NOLEGEIN- Journal of Business Risk Management

Innovation-Driven Financial Inclusion as a Catalyst for Sustainable Economic and Social Development

Shivangi Saxena — 2026-07-04

A key instrument for advancing sustainable economic and social development is innovation-driven financial inclusion. It entails utilizing technology innovations like digital banking, mobile payment systems, fintech platforms, and data analytics to increase marginalized and underprivileged groups' access to financial services. Innovative financial solutions facilitate access to banking, credit, insurance, and savings facilities for consumers and small enterprises by lowering barriers associated with cost, distance, and documentation. Low-income populations' financial security, entrepreneurship, and economic involvement are all improved by this inclusion. Additionally, it strengthens rural economies, empowers women, and makes it easier for government welfare programs to be delivered effectively through digital platforms, all of which contribute to social development. By encouraging financial stability, transparency, and ethical business practices, innovation-driven financial inclusion also contributes to sustainable development. This study looks at how innovation-driven financial inclusion may support sustainable development and the economy. The study assesses opinions about financial innovation accessibility, digital financial usage, and their effects on social and economic outcomes using a survey of 100 participants by Convenience sampling method. The data will be analyzed using correlation analysis and descriptive statistics. In order to optimize the advantages of financial inclusion, the study emphasizes on the significance of digital financial infrastructure, financial literacy, and supportive legislative frameworks.

IMPACT OF USER PERCEPTIONS ON DIGITAL FINANCIAL SERVICE USAGE: EVIDENCE FROM RURAL HOUSEHOLDS IN THE SAURASHTRA REGION

Janhavi Shah — 2026-07-04

The rapid expansion of digital financial services (DFS) has improved financial inclusion, particularly in developing economies like India. However, despite better access, their actual use among rural populations remains uneven. This study explores how user perceptions influence the adoption of digital financial services among rural households in the Saurashtra region. Using a quantitative approach, primary data were collected from 275 respondents. The analysis focused on three key perception-based factors—perceived usefulness (PU), perceived ease of use (PEU), and perceived risk (PR)—and applied descriptive statistics, correlation, and multiple regression techniques. The findings show that both perceived usefulness and perceived ease of use have a positive and statistically significant impact on usage. In contrast, perceived risk has a negative but statistically insignificant effect. The model explains 31.9% of the variation in usage behavior, indicating moderate explanatory power. Overall, the results suggest that rural users are more influenced by the benefits and ease of using digital financial services than by concerns about risk. This highlights the need to design simple, efficient, and user-friendly platforms. The study offers practical insights for policymakers and financial service providers seeking to strengthen digital financial inclusion in rural areas.

A Comparative Study of the Impact of Behavioural Finance on Equity Investment Decisions: Evidence from Investors in Junagadh and Rajkot

Vipul Sundavadara — 2026-05-21

This study examines the influence of behavioural finance on equity investment decisions among individual investors in Junagadh and Rajkot. The research is based on primary data collected through structured questionnaires administered to 200 respondents, with an equal representation of 100 investors from each city. The study specifically focuses on key behavioural biases, including overconfidence, familiarity bias, regret aversion, and narrow framing, and further evaluates the impact of demographic factors such as age and education on these biases. Statistical tools such as independent sample t-tests and analysis of variance (ANOVA) were employed to analyse the data and identify significant differences between the two regions. The findings reveal that most behavioural biases do not significantly differ between investors in Junagadh and Rajkot; however, investors in Junagadh demonstrate a relatively higher tendency toward narrow framing in their investment decisions. On the other hand, demographic variables, particularly age and education, exert a more pronounced influence on investor behaviour in Rajkot, indicating notable regional variation. The study highlights the critical role of behavioural biases in shaping investment decisions and underscores the need for enhanced financial literacy and awareness programs to support rational decision-making among investors.

Predictive Fraud Detection Architecture: A Multi-Method Empirical Evaluation of Data-Driven Forensic Analytics, Internal Control Systems, and Continuous Auditing Using Primary Organizational Data

David Sunday Araoti — 2026-07-04

The growing complexity of financial systems and the increasing sophistication of fraudulent activities have intensified the need for predictive and data-driven fraud detection architectures. Traditional audit approaches, characterized by periodic reviews and retrospective analysis, are no longer sufficient to address real-time financial risks. This study develops and empirically evaluates a predictive fraud detection architecture that integrates data-driven forensic analytics, internal control systems, and continuous auditing mechanisms using primary organizational data. A quantitative research design is adopted, utilizing primary data collected from 150 finance and audit professionals across multiple sectors. The study employs structured questionnaires and applies statistical analysis using SPSS, including descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. The results indicate that all three components significantly contribute to fraud detection effectiveness, with data-driven forensic analytics demonstrating the strongest predictive influence, followed by internal control systems and continuous auditing mechanisms. The regression model explains a substantial proportion of variance in fraud detection effectiveness, confirming the robustness of the proposed architecture. The findings highlight the importance of integrating analytical capabilities with control structures and real-time monitoring systems to enhance predictive fraud detection. Organizations that adopt such architectures are better positioned to identify anomalies, reduce audit lag, and strengthen financial governance. This study contributes to the literature by advancing a predictive, multi-component fraud detection framework grounded in empirical evidence from primary data. It also provides practical insights for organizations seeking to transition from reactive to proactive fraud detection strategies. While the study is based on perception data, it establishes a foundation for future research incorporating real-world financial datasets and advanced predictive models.