Using Artificial Intelligence In Enhancing Banking Services

Elrefai, Aya Tarek; Elgazzar, Mohamed H.; Khodeir, Aliaa Nabil

doi:10.1109/ccwc51732.2021.9375993

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…From only two qualified studies in 2018, this has increased to five each in 2019 and 2020, and it further increased to twelve in 2021 and ten studies in the middle of 2022 (Figure 2). The scope of the studies on the adoption of AI in the banking industry has likewise expanded from focusing mainly on chatbots in the early years, this has extended into various areas of application of AI in the banking industry such as fraud detection, credit and risk management, sales and marketing, investment advisory, data management and automation of back-end services (Ashta & Herrmann, 2021;Belanche et al, 2019;Bhatia et al, 2020;Elrefai et al, 2021;Haddad, 2021;Tiwari & Saxena, 2021)-…”

Section: Discussionmentioning

confidence: 99%

“…These functional areas include customer service, customer relationship management (CRM), fraud detection, credit and risk management, marketing, payment services, back-end operations, and data collection. The most popular application of AI from the customer's perspective is in customer interaction through marketing in the form of interaction with chatbots and roboadvisers, customer service, customer relationship management, and mobile banking (Belanche et al, 2019;Elrefai et al, 2021;Trivedi, 2019;Wicaksono and Zahra, 2022). First used in the banking industry by the Bank of America, a chatbot is an interface that responds to a customer's query typically through text or voice (Hwang and Kim, 2021).…”

Section: Application Of Ai In the Banking Industry By Functional Areamentioning

confidence: 99%

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Artificial Intelligence Adoption in the Banking Industry: Current State and Future Prospect

Lazo,

Ebardo

2023

jim

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AI adoption in the banking industry is a necessity not only to gain a competitive advantage within the industry but more importantly to defend the bank versus technological disruptors that are trying to gain ground in service areas that were previously dominated by banks. This study aims to analyze the current state of AI adoption in the banking industry through a systematic review of related literature. Using the SCOPUS database, thirty-five studies were identified that are relevant to this study. While the number of studies is on increasing trend, the focus of existing literature is predominantly on the customers' and the banks' perspectives. Studies from the regulators' and service providers' perspectives are still sparse despite the importance of these two actors in the adoption of AI in the banking industry. The application of AI has permeated wide areas of the banking function expanding beyond the use of chatbots. Improving customer experience, increasing profitability, and improving competitiveness drive the adoption of AI by banks while people-related issues, technology-related issues, and regulatory-compliance issues deter banks from fully embracing AI. Transparency of algorithms, data privacy, data protection, and fair use of data are the main concerns both of regulators and customers. Results of this study point to the need for more empirical and theoretical research on the regulatory perspective of AI adoption in the banking industry, how to harness the strength of service providers to enable wider and more effective adoption of AI, how institutions can help increase the talent pool for AI-driven technologies, and how to strengthen the synergistic cooperation of the various actors in the adoption of AI in the banking industry.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Ai In the Banking Industry By Functional Areamentioning

confidence: 99%

Artificial Intelligence Adoption in the Banking Industry: Current State and Future Prospect

Lazo,

Ebardo

2023

jim

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“…Banking and financial service are undergoing a major transformation due to the start of AI applications. This study [32] tests various models of machine learning algorithms using various classifiers for banking service making better customer segmentation and enhancing the performance of the sales agents for better recommending the ideal product to the customer and enhancing the overall customer experience. This study [33] proposed a survey and implementation of accurate financial fraud detection under an IoT environment, and the result was that the machine learning method has a higher fraud detection rate than the artificial neural network.…”

Section: Opportunities Of Ai Across Industriesmentioning

confidence: 99%

The Artificial Intelligence (AI) Model Canvas Framework and Use Cases

Nurcahyo

Suroso

Wang

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Artificial Intelligence (AI) has grown increasingly in the past decade. The growth and development bring up several issues for a successful AI project. The AI project requires communication across different domains, like specialists, engineers, data scientists, stakeholders, and ecosystem partners (analytic, storage, labeling, and open-source platforms). It offers numerous vital qualities to give deeper insights into user behavior and give recommendations based on the data. The AI project is hard to define, it requires more than mastery of data, and every enterprise needs guidance and a simple plan on how to use AI. This research creates a wide-view approach of different types of AI Model Canvas for companies that do projects, produce, promote and provide AI technology to organizations. We selected three canvases that represented AI, Machine Learning (ML), and Deep Learning (DL) method. We illustrate and interpret those canvas along with some case studies. We conclude our research by writing the final case report for each use case from the AI model canvas. By filling the one-page Canvas, it will help us explain what AI will provide, how it will interact with humans judgment, and how it will be used to influence decisions, how you will measure success & outcome, and the type of data needed to train, operate, and improve AI. The AI Model Canvas purposed a clear description and differentiation of the roles of stakeholders, customers, and AI strategy. This canvas also can be used in analytical and assembly projects in making new product lines.

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“…Hyperautomation, a relatively new concept in technology, refers to the knowledge work automation across a wide range of business functions and technologies coupled with a responsive workforce, typically integrating Artificial Intelligence (AI) algorithms and Robotic Process Automation (RPA) features (Lasso-Rodriguez and Winkler, 2020). The best strategy for thriving and gaining a competitive advantage is to adopt new technologies (Elrefai et al, 2021). The business services environment has already seized the opportunities offered by artificial intelligence (AI) technologies in terms of increasing competitiveness, reengineering services or rethinking business strategies (Sestino and De Mauro, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…In the present digital era, AI is considered a catalyst for business process transformation, enabling organizations to have real-time analysis, learn more about their customers and forecast future steps (Nosova et al, 2022). Therefore, AI is expanding outside engineering, information technology, and data analytics, gradually becoming a crucial ally of marketing (Liu et al, 2023;Mehta et al, 2022;Ameen et al, 2022;Gao & Liu, 2022;Huang & Rust, 2020), finance and accounting (Mihai & Dutescu, 2022;Shi, 2020), banking (Doumpos et al, 2023;Elrefai et al, 2021) and human resources (Bhat et al, 2022;Charlwood & Guenole, 2022).…”

Section: Introductionmentioning

confidence: 99%

AI – powered Business Services in the Hyperautomation Era

Anica-Popa,

Vrîncianu,

Petrică Papuc

2023

Proceedings of the International Conference on Business Excellence

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Hyperautomation is a business-driven approach, conceptualized in 2019 by Gartner Inc., that combines various technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA) and integrated platforms as a service (iPaas) with the aim of making business processes more efficient by substituting human intervention. Among these, implementations of AI within business services use technologies like Natural Language Processing, Voice and Image Recognition, Virtual Agents, Machine Learning or Deep Learning platforms. Acknowledging this reality, we are interested in developing answers to the following research questions: (1) What are the main categories of business services which integrate specific AI tools? (2) What are the transformed business processes and their operations provided by AI tools? (3) What are the benefits related to AI integrated tools? For this triadic purpose, a systematic literature review on the implementation of Artificial Intelligence in the field of business services was carried out. Only works indexed in the Web of Science database, published in the last 5 years, were selected. Moreover, the websites of the main developers and client companies were investigated. Our findings include a selection of identified AI solutions, structured by main business services categories; we have also outlined the performed tasks and the resulting benefits of each listed AI tool. The synopsis of AI-powered tools presented in the paper could serve professionals, managers and researchers in designing future policies, operational procedures and research approaches to cope with new challenges of disruptive technologies from the AI spectrum.

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Using Artificial Intelligence In Enhancing Banking Services

Cited by 4 publications

References 10 publications

Artificial Intelligence Adoption in the Banking Industry: Current State and Future Prospect

Artificial Intelligence Adoption in the Banking Industry: Current State and Future Prospect

The Artificial Intelligence (AI) Model Canvas Framework and Use Cases

AI – powered Business Services in the Hyperautomation Era

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