Financial Technology and Innovation

The structural transformation of retail financial services by mobile banking platforms, FinTech applications, open banking ecosystems, and AI-powered credit and advisory tools has created both unprecedented opportunities for financial inclusion and a pronounced gap between adoption rates achievable in high-digital-literacy segments and those attainable in mainstream and mass-market contexts. Comparative evidence assessing customer satisfaction and financial advisor acceptance simultaneously across multiple FinTech service domains and institutional segments remains sparse, limiting the evidence base available to practitioners and policymakers designing inclusive FinTech deployment strategies. The present investigation enrolled retail banking customers across four institutional segments—traditional banks, challenger banks, credit unions, and private banking divisions alongside a parallel cohort of relationship managers and financial advisors, to assess adoption rates and satisfaction across four FinTech domains: mobile and digital banking, FinTech financial applications, open banking and personal financial management, and AI-powered credit assessment and advisory services. Significant between-segment variation was documented across all four domains, with private banking customers reporting the highest satisfaction and adoption and credit union customers the lowest. AI-powered credit and advisory services elicited the lowest customer satisfaction across all segments and the largest customer-advisor divergence. Digital financial literacy and prior FinTech experience emerged as the two strongest independent predictors of adoption. The investigation contributes a validated crosssegment measurement instrument, customer-profile-specific adoption profiles, and evidence-based recommendations for financial service providers deploying FinTech capabilities across heterogeneous customer bases.

Digital onboarding has become a decisive business capability for financial technology firms because customer acquisition, compliance screening, product activation, and trust formation now occur in the same online journey. This paper proposes a Digital Onboarding Readiness model for evaluating whether a market has the conditions required to convert identity verification into sustained FinTech usage. The model combines account access, digital payment use, mobile internet readiness, digital identity support, open-finance policy, regulatory onboarding readiness, and consumer trust into a business-oriented index. A cross-market indicator panel is analysed using descriptive profiling, maturity clustering, readiness decomposition, and predictive interpretation. The results show that strong account ownership alone does not guarantee onboarding maturity. Markets with advanced identity and policy infrastructure may still face low payment-use conversion, while markets with widespread digital payments may be constrained by trust and regulatory readiness gaps. The findings suggest that FinTech firms should treat onboarding as a portfolio capability rather than a front-end compliance step. The paper contributes a transparent measurement framework for market entry, platform partnerships, and responsible e-KYC investment decisions.

Digital card products create business value only when issued accounts translate into sustained, responsible use. This paper develops an expenditure-based segmentation model for embedded FinTech card strategy using a real credit-card micro-dataset. Average monthly card expenditure is treated as an observable engagement outcome and is examined alongside income, age, and home-ownership status. The empirical design combines descriptive portfolio profiling, robust regression, cross-validated prediction, and product-action mapping. The results show that income is the strongest observed driver of monthly spend, but the relationship is nonlinear and does not fully explain customer heterogeneity. A quartile-based segmentation separates low-use, developing-use, active-use, and premium-use customers, with mean monthly expenditure increasing from 39.92 to 666.35 across the four operating segments. The analysis argues that card engagement should be managed as a portfolio state rather than as a simple activation metric. The study contributes a transparent business-analytics framework that links observed card expenditure to embedded-finance decisions, including activation support, limit calibration, reward design, and repayment-aware engagement monitoring.

The rapid institutionalisation of artificial intelligence across financial services, healthcare, technology, and the public sector has generated a parallel proliferation of governance frameworks, ethical principles, and regulatory instruments that collectively demand organisations translate abstract values into operational practice. The gap between stated principle and enacted governance—what we term the responsible AI implementation gap—is now recognised as one of the central practical challenges in AI deployment, yet its magnitude, distribution across sectors, and organizational determinants remain poorly characterised in the empirical literature. This paper addresses that gap through a three-phase mixed-methods programme combining systematic analysis of publicly available governance frameworks, a cross-sector practitioner survey, and a governance maturity scoring exercise. Significant variation is documented across sectors on all five governance dimensions examined, with the technology sector leading on accountability and transparency, healthcare on privacy and human oversight, and the public sector on regulatory compliance readiness. Across all sectors, however, a persistent and pronounced gap exists between the governance principles that organisations formally endorse and the operational processes through which those principles are enacted: the average policy-to-practice gap across all eight governance principles assessed is consistent and substantial. Regression analysis identifies the presence of a dedicated responsible AI team as the single strongest organisational predictor of maturity, followed by staff training investment and senior executive sponsorship. The paper contributes a validated governance maturity framework, a framework coverage taxonomy for twenty-four public AI governance instruments, and six evidence-based implementation guidelines for organisations seeking to move from principle adoption to genuine operational accountability.

Financial fraud detection systems increasingly rely on machine learning to identify suspicious transactions at scale. However, the opacity of many high-performing models raises significant concerns regarding trust, regulatory compliance, and practical deployment in real-time financial environments. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance transparency and accountability, yet its feasibility under real-time constraints remains unclear. This systematic literature review examines empirical studies on explainable AI approaches for financial fraud detection, with explicit focus on real-time applicability. Following PRISMA guidelines, nineteen peer-reviewed empirical studies were selected and analyzed based on fraud domain, model type, explainability technique, evaluation metrics, and evidence of real-time performance. Results show that posthoc explanation methods, particularly SHAP and LIME, dominate the literature, while intrinsic explainability and deployment-level latency reporting remain limited. Despite frequent claims of real-time applicability, only one study provides quantitative runtime evidence. The findings highlight critical gaps: absence of explanation latency evaluation, lack of deployment-oriented validation, and insufficient regulatory compliance integration. This review reveals a systematic disconnect between real-time claims and empirical evidence, establishing the need for standardized latency benchmarking in explainable fraud detection research.