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Nayak, Saugat
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Economics, Econometrics & Finance

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Leveraging Artificial Intelligence and Machine Learning for Real-Time Loan Approval Processes in FinTech Nayak, Saugat
The Es Economics and Entrepreneurship Vol. 3 No. 03 (2025): The Es Economics And Entrepreneurship (ESEE)
Publisher : Eastasouth Institute

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AI and ML have revolutionized loan granting across the financial technology industries through lending performance evaluations, changing from conventional, manual analysis to automatic, real-time computations. This transition resolves some of the main failures of conventional methods, significantly decreasing approval time, increasing the accuracy of risk assessment, and creating custom loan services for various customer types. Big data and symbiotic non-conventional parameters, including social media scores and behavioral patterns, are used in the AI and ML systems to determine an applicant's creditworthiness, thus extending a fair credit–risk culture in financial services. Through certain critical technologies like neural networks, NLP, and credit scoring models, there is a more secure and dynamic way of lending since real-time frauds are detected online. This paper focuses on the development, issues, and impact of the regulation of using artificial intelligence in the credit approval process among FinTech firms. The research indicates that while using AI improves business performance and customer experiences, the case necessitates appropriate data security and bias elimination policies to be implemented by FinTech companies. The paper concludes with prospects for the development of AI to further progress financial inclusion and the development of loaning industries.
Enhancing Customer Experience in FinTech through AI, Machine Learning, and Data-Driven Insights Nayak, Saugat
The Es Economics and Entrepreneurship Vol. 3 No. 03 (2025): The Es Economics And Entrepreneurship (ESEE)
Publisher : Eastasouth Institute

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This article discusses AI, ML, and data analyses to define how the FinTech sector enhances client experience. In the current digital financial service delivery world, customers expect to be offered efficient, customized, and self-service. FinTech companies meet these expectations through AI and ML technologies such as predictive analytics, natural language processing, recommendation systems, behavioral analytics, and real-time data visualization. These tools assist firms in creating customer-specific and user-specific CX and interactions to more effectively address customers and users, thus enhancing experience, contentment, and devotion. This article focuses on AI work in predictive customer life cycle management, real-time financial health monitoring, and better support systems. Furthermore, it highlights issues, including data privacy, AI model bias, regulatory issues, and technical issues that firms in this domain must overcome to offer secure and non-biased FinTech services. Conclusions based on the case studies and comparing existing industry trends investigate how the AI tools are being used in the real world to improve FinTech CX from companies such as Chime, Revolut, and Robinhood. In conclusion, with the future trends, the article indicates the potential expansion of AI and data-driven solutions in the FinTech CX, which will provide even more personalization, additional layers of fraud detection, and more mature predictions for the field of AI.
The Role of Data Visualization Tools in Financial Decision-Making: A Comparative Analysis of Tableau, Power BI, and SSRS Nayak, Saugat
The Es Accounting And Finance Vol. 3 No. 03 (2025): The Es Accounting And Finance (ESAF)
Publisher : Eastasouth Institute

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This paper discusses the effect of data visualization instruments on the management of money or funds, with special reference to Tableau, Power BI, and SQL Server Reporting Services (SSRS). As big data volumes define today's financial sector, these tools provide just the right set of features to analyze massive amounts of information. Tableau is thereby brought out for its enhanced data visualization, where financial analysts can easily analyze data and get real-time trends. Power BI is also inexpensive and fits into the Microsoft ecosystem: AI is used to provide personalized recommendations and to detect anomalies automatically. SSRS, on the other hand, is more popular for its strong reporting purposes. It can handle more formatted reports, which are needed for such tips as regulation with colossal organizations. In the comparative analysis, each tool's effectiveness in the financial scenarios and its drawbacks are discussed, and how each of the tools can be applied in risk management, resource allocation, and market trend identification are displayed. Tableau is best used for interactive dashboards, Power BI has customized visuals better for customer behaviors, and SSRS is best for structured tabular reports with large volume data. It also demonstrates the interaction between Tableau and SSRS, which, when combined, make real-time data analysis and structured reports increase the rate of decision making. Anticipated development inversions like AI integration into services, real-time analytics, and self-service business intelligence are others that are seen to redesign the financial sector's manner of handling data. It will be wise to adopt this research's implication that the identification of the right tool based on an organization's organizational needs can substantially enhance financial operations' efficiency and offer a competitive advantage in data-intensive contexts.
Machine Learning in Financial Risk Management: Techniques for Predicting Early Payment and Default Risks Nayak, Saugat
The Es Accounting And Finance Vol. 3 No. 03 (2025): The Es Accounting And Finance (ESAF)
Publisher : Eastasouth Institute

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Artificial intelligence and, commonly, its subfield of machine learning (ML) has dramatically impacted financial risk management by improving the elicitation and flexibility of risk forecasts, especially concerning early payment and default risk. That is why it has become possible to speak about the existing traditional risk assessment models that no longer apply in a modern financial context, as they are oriented on historical data and are to be implemented with the help of relatively rigid frameworks. On the other hand, ML provides real-time prediction services, which leverage big datasets and learning algorithms like the logistic regression models, the random forest, and neural nets to develop proper risk profiling. The significant uses of the JHL method are for early payment prediction, default risk identification and credit scoring, which is flexible. There are benefits accrued to its use, such as increased predictive accuracy and real-time risk assessment, where it adopts a cheaper model to arrive at the results. However, its disadvantages include data privacy, security, and interpretability drawbacks. The future of ML in financial risk management trends will include the eventual use of technologies such as blockchain and AI to enhance decentralized, efficient, and secure risk management systems. As ML progresses, it is predicted that this technology will increase the efficiency, effectiveness, and individuality of risk management processes in the financial industry.
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