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Chatbots in Banking: Transforming Customer Interaction and Service Efficiency Through AI
The advent of Artificial Intelligence (AI) and chatbot technologies has brought drastic transformative changes to the banking sector which has reshaped customer engagement, enhanced efficiency and provided 24/7 assistance to the customers. The paper investigates the usage and impact of AI-powered chatbots on the customer experience and overall performance of the banking institutions through thorough analysis of recent advancements in technology, the study explores how chatbots which are leveraging machine learning (ML) and natural language processing (NLP) are used to address customer enquiries, facilitate transactions and offer customized financial guidance. Additionally, the current study also examines the influence of chatbots on customer satisfaction, regulatory compliance and measures of security highlighting both the advantages and challenges of such systems. Hence, the aim is to contribute to a comprehensive understanding of chatbots' role in banking providing insights into their effectiveness and potential for the future refinement to meet evolving customer expectations. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Chatbots as tools for psychoeducation and self-help in mental health
The chapter highlights the role of mental health chatbots in solving problems in global mental health. It aims to explore how AI-powered conversational agents bridge the gap between growing demand and limited availability of mental health services. The authors explain advantages of chatbots, including accessibility, affordability, and user anonymity. They investigate the effectiveness of chatbots in improving treatment outcomes, meeting user needs, and improving operational efficiency. In addition, the chapter highlights the ability of chatbot as a knowledge sharer that seamlessly integrates information from various sources and keeps professionals up to date with current events. It deals with psychoeducation in clinical and non- clinical populations, covering biological, cognitive, emotional, and behavioural aspects. The authors also discuss the potential of chatbots in screening and self- help. Finally, they emphasise the importance of collaboration between psychologists, physicians, and engineers to optimise the development of chatbots to respond dynamically to user needs. 2025, IGI Global Scientific Publishing. All rights reserved. -
Chatbot Service Quality in Banking : Analyzing Indian Banking Customer Perceptions and Influence on Customer Satisfaction and Value
Purpose: The study has two objectives: first, to determine the quality of chatbot services provided by Indian banks; second, to assess the influence of chatbot service quality variables on customer satisfaction and customer value. Research Methodology: The study used a quantitative methodology, selecting individuals at random from a group of Indian banking clients. We used a questionnaire to collect data from the selected sample as part of a causal research investigation. We made use of SPSS and Python for this analysis. Customer satisfaction and value were taken into account as the dependent variables in our study. The seven elements of service qualityfunctionality, convenience, security, design, customization, enjoyment, and assurancemade up the independent variables. Findings: According to this study, client satisfaction and value were significantly shaped by the quality of the services provided. Customers value was significantly impacted by functionality and enjoyment, and their satisfaction was greatly influenced by assurance, design, and personalization. The unexpected negative impact assurance had on customer value is noteworthy and calls for more research. Practical Implications: In the highly competitive banking industry, this research has important ramifications for banks. It highlighted how important service quality is, which led banks to give priority to customer pleasure and think about making strategic changes. Banks could obtain a competitive advantage by improving the quality of their services, improving chatbot services, and implementing a customer-centric strategy by utilizing the research findings that have been presented. Our research helped banks evolve with the needs of their customers in mind, enabling them to gain credibility, repeat business, and long-term success in the ever-changing banking services market. Originality/Value: This study examined how consumers in Indian banks perceive the value and satisfaction of chatbot services and how they use them. The study provided useful recommendations and concepts to improve the general consumer experience. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Charting the Future of Fintech: Unveiling Finoracle through an In-depth Comparison of LLAMA 2, FLAN, and GPT-3.5
The research paper compares three Large Language Models (LLMs): LLAMA 2, FLAN, and GPT-3.5, in summarizing financial technology (fintech) news. Using 100 articles and the Rouge scoring system, it focuses on LLAMA 2's superior performance in creating concise and precise summaries. The study also introduces FinSage, a new framework utilizing LLAMA 2, promising to enhance fintech text analysis and decision-making. It concludes that LLAMA 2 sets a new standard for AI in financial data processing and analysis. 2024 IEEE. -
Charting the Course of Leadership Through the Digital Era: A Synergy of Leadership Styles, Technology Integration, and Deep Learning
This chapter aims to provide actionable strategies for empowering leaders in leveraging technology to amplify their leadership efficacy in contemporary business environments by employing extensive literature review and deep learning models, a method in Artificial Intelligence (AI). It investigates the effectiveness of three leadership approachesTransformational, Transactional, and Servant Leadershipin meeting high-performance expectations within organizational contexts. The performance of these leadership styles based on their effectiveness scores have been analyzed using data analysis techniques and neural network models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Furthermore, Deep Convolutional Generative Adversarial Networks (DCGANs), have been utilized to visualize complex leadership dynamics. The findings from this comprehensive analysis provide valuable insights into the strengths and limitations of each leadership approach, guiding strategic leadership development initiatives and organizational decision-making processes. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions
Diabetes mellitus is a global health challenge shaped by complex clinical, demographic, and socioenvironmental factors. Traditional linear models often overlook the non-linear dependencies that drive diabetes risk. This study adopts a systems-thinking approach by integrating mutual information (MI)-based network modeling with machine learning to improve prediction, interpretability, and fairness. Using a nationally representative CDC dataset, we build a weighted undirected network where variables are nodes connected by MI-derived edges. Centrality analysis identifies age, HbA1c, and BMI as key hubs. Community analysis reveals clinical, demographic, and racial modules, reflecting the multidimensional nature of diabetes risk. These network insights inform feature selection for training logistic regression, random forest, and XGBoost models. XGBoost achieves the highest accuracy (95.3%) and AUC (0.939), while logistic regression offers the best calibration (Brier score = 0.045), enhancing clinical usability. Subgroup analysis shows stable predictions across racial groups, supporting fairness. This integrated framework uncovers latent, non-linear associations and offers a robust, interpretable, and equitable tool for precision diabetes risk modeling. 2025 IEEE. -
Charting Industry 4.0 Routes Incubation Centers: A Study on Atal Incubation Centre
Start-ups have garnered significant attention in India, as well as many other areas of the world, in the past few years. Start-ups may produce significant solutions through innovative and adaptable technology, acting as vehicles for socioeconomic growth and transformation. Some businesses were created in recent times, but the ecosystem was still in its infancy, with just a few investments and a limited number of support programs such as incubators. As business environments have evolved, there are signs that the function of incubators has shifted and extended to a center giving training, with over 20, 000 start-ups and a year-on-year growth rate of 10-12%, India boasts the worlds second largest start-up ecosystem. Since business settings have changed, there are indications that the purpose of incubators has moved and expanded to a center for training and support, connecting, and consulting to new enterprises in all fields of specialization rather than just a business center with office space. According to incubators, the major worry or problem was establishing suitable infrastructure with specialized technology that met the demands of the companies. In terms of investment, incubators were concerned about the schemes rigidity. Some incubators opted to extend financing or make grants rather than make equity investments. This issue originated largely from the regulatory complexities of incubators at educational institutions that make equity-based investments in businesses. Start-ups do not exist in isolation, but rather as a component of the larger economy. In terms of the regulatory framework, it is thought that enhancing the execution of existing start-up regulations and eliminating inefficiency within the administration is critical to making it easier for start-ups to do business. Start-ups would benefit from less paperwork and documentation, improved access to information, more standardized operational processes, and clear criteria. 2023 Taylor and Francis Group, LLC. -
CHARM: Context-based Hierarchical Association Rule Mining for Analyzing Purchase Patterns
Data mining is now an essential part of business intelligence, specially in the retail analytics, allowing companies to derive meaningful insights out of big volumes of transaction data. This paper uses Context-Based Hierarchical Association Rule Mining to study purchase behavior in Indian retail outlets through Apriori algorithm that helps to take effective decissions. The available literature primarily employs flat item association models and lacks contextual dimensions and profit-oriented outcomes of rules, which also creates an evident gap in the current research. The study combines various contextual aspects, including product category, sub-category, region, and state, to produce the multilevel association rules indicating the product relationship under different sales levels of the products following an hierarchy. The Lift and Conviction metrics are applied along with support and confidence to eliminate the coincidental patterns and make the rules in business reliable. Support-based filtering and a minimum threshold of confidence of 0.1 are used to determine separate patterns of co-purchase that are significant. In order to make business relevant, the level of profit is involved as a result which puts into emphasis rules which lead directly to financial performance. The findings show that context-enriched rules offer a better insight into customer buying behavior and retailers have the opportunity to identify profitable cross-selling opportunities that more traditional flat associated analysis might otherwise miss. The hierarchical structure allows improving interpretability through associating items with larger contextual properties, which will be useful in designing the promotion, product placement, and optimizing the regional strategy. Overall, this paper presents the combination of contextual and profit-driven parameters as a concept that can be used to provide a data-driven basis of strategic retail decision-making and sustainable competitive advantage. 2026 IEEE. -
Characterizing Ultimatum Game responders: a scoping review of factors that influence decision-making through an evolutionary lens
The Ultimatum Game is a widely used tool for studying conflict resolution within a bargaining framework. This scoping review aims to comprehensively examine the various internal and external factors influencing the responders behavior in this game and compile the status quo of the knowledge space. 31 pertinent research articles were identified from databases like Google Scholar, PubMed and JStor, using the following keywords ultimatum game, responder behavior, emotions and the ultimatum game, fairness in the ultimatum game, social norms and the ultimatum game, punishment game, impunity game, outside options in the ultimatum game. An analysis of the same yielded two broad domains of influencing factors: internal and external. Internal factors encompassed emotions, personality traits, and cognitive capabilities, showcasing their significant influence on decision-making. External factors, including ownership, social norms, power dynamics, outside options, gender, and attraction, revealed how the context of the game shaped responder choices. This review investigates how internal and external factors influence bargaining behavior within the Ultimatum Game, distinguishing between typical and atypical responder behavior. Invoking Kahnemans dual system theory offer insights into the evolutionary roots and modern cognitive processes guiding decision-making. The interplay between these systems reveals nuanced responses to fairness, reciprocity, and self-interest, challenging traditional economic models. While acknowledging the oversimplification of brain dynamics in these studies and also the need for cultural integration, the current review compiles a framework that advances our understanding of human behavior across disciplines, particularly for economics, psychology, and evolutionary biology. Refining this model promises deeper insights into decision-making processes amidst societal complexities. Copyright 2026 Chowdhury, Rangaswamy and Kolte. -
Characterizing Context-Dependent Biochar Effects: An ANOVA-Based Study on Soil Properties and Microbial Diversity
Contemporary intensive agriculture has improved food security, but is a detriment to soil health, biodiversity, and long-term sustainability. Biochar is an exciting product derived from the pyrolysis of biomass that possesses great potential to be a soil amendment that can improve soil chemical, physical and biological properties and sequester carbon. This paper summarizes recent international studies (2024-2025) and contains experimental analyses showing how biochar had an effect on soil systems. Considering soil pH, hydrophobicity, porosity, and particle size were emphasized. Our findings indicate that biochar improves soil structure, water retention, nutrient retention, and diversity in microbes, all of which increase crop resilience under abiotic stress conditions. However, there is a context-sensitivity to the utilization of biochar - often changing with soil types, feedstock, pyrolysis, and application rates. By using standardized and characterizing methods in soil characteristics and ANOVA based statistical analysis, this study presents the rationale and insights, opportunities and limitations of biochar as a sustainable soil conditioner. Further, the findings suggest to tailor "designer biochars". It seems plausible that these could be optimized for targeted soil and crop systems, and be a vital tool in developing climate-resilient and sustainable. 2026 IEEE. -
Characterizations of some parity signed graphs
We describe parity labellings of signed graphs: equivalently, cuts of the underlying graph that have nearly equal sides. We characterize the bal-anced signed graphs which are parity signed graphs. We give structural characterizations of all parity signed stars, bistars, cycles, paths and com-plete bipartite graphs. The rna number of a graph is the smallest cut size that has nearly equal sides; we find this for a few classes of graphs. The author(s). -
Characterization, molecular docking, and therapeutic properties of biomaterial obtained from Pangasianodon hypopthalmus
The therapeutic properties of fish have been recognized since ancient times because of the presence of various fatty acids. Oleic acid, which is a mono-unsaturated fatty acid, is known for curing heart-related diseases and promoting brain health. Our study is a first attempt to evaluate the Anti-inflammatory activity, Anti-oxidant activity and perform characterization studies on mucus obtained from Pangasianodon hypopthalmus and explore its therapeutic properties. Anti-inflammatory property evaluation was performed via, albumin denaturation assay showing an 81% inhibition rate and RBC hemolysis assay with a 76.79% inhibition rate, anti-oxidant activity evaluation performed using the DPPH assay showed an 80% radical scavenging activity at 150?g/mL concentration. Overall results showed that anti-inflammatory was highest at 100mg/mL concentration and anti-oxidant activity was highest at 150?g/mL concentration. Characterization studies involved FTIR, NMR and GCMS analysis. Simulation study was performed on selected compound (ligand) against selected targets. From GC-MS analysis, C18 H34 O2 (Oleic acid), was found to be present at higher concentration. Docking studies revealed good binding energy and strong hydrogen bonding between oleic acid and TNF-?, with a binding energy of ?5.3 kcal/mol, thereby demonstrating strong anti-inflammatory activity and wound-healing potency. Further in-vitro and in-vivo analysis has to be performed to develop potential drug for therapeutic purpose. 2026 Visagaa Publishing House. -
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Characterization of thermal damage of skin tissue subjected to moving heat source in the purview of dual phase lag theory with memory-dependent derivative
This investigation is devoted to exhibit the thermal damage of skin tissue exposed to a moving heat source. Modelling of the problem is performed by adopting dual phase lag theory of bio heat transfer in context of memory dependent derivative. Laplace transform technique has been adopted to represent the analytical solutions of temperature and thermal damage of skin tissue. Thermal damages to the tissues are calculated by the extent of the denatured protein employing with the Arrhenius equation. In order to predict the significance of memory dependent derivative approach, computational results of temperature and thermal damage are evaluated in the frame of different kernel functions as well as time-delay. For the purpose of exhibiting the attractiveness of the present model, obtained results are compared with the results corresponding to the absence of memory dependent derivative. Also, the impact of the velocity of moving heat source has been precisely investigated on temperature variation and thermal damage of skin tissue using quantitative results. Authors believe that this study will be helpful to study the thermal treatment of several diseases such as hyperthermia. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Characterization of the Forgotten Topological Index and the HyperZagreb Index for the Unicyclic Graphs
Let G be a molecular graph with V (G) and E(G) be the vertex set and edge set, respectively. Various investigations show that many degree and distance based topological indices are used to exhibit strong intrinsic connection between the molec- ular structures and the physico-chemical properties of the chemical compounds. In this paper, we focus on two degree-based topological indices, namely, the forgotten topological index and the hyper-Zagreb Index expressed as F(G) = P u2V (G) d(u)3 and HM(G) = P uv2E(G)(d(u) + d(v))2, respectively, where d(u) and d(v) are the degrees of the vertices u and v, respectively, in the graph G. We show that the unicyclic graphs can take any even positive integer except 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 26, 28, 30, 34, 36, 38, 42, 46, 50, 50, 54, 58, 62, 66, 70, 74, 78, 86 and 94 for the forgotten index. A comparable result for the hyper-Zagreb index is also presented. 2020 University of Kragujevac, Faculty of Science. All rights reserved. -
Characterization of signed paths and cycles admitting minus dominating function
Let G = (V, E, ?) be a finite signed graph. A function f: V ? {?1, 0, 1} is a minus dominating function (MDF) of G if f(u) + Pv?N(u) ?(uv)f(v) ? 1 for all u ? V. In this paper we characterize signed paths and cycles admitting an MDF. 2020 Azarbaijan Shahid Madani University -
Characterization of product cordial dragon graphs
The vertices of a graph are to be labelled with 0 or 1 such that each edge gets the label as the product of its end vertices. If the number of vertices labelled with 0's and 1's differ by at most one and if the number of edges labelled with 0's and 1's differ by at most by one, then the labelling is called product cordial labelling. Complete characterizations of product cordial dragon graphs is given. We also characterize dragon graphs whose line graphs are product cordial. 2024 Azarbaijan Shahid Madani University. -
Characterization of Negative Exponential Distribution Based on Patil-Seshadri Condition
In this paper, the different characterizations of the negative exponential distribution in the context of the Patil-Seshadri (P-S) condition are analyzed. To support this conclusion, we next show in the case of several continuous probability distributions including the generalized logistic, Laplace, lognormal and more, that under certain conditions they can be seen as a damaged rendition of the negative exponential distribution. The results offer new insights into the ways to continue previous research on how damaged data may appear to follow simpler exponential forms. The paper also presents the theoretical judgments of these characterizations and practical uses in biological frameworks, fund and signal handling where exponential developments and decays are usual scenes. Our research aims to have the following implications to these fields; it provides a fresh view to exponential model. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Characterization of nanocarbon based electrode material derived from anthracite coal
Nanocarbon derivatives (NCD's) have wide range of scope in the field of sensors, supercapacitors and charge storage application. In the present study, anthracite is used as a precursor to synthesis nano-carbon derivatives. One of the important aspects of this study is to intercalate the synthesized NCD's with Li-ion to enhance its electrochemical and optical properties. The prepared NCD with Li-ion interface is used as an electrode material to study charge-discharge capacity and cyclic stability. The NCD shows a specific capacitance 65.4 mF g-1 and retention of capacitance after 200 cycles. However, adding small amount of supportive electrode material with NCD's enhances the capacitance after 160 cycles. The drastic increase in electronic conductivity of NCD's by adding supportive Li-ion permits the electrochemical activity of electrode material to be effectively utilized for practical applications. 2020 IOP Publishing Ltd. -
Characterization of nano-crystalline carbon from camphor and diesel by x-ray diffraction technique
Hydrocarbons are by far the most widespread precursors among carbon sources employed in the production of carbon nanotubes and carbon nanosphers. In the present study, diesel and camphor have been used as precursors for nanomaterials. Carbonaceous soot produced from combustion of diesel in engine shows the presence of significant amount of carbon nanomaterials. The ? band at about 19.28 has been attributed to the presence of amorphous carbon and surface defects in carbon nanotubes. The ? band at about 25.81 corresponds to e2g mode of graphite which is related to vibration of sp2bonded carbon atoms and the presence of ordered carbon nanotubes in diesel soot. The SEM micrographs provide a clear indication that nanoparticle formed in diesel soot are clusters of carbon nanospheres. Energy dispersive spectrum analysis of diesel soot confirms that the soot particles to be composed of primarily carbon and oxygen along with hydrogen. The camphor soot shows ? and ? bands which reveals the presence of crystalline graphitic carbon. The SEM micrographs of camphor show the presence of carbon nanostructures. It is found nanomaterials formed in the diesel soot consists more of disordered carbon, whereas in camphor it is more of ordered graphite like carbon.