Fintech credit growth and commercial bank lending: Substitute or complement?

This study investigates the Millennials and Gen Z perception toward Fintech services, their usage intention, and their financial behavior. The study took place in the Palestinian context with a global comparison among these generations. The authors used the questionnaire-based technique to meet the study objective. West Bank respondents were selected for this purpose; the study instrument was distributed through different social media channels. The findings show that reliability/trust and ease of use are the main issues in using a financial service. Millennials are more aware (48%) of Fintech services than Gen Z (38%), which is different from the global view where Gen Z is the highest. The smartphone penetration rate is 100% among both generations, while the financial inclusion ratio in Palestine is around 36.4%; these clear indicators are the main Fintech drivers to promote Fintech services in Palestine, and these are global indicators for Fintech adoption intention. Both generations (84%) intend to use e-wallet services, Millennials (87%) and Gen Z is (70%) prefer using real-time services. Half of the respondents see that Fintech plays a complementary role with banks. The majority see that Fintech services are cheaper than bank services. Wealth management, and robot advisor services, and both generations are looking to acquire them in the long run. The authors revealed that 85% of respondents from both generations trust banks, so it is recommended that banks digitize their financial services to meet the customers’ needs, considering that 90% of respondents see that promotions are a key issue in adopting Fintech services. Promoting e-wallet services by banks is highly recommended due to the massive rivalry with Fintech parties.

The primary aim of this study is to delve into the factors influencing individuals’ readiness to embrace financial technology (FinTech) services in Bangladesh. Specifically, the study focused on Bangladeshi fintech consumer’s knowledge about contemporary digital financial tools, such as mobile-based payment service apps. Data collection was carried out using a survey questionnaire tailored to the Bangladeshi context. Participants were invited to participate in the survey, and their responses were gathered upon their consent. A five-point Likert scale, ranging from ‘1’ for ‘Strongly Disagree’ to ‘5’ for ‘Strongly Agree,’ was employed to gauge the questionnaire items. The final sample size was 450 respondents. To assess the hypotheses, a 5% significance level was employed, with data analysis conducted using SPSS software. The findings underscore a positive and statistically significant impact of financial literacy, digital literacy, and financial self-efficacy on the adoption of FinTech services in Bangladesh. Collectively, these variables elucidate 48.20% of the variance (R2=0.482) in predicting individuals’ adoption behavior of FinTech. Financial self-efficacy (β = 0.574; t-value = 8.394) has the highest effect on FinTech adoption compared to the other two factors. Additionally, a substantial correlation coefficient (r=0.634) is present between digital literacy and FinTech adoption. This study contributes to the extant literature on FinTech services by providing valuable insights that enhance scholars’ understanding of the emerging financial technologies’ significance and their predominant impacts within the Bangladeshi FinTech ecosystem. These findings hold implications for policymakers, financial institutions, and stakeholders seeking to promote FinTech adoption and foster financial inclusion in Bangladesh.

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.