62%. Much of this increase in investment has come from traditional financial institutions. Traditional financial institutions invest in external fintech startups in the form of collaborative fintech ventures, as well as their internal fintech projects in hopes of leapfrogging fintech innovation and gaining a competitive advantage.

As the banking industry becomes increasingly competitive, each bank must develop proprietary services and products to serve existing customer needs and attract new clients. Banks should bring the greatest utility and convenience to their customers and thus need to focus on customer satisfaction and provide incentives to influence customer loyalty. The services that meet customer requirements should be simple, compatible, and personalized with complementary services. In addition, banks should have a digital platform with fast and secure access (Shaikh & Karjaluoto, 2015). In the current digital age, Financial Technology (FinTech) plays an important role in helping banks gain new customers and scale their business operations. Banks and other financial services companies found that FinTech has brought innovations into the financial market (Gulamhuseinwala, Bull, & Lewis, 2015) with mobile technologies as an essential platform supporting FinTech. A wide range of new mobile technologies have been developed specifically for financial services and the banking industry. The number of mobile payment transactions worldwide has increased by approximately 58.33 percent from 2014 to 2015 (Dapp, Slomka, & Hoffmann, 2015). Hence, banks should integrate mobile payment systems in order to eliminate the need for customers to enter their personal payment details for each transaction. Thailand’s payment environment, while challenging, supports the use of electronic devices such as the mobile phone. According to the mobile banking usage statistics for Thailand published by the Bank of Thailand, the number of customer transactions conducted via mobile banking services has rapidly increased in the last five years (Trairatvorakul, 2014). Takorn Tantasith, the SecretaryGeneral of the National Broadcasting and Telecommunications Commission (NBTC) Thailand, noted that the latest estimates indicate approximately 93.7 million registered mobile phone users, and yet only 20 percent of bank customers presently use mobile banking services. This shows a low uptake of mobile banking services despite high mobile phone use by the Thai population. The main barrier to using mobile banking in Thailand is the preference for cash payment, followed by security concerns, and finally consumer behavior (Tavilla, 2015). Extensive review of extant research on technology adoption reveals that many authors have previously attempted to combine two or more theories to explore customer adoption of information technology (ArenasGaitan, Peral, & Jeronimo, 2015; Chan & Lu, 2004; Martins, Oliveira, & Popovic, 2014; Oliveira, Faria, Thomas, & Popovic, 2014). In the current research, the Technology Acceptance Model (TAM) and the secondgeneration theoretical model known as Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) are used to empirically study mobile banking adoption.

The paper by Hong [1] presents a secure and light IoT protocol (SLIP) for antihacking. The proposed method introduces a number of typical applications such as the smart home, intelligent transportation, a security-issue analysis, and related convergence technologies for the realization of a secure IoT. Further, a comparison of the security issues between the IoT and the traditional network was performed, and it was concluded that the environment of the IoT-convergence system is a dangerous one with limited resources and fewer network guards. The paper by Li et al. [2] analyzes a study on the service and the trend of fintech security that is based on text mining. This study presents the tasks and directions of the fintech industry in consideration of the successful operation of fintech security through an analysis of the news articles from an online-data collection. The results contributed to a switching of the market awareness of fintech services and security in the present-day ongoing expansion of the fintech market.

A considerable amount of revenue has been invested in the information technology (IT) infrastructure of banks to enhance their performance, but investment in IT remains a substantial risk regarding the return on investment (Carlson, 2015). Most banks and financial organizations around the globe are subject to extreme pressure from their customers and competitors to enhance IT. In the 21st century, the main sources of revenue generation for German banks are interest margins and the provision of services such as wealth management, mortgage lending and financial advice. However, the benefit from these services has declined, causing many challenges for these banks as they strive to return to a period of profit. Today, most of these banks are embracing financial technology (FinTech), due to the promise of its ability to generate new revenue streams, personalize offers, target cross-selling and improve customer services. However, in order to explore such opportunities, mobile applications and other technology platforms need to be launched. Germany has implemented various regulations and programs to encourage FinTech adoption; for example, during the Bundesbank 19th banking symposium, it was argued that banks in Germany need to adopt disruptive digital innovation to acquire technical awareness in advances (Patel, 2000; Stolterman & Fors, 2004). Furthermore, today’s digital banking has broadened from standard online banking to inventive ideas that involve video consultancy services, credit brokerage and the incorporation of social media. The need for awareness of global cyber-attacks and their mitigation was also stressed. Furthermore, cooperation between the Bundesbank, the BaFin, the European Banking Authority (EBA) and the European Central Bank (ECB) was suggested to establish an information technology audit service, with the intention of developing a supervisory regime to enhance security (Deutsche Bundesbank Eurosystem, 2015; Carlson, 2015). In the face of ongoing cyber-attacks, financial institutions must continue to strengthen their cyber security framework by investing assets in gathering, examining andsharing cyber-attack intelligence information to better comprehend the change in complex security risks (Carlson, 2015).