Table 2 Illustrates some of the... | Emerald Publishing

Table 2

Illustrates some of the concepts, sub-themes and themes extracted from the coding of the data collected from the reviewed literature

Some literature	Direct quotes	Concepts	Sub-themes	Themes
Bernards (2019), Cai et al. (2022), Girardone et al. (2024), Gozman et al. (2018), Mamonov (2021), Milana and Ashta (2021), Muthukannan et al. (2021)	“The availability of big data, in conjunction with advances in artificial intelligence (AI) and machine learning (ML), allows FinTech to achieve more precise risk estimations” (Girardone et al., 2024, p. 3)	Sophisticated algorithms; Mobile computing; Advanced storage and networking; Borrowers’ data disclosure; Information flow; and financial information restructuring	De-obfuscating financial information flow; Coordinated flow of financial information; Data-driven analysis of historical customer data; Algorithmic scanning pattern; and Internet browsing histories	Synaptic Signalling of Borrowers' Financial and Demographic Records for Credit–Risk Evaluation
	“Thanks to sophisticated algorithms, FinTech can speed up the borrower screening processes and reduce the time needed to accept/deny loan requests” (Girardone et al., 2024, p. 3)
	“Financial information restructuring through combinations of technological innovations” (Gozman et al., 2018, p. 170)
	“Data-driven analysis of historical customer data for personalised, real-time adopted financial recommendations gets even more precise. This is fostered by more available data and the evolving AI capability to handle structured and unstructured data” (Fabri et al., 2022, p. 6)
	“Alternative credit data seeks to abstract calculable credit risks from everyday economic activities, and how those systems plug into existing financial infrastructures” (Bernards, 2019, p. 5)
	“The advent of mobile banking means that ML models being deployed by financial services firms today are able to leverage real-time location data to improve detection of fraudulent Transactions” (Gomber et al., 2018, p. 231)
Bharathi et al. (2023), Cai et al. (2022), Dhavamani et al. (2023), Girardone et al. (2024), Gozman et al. (2018) Mamonov (2021), Muthukannan et al. (2021), Pal (2023)	“The digital revolution has promised a world without boundaries, where opportunities are just a click away” (Samy et al., 2024, p. 1)	Value-cocreation and customer-centric; Extension of access; Screening and monitoring of borrowers' activities	Machine-based learning and financial information flow; Disintermediate and restructure the financial information flow	Boundless Access and Continuous Synaptic Financial Signal Acquisition
	“Our findings show that fintech is broadening access to financial services, offering individuals in emerging markets like Egypt more options and therefore, theoretically, enhancing their well-being and thus pushing forward FI” (Samy et al., 2024, p. 2)
	“Financial innovations that build on new technological platforms are breaking down traditional barriers of geography, access, and asymmetric information” (Gozman et al., 2018, p. 149)
	“Crowdfunding platforms harness the power of social media to allow private individuals to collectively finance entrepreneurial activities” (Gozman et al., 2018, p. 148)
	“A particularly common claim here is that innovative uses of what are often called “alternative” forms of data – ranging from algorithms scanning patterns of mobile phone use or internet browsing histories, to so-called “psychometric” credit scores – offer means of increasing lending to borrowers in the global south lacking recorded credit histories, property titles, or pay slips and income tax records” (Bernards, 2019, p. 1)
Bernards (2019), Dhavamani et al. (2023), Girardone et al. (2024), Gomber et al. (2018), Gozman et al. (2018), Mamonov (2021), Muthukannan et al. (2021)	“Careful profiling of customer purchase patterns on one card can also help develop purchase patterns for infrequently used or potentially new cards” (Gomber et al., 2018, p. 232)	Precise risk estimation; Transparency of borrowers’ data; Borrowers’ specific information	Contextualised information gathering for a specific domestic borrower; personalised customer-centric experiment, information transparency	Specificity of Synaptic Financial Signals in Credit–Risk Evaluation
	“Data-driven analysis of historical customer data for personalised, real-time adopted financial recommendations gets even more precise. This is fostered by more available data and the evolving AI capability to handle structured and unstructured data” (Fabri et al., 2022, p. 6)
	“The automatic processing of the generated data allows Fintech to operate far more efficiently and enables them to make use of technologies, such as data analytics or artificial intelligence, to retain and expand their customer base while managing their risks” (Zhang-Zhang, Rohlfer, & Rajasekera, 2020, p. 3)
	“The connection between the crowd and entrepreneurs is often facilitated by an online platform. Entrepreneurs present their projects on the platform, alongside other projects. Users can browse several projects, get information and updates, and are provided with direct channels of communication with the entrepreneurs. Hence, users take individual decisions to invest/lend/purchase/donate, but fund as a crowd” (Crosetto & Regner, 2018, p. 1463)

Some literature	Direct quotes	Concepts	Sub-themes	Themes
Bernards (2019), Cai et al. (2022), Girardone et al. (2024), Gozman et al. (2018), Mamonov (2021), Milana and Ashta (2021), Muthukannan et al. (2021)	“The availability of big data, in conjunction with advances in artificial intelligence (AI) and machine learning (ML), allows FinTech to achieve more precise risk estimations” (Girardone et al., 2024, p. 3)	Sophisticated algorithms; Mobile computing; Advanced storage and networking; Borrowers’ data disclosure; Information flow; and financial information restructuring	De-obfuscating financial information flow; Coordinated flow of financial information; Data-driven analysis of historical customer data; Algorithmic scanning pattern; and Internet browsing histories	Synaptic Signalling of Borrowers' Financial and Demographic Records for Credit–Risk Evaluation
	“Thanks to sophisticated algorithms, FinTech can speed up the borrower screening processes and reduce the time needed to accept/deny loan requests” (Girardone et al., 2024, p. 3)
	“Financial information restructuring through combinations of technological innovations” (Gozman et al., 2018, p. 170)
	“Data-driven analysis of historical customer data for personalised, real-time adopted financial recommendations gets even more precise. This is fostered by more available data and the evolving AI capability to handle structured and unstructured data” (Fabri et al., 2022, p. 6)
	“Alternative credit data seeks to abstract calculable credit risks from everyday economic activities, and how those systems plug into existing financial infrastructures” (Bernards, 2019, p. 5)
	“The advent of mobile banking means that ML models being deployed by financial services firms today are able to leverage real-time location data to improve detection of fraudulent Transactions” (Gomber et al., 2018, p. 231)
Bharathi et al. (2023), Cai et al. (2022), Dhavamani et al. (2023), Girardone et al. (2024), Gozman et al. (2018) Mamonov (2021), Muthukannan et al. (2021), Pal (2023)	“The digital revolution has promised a world without boundaries, where opportunities are just a click away” (Samy et al., 2024, p. 1)	Value-cocreation and customer-centric; Extension of access; Screening and monitoring of borrowers' activities	Machine-based learning and financial information flow; Disintermediate and restructure the financial information flow	Boundless Access and Continuous Synaptic Financial Signal Acquisition
	“Our findings show that fintech is broadening access to financial services, offering individuals in emerging markets like Egypt more options and therefore, theoretically, enhancing their well-being and thus pushing forward FI” (Samy et al., 2024, p. 2)
	“Financial innovations that build on new technological platforms are breaking down traditional barriers of geography, access, and asymmetric information” (Gozman et al., 2018, p. 149)
	“Crowdfunding platforms harness the power of social media to allow private individuals to collectively finance entrepreneurial activities” (Gozman et al., 2018, p. 148)
	“A particularly common claim here is that innovative uses of what are often called “alternative” forms of data – ranging from algorithms scanning patterns of mobile phone use or internet browsing histories, to so-called “psychometric” credit scores – offer means of increasing lending to borrowers in the global south lacking recorded credit histories, property titles, or pay slips and income tax records” (Bernards, 2019, p. 1)
Bernards (2019), Dhavamani et al. (2023), Girardone et al. (2024), Gomber et al. (2018), Gozman et al. (2018), Mamonov (2021), Muthukannan et al. (2021)	“Careful profiling of customer purchase patterns on one card can also help develop purchase patterns for infrequently used or potentially new cards” (Gomber et al., 2018, p. 232)	Precise risk estimation; Transparency of borrowers’ data; Borrowers’ specific information	Contextualised information gathering for a specific domestic borrower; personalised customer-centric experiment, information transparency	Specificity of Synaptic Financial Signals in Credit–Risk Evaluation
	“Data-driven analysis of historical customer data for personalised, real-time adopted financial recommendations gets even more precise. This is fostered by more available data and the evolving AI capability to handle structured and unstructured data” (Fabri et al., 2022, p. 6)
	“The automatic processing of the generated data allows Fintech to operate far more efficiently and enables them to make use of technologies, such as data analytics or artificial intelligence, to retain and expand their customer base while managing their risks” (Zhang-Zhang, Rohlfer, & Rajasekera, 2020, p. 3)
	“The connection between the crowd and entrepreneurs is often facilitated by an online platform. Entrepreneurs present their projects on the platform, alongside other projects. Users can browse several projects, get information and updates, and are provided with direct channels of communication with the entrepreneurs. Hence, users take individual decisions to invest/lend/purchase/donate, but fund as a crowd” (Crosetto & Regner, 2018, p. 1463)