Financial Services Applications Research Articles

Harnessing artificial intelligence to optimize financial technologies for achieving sustainable development goals.

This comprehensive review examines the convergence of Artificial Intelligence (AI), Financial Technologies (FinTech), and the United Nations Sustainable Development Goals (SDGs). It explores the transformative potential of AI in enhancing FinTech solutions to address global challenges outlined in the SDGs. The study provides an in-depth analysis of current AI applications in financial services, their impact on sustainable development, and emerging trends in this rapidly evolving field. Key findings reveal that AI-driven FinTech innovations can significantly contribute to financial inclusion, poverty reduction, and economic growth. Machine learning algorithms are revolutionizing credit scoring, risk assessment, and fraud detection, while natural language processing is enhancing customer service and market analysis. Computer vision technologies are improving security measures and streamlining processes in the financial sector. However, the study also identifies critical challenges that must be addressed, including data privacy concerns, algorithmic bias, and the widening technological gap. The review concludes with a series of recommendations for policymakers, financial institutions, and technology developers. These guidelines aim to promote the responsible and effective leverage of AI in FinTech to achieve the SDGs, emphasizing the need for ethical considerations, regulatory frameworks, and cross-sector collaboration. This research provides valuable insights for stakeholders working at the intersection of AI, FinTech, and sustainable development, offering a roadmap for harnessing these technologies to create a more inclusive and sustainable global financial ecosystem.

The role of artificial intelligence in enhancing financial inclusion: A review of its impact on financial services for the unbanked population in the United States

Financial inclusion, the strategy for assuring availability of accessible, affordable and reliable financial services, is critical for reducing poverty and fostering economic development globally. Despite progress, billions of people worldwide remain excluded from formal financial systems. Artificial Intelligence (AI) presents a transformative opportunity to bridge these exclusion gaps by offering innovative solutions that enhance the availability, affordability and the usability of financial services. Based on the existing research and literature review, the study examines the potential advantages, challenges, and ethical considerations associated with AI-driven financial services. This research also offers an overview of AI applications in financial services, exploring how advanced data driven methodologies such as machine learning, natural language processing, and predictive analytics are transforming the field of financial inclusion. The findings underscore AI's role in broadening access to financial services, improving financial literacy, and fostering inclusive economic growth. This research contributes to both theoretical understanding and practical applications, offering insights for policymakers, financial institutions, and fintech innovators to understand and advance inclusive financial systems globally.

Blockchain technology in financial services: enhancing security, transparency, and efficiency in transactions and services

Blockchain technology revolutionizes financial services by enhancing security, transparency, and efficiency in transactions and services. This paper examines the fundamental aspects of blockchain, including its core concepts and components, and explores its application in financial services. Blockchain's cryptographic hashing, decentralization, and immutability significantly improve security by preventing tampering and reducing fraud risks. Increased transparency through immutable audit trails, real-time updates, and open ledgers fosters trust among customers, regulators, and institutions. Furthermore, blockchain streamlines operations by automating processes, reducing intermediaries, and expediting transactions, resulting in substantial cost reductions and improved efficiency. Despite these advantages, challenges such as scalability, interoperability, regulatory uncertainty, and environmental impact must be addressed for widespread adoption. The prospects of blockchain in financial services are promising, with potential applications in decentralized finance, digital identity verification, and central bank digital currencies. This paper underscores the transformative potential of blockchain technology in creating a more secure, transparent, and efficient financial ecosystem.

Blockchain Innovation: Enhancing Security and Clarity in Digital Exchanges

Blockchain technology, which was originally intended for cryptocurrency transactions, has grown into a transformative tool for improving security and transparency in a variety of industries. This research paper examines blockchain mechanisms such as decentralisation, cryptographic security, and consensus algorithms, emphasising their importance in ensuring data integrity and transparency. The broad impact of blockchain is demonstrated through applications in financial services, supply chain management, healthcare, digital identity verification, real estate, and public services. Despite challenges such as scalability, regulatory issues, and energy consumption, blockchain offers significant benefits in terms of fraud prevention, cost savings, and efficiency. Future possibilities include combining blockchain with emerging technologies such as AI and IoT, central bank digital currencies, and more energy-efficient consensus mechanisms. This study emphasises blockchain's crucial role in improving digital exchanges and its potential to revolutionise them. Key Words: Blockchain technology, security, transparency, digital transactions, decentralization, cryptographic security, smart contracts

The AI handbook for financial services leaders : Tips and tactics for mastering AI in banking and finance

This paper explores the multifaceted realm of artificial intelligence (AI) implementation in financial services, providing insights into its potential, challenges and best practices. Highlighting the emergence of generative AI (GenAI) as a transformative tool, the paper underscores its significant impact on productivity and revenue generation within investment banks and capital markets. Addressing inherent risks of AI adoption, the paper stresses the importance of robust governance frameworks to mitigate operational, reputational and compliance risks. Specific attention is given to the phenomenon of GenAI hallucinations and the imperative for deterministic AI models to ensure data integrity and regulatory compliance. The paper outlines four key pillars of AI’s applications in financial services: predictive AI, anomaly detection AI, classification AI and GenAI. Examples highlight AI’s role in risk management, fraud prevention, customer experience enhancement and internal process optimisation, underscoring its transformative potential across the industry. The paper also covers the distinction between public and private AI models, emphasising the advantages of proprietary data-driven insights in ensuring competitive advantage and regulatory compliance. Concluding with actionable insights for AI implementation, the paper advocates for a strategic approach encompassing clear vision setting, risk oversight, data privacy management, centralised data architecture and comprehensive process automation.

Enhancing Customer Service in Banking with AI: Intent Classification Using Distilbert

With the increasing demand for efficient and responsive customer service in the banking sector, artificial intelligence offers a promising solution. This paper presents a comparative analysis of artificial intelligence methodologies applied to intent classification within the banking sector customer service domain. Utilizing a comprehensive dataset of banking service inquiries, we evaluate several machine learning approaches, including Naive Bayes, Logistic Regression, Support Vector Machine with Linear Kernel, Random Forest, XGBoost, and the transformer-based DistilBERT model. The models are assessed based on their accuracy, precision, recall, and F1 score metrics. Our findings indicate that DistilBERT, with its distilled architecture, not only outstrips traditional models but also demonstrates exceptional performance with an accuracy and F1 score exceeding 92%. The paper delves into the advantages of employing such an efficient and powerful model in real-time customer service settings, suggesting that DistilBERT offers a substantial enhancement over conventional methods. By providing detailed insights into the model’s capabilities, we underscore the transformative impact of employing advanced AI in the financial industry to elevate customer service standards, streamline operational efficiency, and harness the power of state-of-the-art technology for improved client interactions. The results showcased in this study are indicative of the strides being made in AI applications for financial services and set a benchmark for future exploratory and practical endeavors in the field.

Regression Testing Strategies for Risk and Regulatory Compliance Applications in Financial Services

Regression Testing Strategies for Risk and Regulatory Compliance Applications in Financial Services

Private Equity and Innovations in Blockchain

Advocates of blockchain claim widespread impact will be forthcoming shortly. However, the path of innovation in blockchain is uncharted. Investors will assess where blockchain innovation will profit first and most. The timing and impact of each blockchain innovation is a key consideration for business managers. Link, Ruhm, and Siegel (2014) suggest private equity investments forecast which innovations are likely to succeed. Previous research on private equity gives insight on why that ownership structure is especially successful. Private equity investments are collected for two periods and categorized according to basic blockchain components, basic cryptocurrency payment components, and decentralized applications, particularly applications in financial services. The results suggest investors have acted prudently, investing strongly in building blocks, payment systems and fundamental applications, showing a primary emphasis on refining blockchain and realizing standard blockchain protocols before investing in highly complex applications. Further, financial services applications dominate, suggesting investors see financial services as far and away the most likely industry for imminent deconstruction.

Mobile Banking: A Technological Innovation: A Strategic Application in Financial Services and the Performance of Commercial Banks in Kenya

There is perception that innovations drive long-term economic growth, productivity and improvement of performance in financial institutions however limited information is available to vouch this perception. The study which focused on Commercial Banks in Kenya was guided by these two objectives. To establish the influence of mobile banking; agency banking and intelligent ATMS on the performance of commercial banks in Kenya. The study was anchored on Dynamic Capabilities, Disruptive Innovation and Financial Intermediation Theories. Mixed research design was used in collecting and analyzing. The study targeted 10,717 management cadre in the entire banking sector out of whom a sample of 386 was chosen using Yamane formula. Primary data was collected mainly using questionnaires administered using drop and pick technique. Primary data was collected using interview guide from KII and questionnaires for quantitative information. Secondary data was collected using document analysis guide as way of giving relevance to the primary data analyzed. Analysis of data was facilitated using computer supported software SPSS 21 and presented using descriptive and inferential statistics as well as thematic prose from KII. Th descriptive statistics include frequency, percentages, weighted averages, mean and standard deviation were used. Correlation analysis and multiple regression analysis were used to infer the relationship between variables. The findings showed a positive correlation between technological innovation in financial services and the performance of commercial banks in Kenya. The findings showed that the tested technological innovations under study accounted for 66.4% of performance of commercial banks leaving 33.6% of performance to be explained by other factors not under study. The ANOVA results showed that Mobile banking, Agency banking and Intelligent ATMs as technological innovations in financial services together have a significant and positive combined effect on performance of commercial banks in Kenya

Analisis SWOT Terhadap Motivasi Penggunaan Transaksi Non Tunai (E- Money Syariah) oleh Pelaku UMKM

The necessity of easy-to-use financial services applications emerges as people's needs and mobility in the recent digital era become more complex than before. Users expect Islamic-based electronic money (e-money) applications to provide convenience in their transactions. But the development of these apps has some shortcomings. Among them is that users must connect to a good and stable internet network to run the application properly. In addition, the potential threats of cybercrimes often worry the public, which may reduce their interest in Islamic e-money applications. This study uses a qualitative descriptive approach. It aims to determine the problems in non-cash transactions within Islamic e-money applications by using SWOT analysis. This research obtains data through a combination of observation, interviews, documentation, and triangulation. Thus, this study recommends that Islamic e-money publishers improve application security systems to keep users secure and safe. It also suggests that Islamic financial institutions should expand the education of non-cash transactions and strengthen the promotion of the apps to reach a wider audience from the public and MSMEs businesspeople.

Modeling brand resonance score (BRS) – an application in financial services

PurposeThe experts in industry and academicians value brand resonance is the prerequisite factor in the firms of financial services. In this regard, the purpose of this paper is to model the brand resonance score (BRS) for modified customer-based brand equity (CBBE) model in mutual fund financial services using structural equation modeling (SEM) and analytic network process (ANP).Design/methodology/approachCriteria and sub-criteria relative weights are calculated from the SEM and sub-sub-criteria relative weights are measured through pair-wise comparison matrix for BRS modeling using ANP approach.FindingsThe brand resonance using ANP has been quantified, and BRSs of each brand through brand judgments and brand feelings criteria are calculated using two renowned Indian mutual fund services brands State Bank of India and Hong Kong and Shanghai Banking Corporation.Research limitations/implicationsInterdependency between sub-criteria are not explored. This research study is specific to Indian bank mutual fund services context.Practical implicationsResearch findings provide useful guidelines for fund managers/analysts of mutual fund service firms to improve the brand resonance to investors.Originality/valueThe paper explained modeling BRS using ANP technique which helps organizations quantify the brand resonance effectively.

Brand resonance score for CBBE model: an application in financial services

PurposeBrand resonance will significantly improve the profits of the services industry in the twenty-first century. The purpose of this paper is to find the resonance score for modified customer-based brand equity (CBBE) model in mutual fund financial services and improve the conceptualization of customer-based mutual fund services’ brand equity through brand resonance.Design/methodology/approachThe path values of SEM model was used to estimate the relative weights of criteria and sub-criteria in analytic hierarchy process (AHP) model and it was empirically tested with a sample of 240 mutual fund investors.FindingsThe brand resonance using AHP has been quantified. The resonance quantification of each brand has been demonstrated using two renowned Indian mutual fund services brands State Bank of India and Hong Kong and Shanghai Banking Corporation.Research limitations/implicationsThe interdependency of the factors which influence the resonance score is not explored.Practical implicationsResearch findings provide useful guidelines for fund managers/analysts of mutual fund services companies while improving the brand equity and strong brand’s resonance with investors.Originality/valueThe paper examines quantification of resonance for modified CBBE model in mutual fund services using data from a sample of investors in India with two mutual fund brands. The AHP structure model helps firms effectively quantify the resonance score.

Designing Lean Processes With Improved Service Quality: An Application in Financial Services

In this study, the authors focus on how service providers can meet rising customer value expectations through service process designs that are not only lean but also with high service quality. The authors consider two design approaches: one with separate (decoupled) front-office and back-office operations and one that integrates the front-office and back-office operations with a combined workforce. They develop a decision support framework to compare these two approaches in terms of both cost and service level. The integrated design approach, based on a constant staffing model, takes a systems perspective to designing the service delivery process, thereby creating opportunities to share employee resources between front-office and back-office activities due to their differing time sensitivities.Using data from a community bank case study, the authors find that the integrated design is both leaner and has a higher service level than the decoupled design. Simulation results over various scenarios confirm that the integrated design achieves better performance than the decoupled design over a wide range of parameters found in services with both front-office and back-office operations, although the decoupled design is better for some settings. This suggests that the authors' decision support tool can help service providers design lean service processes with improved service quality tailored to their own operating environments.

Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds

Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars.

Rapid Evaluation of Radiation Therapy Response Using Multi-Parametric Image Treemaps

Utilization of multi-parametric images for evaluation of treatment response can be time consuming and overwhelming. Treemaps are a new class of intuitive data visualization techniques for rapidly evaluating large amounts of time-varying data, particularly in financial services applications. The goal of this work was to investigate whether treemaps offer advantages for evaluating response to radiation therapy (RT).

Segmented Service Quality Audit (SSQA) for Banks: An Empirical Analysis

Since 1990, the Indian economy has moved towards greater openness and less controls. Many of the regulations which restricted entry, expansion of capacity and competition have been removed. This has increased the number of choices for the Indian consumer as well as competition among firms. The banking sector in India has seen several regulatory changes which have led to increased level of competition. At the same time the banks have had to meet the prudential norms set by the Reserve Bank of India (RBI). This period has also seen a gradual de-regulation of interest rates for products offered by banks and lowering of reserve requirements. More importantly, the reforms in the banking sector, following the recommendations of the Narasimhan Committee, permitted the entry of private banks, increasing the choice available to the consumer. The Indian consumer has been exposed to greater number of options and better service in almost all areas thus increasing their expectations from products and service providers. The innovations in information technology and its applications in financial services have helped in improving customer service but have also raised customer expectations regarding better technology enabled service.In this changing context it is important for banks to be aware of the various requirements and expectations amongst the different customers. This issue is gaining importance due to the fact that new age customers such as Double Income No Kids (DINK) groups have very different expectations and saving habits compared to retired-rich groups or the rural farmer. Also, rapid industrialization and blurring of geographical boundaries has brought in new challenges to banks regarding the levels of services to be offered. This study presents empirical evidence that suggests the value of conducting segmented service quality audits (SSQA).

Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution

Motivated by applications in financial services, we consider a seller who offers prices sequentially to a stream of potential customers, observing either success or failure in each sales attempt. The parameters of the underlying demand model are initially unknown, so each price decision involves a trade-off between learning and earning. Attention is restricted to the simplest kind of model uncertainty, where one of two demand models is known to apply, and we focus initially on performance of the myopic Bayesian policy (MBP), variants of which are commonly used in practice. Because learning is passive under the MBP (that is, learning only takes place as a by-product of actions that have a different purpose), it can lead to incomplete learning and poor profit performance. However, under one additional assumption, a constrained variant of the myopic policy is shown to have the following strong theoretical virtue: the expected performance gap relative to a clairvoyant who knows the underlying demand model is bounded by a constant as the number of sales attempts becomes large. This paper was accepted by Gérard P. Cachon, stochastic models and simulation.

Contributors

Contributors

Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

The move towards a customer-centred approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Several authors point out that, when evaluating customer profitability, profitable customers are rare compared to the unprofitable ones. In spite of this, most authors fail to recognize the implications of these skewed distributions on the performance of models they use. In this study, we propose analyzing CLV by means of quantile regression. In a financial services application, we show that this technique provides management more in-depth insights into the effects of the covariates that are missed with linear regression. Moreover, we show that in the common situation where interest is in a top-customer segment, quantile regression outperforms linear regression. The method also has the ability of constructing prediction intervals. Combining the CLV point estimate with the prediction intervals leads to a new segmentation scheme that is the first to account for uncertainty in the predictions. This segmentation is ideally suited for managing the portfolio of customers.

Planning for IS applications: a practical, information theoretical method and case study in mobile financial services

We use information theory to justify use of a method to help managers better understand what new IT applications and features will be most valued by users and why and then apply this method in a case study involving the development of financial service applications for mobile devices. We review five methods for data gathering, analysis, modeling, and decision-making and compare them with information processing methods for IS planning. Then we develop an IS planning method, an extended version of ‘critical success chains’ (CSC), that supports five of six of the identified information processing needs. We use this method in a project to develop ideas for mobile financial services applications at Digia, a Finland-based R&D firm. We select a group of experts and potential early adopting users. In structured individual interviews, we ask why participants prefer particular specific features the participant would expect to be part of an application. We record this data as linked chains connecting features with consequential performance and with the perceived value of such performance. The data is analyzed across participants to create network models of features, performance, and values. We conduct an ideation workshop with Digia engineers and executives to generate ideas for new systems based on the CSC models. Workshop participants created back-of-the-envelope level ideas for three new applications and business models to show graphically the flow of information, value, and revenue among parties involved in producing and using the applications. We conclude by comparing the extended CSC and other IS planning methods in terms of information theory and the information processing needs for IS planning.