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PDS434
SA
King Abdulaziz University
In the recent times, the increase in the e-Banking Services has led to new levels of flexibility and convenience demanded by the customers. In this regard, several researches have emphasised on the strongest factor which influence customer satisfaction based on the service quality provided through e-banking services. This is often referred to as “electronic-banking service quality (EBSQ)”, which is defined the service parameter in terms of security, privacy, design and reliability. Eventually, e-banking services paired with emergence of FinTech companies has conceptualised “Banking as a service (BaaS)”. BaaS serves as an end-to-end process for providing comprehensive on demand financial service in a specified timeframe via internet.
It is evident that players and technologies combined with favourable regulatory directive is changing the way banking industry operates. This has attributed to FinTech contribute to the constantly changing landscape of the evolving services offered by the banks. Despite of cumulative focus on digitisation and its impact on the banking industry, little attention has been given to whether the e-banking services offered under BaaS will be able to add value to the banking industry. The real problem does not relate to whether FinTech is better than the worldwide banks. In reality, the customer decides the efficacy of shifting towards BaaS implementation.
The aim of the research is to evaluate the impact of BaaS offered by the banks on the FinTech companies and banking industry as a whole.
H0: BaaS offered by the banks do not increase customer rating of the FinTech companies and banks
H1: BaaS offered by the banks increases customer rating of the FinTech companies and banks
BaaS is seen as an ecosystem for multiple components with changes globally as a result of regulatory framework pertaining to data access. It acts as an intermediary between FinTech and banks for offering varied types of financial services. BaaS allows plumbing of financial services through clearing access to current accounts, debit cards and the system with consumable “Application Programming Interface (API)” for the individual consumers. In many ways, BaaS allows a business to grow by creating new customer experiences and producing back-office banking features. This relates to API access to the data related to the bank account, access to core banking system, direct debit payments and domestic clearing access. The traditional approach of banking only emphasised on primary distribution channels for delivering the services. However, BaaS has made it possible for the banks to the service delivery process via third-party APIs. The BaaS offering is mainly depicted in form of API-based stack and cloud-based stack. In case of API-based stack, the structure is segregated into three segments namely ecosystem for FinTech, BaaS middleware and banks. This model is mainly preferred by the FinTech start-ups. On the other hand, the Cloud-based stack is the most preferred model where it is possible to make new developments even for the established FinTech firms through “Infrastructure as a service (IaaS)”, “Banking as a platform (BaaP)”, “software as a service (SaaS)” and “Humans as a service (HuaaS)”.
FinTech refers to the condition of new technologies and advancement to the financial industry, services and products. The increased use of investment apps, mobile banking, insurance and cryptocurrency has popularised the use of FinTech. In terms of mobile banking, the growth of FinTech is evident in form of ease of digital access to the bank accounts of the users through mobile device. Furthermore, with the advent of neo banks, FinTech have a greater role to play in terms of facilitating BaaS. In this regard, BaaS is seen as emerging sector in FinTech for delivering hassle free experience for banking products. FinTech has been conducive for inculcating the neo bank movement to help tech giants such as Google and Apple to offer valid form of BaaS services. As the BaaS works on a three-layer API-based BaaS model, the top layer is operated by the FinTech company which creates the interface with the end user for allowing receiving on the data from customers and sending it back to the BaaS layer.
As the impact of BaaS offered by the bank on FinTech and the banking industry is yet to be studied with a greater depth of understanding, exploratory research has been selected for the study. Such a research design model was conducive for investigating the problem which is yet to be clearly explained. Moreover, previous data explaining whether there is any positive or negative impact of BaaS on the FinTech and banks does not exist.
The study further relies on positivism research philosophy as it tries to explain the findings in a most objective manner based on factual information. In this regard, all the interpretation with the use of appropriate analysis techniques have only concluded on a particular finding based on observations as per the factual information.
The research has adopted deductive approach as it is entirely based on practical data collected from primary and secondary sources. As a result of such a research approach, the study has formulated a research hypothesis for testing whether or not BaaS offered by the banks increase customer rating of the FinTech companies and banks. This ensures that the research approach is efficient, thereby ensuring consistent results throughout the observations.
The data has been collected both from both primary and secondary sources. The primary data collection has been performed by conducting direct personal interview from an employee working in bank and FinTech. This data has been mainly used for the thematic analysis in order to address Research Objective 1 and Research Objective 2 of the study. The primary data has been also collected by conducting survey questionnaire through Google Form. This type of data was collected for demographic profile and to address Research Objective 3, Research Objective 4 and Research Objective 5. However, the main data collected using survey questionnaire relates to the three independent variables. This includes Independent Variable 1- Cross-Selling Capability of BaaS, Independent Variable 2- Compartmentalize business logic and Independent Variable 3- Innovation in BaaS. On the other hand, the secondary data was collected solely for the purpose of Dependent Variable which is the rating of the app. The sources of secondary data are evident with Google Play Store and Apple store (Refer to appendix 1).
The questionnaire created for conducting the survey using Google Form comprises of concepts used as per the “Nominal, Ordinal, Interval and Ratio Data (NOIR)” scale. In relation to this concept, the nominal scale has been used for the data related to demographic profile which includes parameters such as gender, age, years of experience, name of organisation and monthly income of the employees belonging to both banking and FinTech organisations. The use of interval scale is evident with intervals decided for questions such as age, monthly income and experience. Similarly, the ordinal scale is implemented for collecting the data on independent Variables, which is mainly seen with the use of Likert scale in order to define ordinality among the data. In this regard, the ordinal increase in the number is apparent with 1= “Strongly Disagree”, 2= “Disagree”, 3= “Neutral”, 4= “Agree” and 5= “Strongly Agree” (Refer to appendix 2).
The sample size of the research is 100 employees belonging to the banking and the FinTech sector. The selected banks for the study include Al Rajhi Banking Corporation, Riyadh Bank and National Commercial Bank. On the contrary, the selected FinTech for the study comprises of STCPAY, AlinmaPay and Foodics. Simple random sampling technique has been used for ensuring that each respondent has been provided with equal opportunity, thereby avoiding any biasness.
The data analysis has been performed by conducting a mixed method. In other words, both qualitative and quantitative methods have been used to validate the research hypothesis and that was the research objectives. In terms of qualitative technique, thematic approach has been implemented, which is based on forming themes as per interview transcript of the respondents. On the other hand, the quantitative research method for the study can be seen with the use of multiple regression analysis, Pearson correlation and descriptive statistics.
The validity of the hypothesis has been tested by using the significance of “Analysis of Variance (ANOVA)” computed in the multiple regression analysis. In order to discern that our model is significant, the significance of ANOVA must be ≤ 0.5 in order to reject the H0. Furthermore, the overall reliability of the independent variables has been tested by using Cronbach's Alpha value.
Two broad teams are selected for this study. These include “BaaS and open banking” as the first theme and “situation between banks and Fintech companies” as the second theme.
As per the interview response, the employees of fintech company and bank emphasised on the common ideology of segregating the different types of banking services, so that it can be offered by the third parties through API.
“BaaS is the deconstruction of banking into granular services which can be offered by the third parties via an API”.
The respondents further opined that open banking and BaaS are not the same as open banking only allows the third parties to gain access to data of the customers of the existing bank, whereas BaaS is able to provide the third parties access to functionality.
“I firmly believe that open banking and BaaS are two different concepts as open banking only allows the third parties to gain access to data of the customers of the existing bank, whereas BaaS is able to provide the third parties access to functionality as well”.
The respondents have agreed that there is an increased collaboration between the banks and into companies since GFC 2008 and also the increased use of mobile banking services in the US by 81% during 2019. This suggests that there is an increasing dependency of the banks and Fintech firms.
“There is a drastic increase of use of mobile banking services in the US by 81% in 2019.
As per the findings of the study we can infer that BaaS offered by the banks and FinTech directly influence the customer rating of the FinTech companies and banks. The interpretations of multiple regression explains that the perceived improvement through BaaS in terms of Cross-Selling Capability of BaaS and compartmentalize business logic by the banks and FinTech impacts the customer’s review of a bank or FinTech. This describes the impact of BaaS offered by the banks on the FinTech companies and banking industry. This is also evident with our rejection of null hypothesis. Therefore, if the service quality of BaaS degrades among banks and FinTech companies, the customer rating would also get impacted.
The qualitative analysis for theme one, relates to Research objective 1, which explains that BaaS is the segregation of different types of banking services offered through API by the third parties. Moreover, BaaS and open banking are not the same, as open banking only allows the third parties to gain access to data of the customers of the existing bank, whereas BaaS is able to provide the third parties access to multiple functionalities. The qualitative analysis for theme two, relates to Research Objective 2, which explains that in the recent times banks have shown an increasing tendency of collaborating with the FinTech firms. The results of Google form show that in terms of services which the bank should start offering under BaaS relates to credit arrangements. This addresses Research objective 3 of our study. The results of our study show BaaS is mainly implemented with cloud-based BaaS stack model. Such an analysis is relevant with Research Objective 4 of the study. Defect and failures are seen as the main problem among the selected models of BaaS. This is further relevant with Research objective 5 of the study.
The limitation for the study is seen with consideration of a sample size of only 100 respondents, which is the reason we are unable to achieve a better fit for our model, which is indicated by the R-square value of 0.245. Furthermore, the findings are relevant only for the FinTech/bank situated in the Kingdom of Saudi Arabia. The selection of only six FinTech/bank can be also seen as a limiting factor for the research results.
The future work should not only consider the larger sample size for the total number of employees but also including more banks and FinTech. Moreover, the number of independent variables should be also increased so that customer perspective of bank/FinTech is not restricted to only three factors of BaaS.
Based on the findings of the study, it is recommended for the users of this research to take into account various other factors which can either have a positive or negative impact of BaaS offered by the bank on FinTech and the banking industry. This can include service quality of branch banking, unique products, responsiveness, bank or FinTech size etc.
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