1,205 research outputs found
Self-service Systems Performance Evaluation and Improvement Model
Abstract. The paper analyses the topic of service system productivity and profitability. Main focus of the research is self-service area, namely, the increase of ATM network productivity. Paper presents performance evaluation of self-service systems and improvement model for its increasing profitability. This model combines internal and external quality criteria and provides detailed understanding of the main components of productivity evaluation and methods. Using the model it is possible to create evaluation and improvement tools for increasing productivity of self-service systems. Experimental result shows that using the developed productivity model, ANN method and optimization procedure, productivity of ATM cash management could be increased by approximately 33 percent
E-finance-lab at the House of Finance : about us
The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..
Developing banking intelligence in emerging markets: Systematic review and agenda
The current banking industry is heavily dependent on technological artifacts supported by intelligent systems for performance on operational and marketing parameters. However, the attributes for enabling practice between such technological interfaces with managerial adoption are been lagging creating a knowledge gap. To address this, present research surveys the prior work from 1970 to 2020 on intelligent decision support models specific to banking. Subsequently, findings are synthesized on quadrant outcomes; technology; employees, customers, and organizations for service ecosystems. In addition, the managerial perceptions of technology on work are captured through short survey. Finally, scope of advancements like big data, internet of things (IoT), virtual reality (VR) along other untapped conceptual relationships into this framework are discussed
The role of technology in improving the Customer Experience in the banking sector: a systematic mapping study
Information Technology (IT) has revolutionized the way we manage our money. The adoption of innovative technologies in banking scenarios allows to access old and new financial services but in a faster and more secure, comfortable, rewarding and engaging way. The number, the performances and the seamless integration of these innovations is a driver for banks to retain their customers and avoid costly change of hearts. The literature is rich in works reporting on the use of technology with direct or indirect impact on the experience of banking customers. Some mapping studies about the adoption of technologies in the field exist, but they are specific to particular technologies (e.g., only Artificial Intelligence), or vice versa too generic (e.g., reviewing the adoption of technologies to support any kind of banking process). So a specific research effort on the crossed domain of technology and Customer Experience (CX) is missing. This paper aims to overcome the following gaps: the lack of a comprehensive map of the research made in the field in the past decade; a discussion on the current research trends of top publications and journals is missing; the next research challenges are yet to be identified. To face these limitations, we designed and submitted 7 different queries to pull papers out of 4 popular scientific databases. From an initial set of 6,756 results, we identified a set of 89 primary studies that we thoroughly analyzed. A selection of the top 20% works allowed us to seek the most performant technologies as well as other promising ones that have not been experimented yet in the field. Main results prove that the combined study of technology and CX in the banking sector is not approached systematically and thus the development of a new specific research line is needed
A strategic cooperative game-theoretic model for market segmentation with application to banking in emerging economies
Market segmentation is essential to target efficaciously core-segment customers and to obtain a competitive advantage. Firms when confronted by the range of market segments, have difficulty in deciding the core-segment customers who are the most probable purchasers of their product and services. We propose a novel fuzzy group multi-criteria method for market entry and segment evaluation and selection. This proposed method provides a comprehensive and systematic framework that combines bi-level multi-objective optimization with real option analysis (ROA) and fuzzy cooperative n-person game theory. The contribution of the proposed segment evaluation and selection method is fivefold: (1) it addresses the gaps in the marketing literature on the efficacious and effective assessment of market segments; (2) it provides a comprehensive and systematic framework that combines bi-level multi-objective optimization with ROA and fuzzy cooperative n-person game theory; (3) it considers fuzzy logic and fuzzy sets to represent ambiguous, uncertain or imprecise information; (4) it does not insist on consensus but synthesizes a representative outcome based on qualitative judgments and quantitative data; and (5) it is applicable to national and international market segmentation. The practical application of this proposed framework illustrates the efficacy of the procedures and algorithms
Electronic Banking in Ghana: A Case of GCB Bank Ltd
The introductions of electronic banking products and services have changed the nature and way of financial service delivery to customers in Ghana. The purpose of this study is to gain an insight or a deep understanding into electronic banking products and services that customers subscribe to and to find out from both the bank and its customers perspective the benefits they enjoy and the challenges they are faced with whiles providing and accessing the facilities respectively. The methodology used to conduct this research consisted of questionnaires and interviews to collect data. Non-probability sampling which this research adopted and used provided a range of alternative techniques based on researchers’ subjective judgment. SPSS and Microsoft Excel were used for organizing, analyzing and interpreting the data collected. The population of interest was defined as retail customers of GCB Bank Ltd. The sample size for the study was 200 customers and 25 employees of GCB Bank Ltd. Data collection was conducted in four branches of GCB Bank Ltd in Kumasi. The study found out that ATM services were the most popular among users and constituted the main knowledge of customers as far electronic banking was concerned. It was followed by SMS or mobile banking. Similarly, that electronic banking was convenient and saves customers time was the major benefit for which reason most customers used the banks electronic products and currently persist. As a strategy, awareness of GCB electronic banking products and services is very essential to increase customer knowledge and usage since most of GCBs electronic banking products and services are new in Ghana and particularly to its customers. Effective presentations using all forms of media advertising such as leaflets, brochures, web pages etc will be useful to introduce the products and services to its customers and a wider audience. Finally, a high quality internet infrastructure and other systems support services and IT infrastructure should be provided by GCB since it is one of the primary requirements for electronic banking. Keywords: E-banking, Internet banking, ATM, SMS, PO
The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance
Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision. Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness. The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively. A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process
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