FinTech Adoption Drives Faster, Safer Bank Lending in Bangladesh, Study Finds

A sweeping new study of Bangladesh’s banking sector suggests that financial technology is not merely a convenience but a genuine engine of lending performance. Researchers surveyed 354 banking and financial-sector professionals across state-owned, private, and specialized institutions and applied an extended version of the Unified Theory of Acceptance and Use of Technology, or UTAUT, to trace exactly how digital tools move from being adopted to delivering measurable results. Their findings, published in Discover Global Society, show that FinTech use strongly improves loan disbursement speed, recovery efficiency, and default-risk reduction, with the model explaining an unusually high share of the variation in loan performance.

The research arrives at a pivotal moment for Bangladesh. Mobile financial services such as bKash and Nagad now process more than USD 20 billion in transactions every month, and smartphone coverage reached roughly 70 percent in 2023, creating an enormous trove of digital transaction data. Yet the banking sector remains hampered by persistent inefficiencies: slow loan processing, weak monitoring, and a non-performing loan ratio of 8.16 percent in 2023. While digital payments have flourished, the integration of FinTech into core loan management, including origination, tracking, and recovery, has lagged far behind, leaving a critical gap between the country’s payment revolution and its lending operations.

To bridge that gap, the research team extended the classic UTAUT framework, originally developed by Venkatesh and colleagues, by adding Loan Performance as a final endogenous variable. The original model posits that Performance Expectancy, the belief that technology improves job performance; Effort Expectancy, the perceived ease of use; and Social Influence, the pressure from peers, managers, and regulators, jointly shape Behavioral Intention to adopt a technology. Behavioral Intention and Facilitating Conditions, which cover infrastructure, training, and technical support, then drive actual Use Behavior. The researchers argue this original formulation fits institutional banking better than the consumer-oriented UTAUT2, because bank adoption decisions hinge on efficiency and performance rather than hedonic motivation or price value.

The data were collected through structured questionnaires administered both physically and electronically, targeting bank officials, loan officers, IT staff, and FinTech users across all major divisions of Bangladesh, including Dhaka, Chattogram, and Khulna. Of 440 initial surveys, 354 valid responses were retained, an 80.45 percent retention rate that comfortably exceeds methodological minimums for partial least squares structural equation modeling. Respondents were experienced and well qualified: nearly half had more than fifteen years in the industry, and most held graduate degrees or professional banking credentials. All constructs were measured on seven-point Likert scales adapted from validated prior instruments and refined through a pilot test with thirty respondents.

The statistical analysis, conducted with SmartPLS and verified with STATA, revealed a clear and statistically significant causal chain. Performance Expectancy exerted a strong effect on Behavioral Intention with a path coefficient of 0.381, while Effort Expectancy was the single most powerful adoption driver at 0.438, underscoring that in a market with uneven digital literacy, user-friendly systems matter enormously. Social Influence contributed a smaller but still significant 0.193. On the next stage of the chain, Facilitating Conditions dominated with a coefficient of 0.615, and Behavioral Intention added 0.380 in predicting actual Use Behavior. Most strikingly, Use Behavior predicted Loan Performance with a coefficient of 0.898, confirming that the benefits of FinTech materialize only through consistent, effective use rather than mere intention.

The explanatory power of the model was remarkable across every stage. Behavioral Intention accounted for 82.9 percent of the variance, Use Behavior for 88.8 percent, and Loan Performance for 80.7 percent, all well above the 0.75 threshold conventionally regarded as strong. Measurement quality checks were rigorous: indicator loadings, composite reliability values between 0.880 and 0.942, and average variance extracted figures above 0.50 confirmed convergent validity, while heterotrait-monotrait ratios all fell within the acceptable 0.90 limit. Harman’s single-factor test and full collinearity assessments indicated that common method bias was not a serious concern, and robustness checks re-estimating the model without high-collinearity items produced consistent results.

The theoretical contribution is substantial. Most previous UTAUT research stopped at intention or usage, and most FinTech performance studies examined macro-level indicators such as profitability without grounding them in adoption theory. By linking individual-level acceptance constructs to institutional loan outcomes through a behavior-to-performance pathway, the study converts UTAUT into a performance-oriented institutional model. The mediation analysis is particularly instructive: Behavioral Intention affects Loan Performance indirectly, only through Use Behavior, demonstrating that strategic financial gains require the transformation of positive attitudes into daily operational practice.

The findings also carry important cautions. The authors emphasize that FinTech adoption exposes banks and borrowers to significant risks, including phishing, identity theft, ransomware, fraudulent loan applications, and algorithmic bias that could disadvantage rural borrowers, women, and small enterprises with thin credit files. Data privacy, transparency in automated lending decisions, responsible affordability assessments, and accessible grievance mechanisms are essential to sustaining trust. Bangladesh’s substantial Islamic banking sector adds a further layer, since digital lending products must comply with Shariah principles and offer clear contractual terms. Regulatory foundations such as Bangladesh Bank’s ICT Security Guidelines, mobile financial services regulations, and updated electronic know-your-customer requirements provide a starting framework, but regulatory clarity for emerging tools like peer-to-peer lending and blockchain-based credit remains uncertain.

For policymakers and bank leaders, the practical implications are direct. Because Effort Expectancy proved so influential, banks should invest in intuitive, well-tested lending platforms and pair them with digital literacy programs for staff and customers. The dominance of Facilitating Conditions argues for sustained spending on IT infrastructure, reliable connectivity, and technical support, especially in rural branches, alongside cybersecurity measures such as multi-factor authentication, encryption, real-time fraud detection, and rapid incident response. Social Influence, though weaker, can be harnessed through leadership-led digital transformation targets, industry-wide standards, and visible success stories that normalize FinTech-based lending.

The study does have limitations that temper interpretation. It relies on self-reported, perceptual measures of loan performance, since bank-level financial data are confidential, and it treats all banks as operating under comparable conditions despite real differences between public, private, and specialized institutions. Its cross-sectional design cannot capture how FinTech benefits accumulate over time, and moderating factors such as organizational culture, customer trust, and regulatory constraints were not modeled explicitly. Future research using objective indicators such as non-performing loan ratios and recovery rates, along with longitudinal and moderated designs, would strengthen the evidence. Even so, the Bangladesh experience offers a compelling template for other developing economies: locally adapted digital innovation, when paired with infrastructure, training, and prudent regulation, can convert technology acceptance into faster, safer, and more inclusive lending.

Subject of Research: FinTech adoption and bank loan performance in Bangladesh using the UTAUT model

Article Title: The role of FinTech in enhancing bank loan performance in Bangladesh through the UTAUT model

Article References: The role of FinTech in enhancing bank loan performance in Bangladesh through the UTAUT model. (n.d.). https://doi.org/10.1007/s44282-026-00573-6

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00573-6

Keywords: FinTech, Bangladesh, UTAUT, bank loan performance, digital lending, structural equation modeling, behavioral intention, use behavior, financial inclusion, cybersecurity, mobile financial services, emerging economies