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The Role of AI in Disrupting Traditional Banking and Financial Services: Harnessing Data Analytics and Machine Learning for Competitive Advantage
Abstract
The advent of artificial intelligence (AI) has revolutionized traditional banking and financial services by leveraging advanced data analytics and machine learning algorithms. This paper explores how AI disrupts conventional banking paradigms, offering institutions a competitive edge through enhanced customer experiences, risk management, and operational efficiency. By harnessing vast amounts of data, AI algorithms can predict customer behavior, personalize services, and detect fraudulent activities in real-time. Moreover, machine learning models optimize lending decisions, streamline back-office operations, and automate routine tasks, reducing costs and improving scalability. However, AI implementation comes with challenges such as data privacy concerns, algorithmic biases, and regulatory compliance. Successful integration requires a strategic approach encompassing talent acquisition, infrastructure development, and stakeholder collaboration. Overall, AI represents a transformative force in the financial sector, reshaping business models and redefining industry standards. Organizations that embrace AI technologies stand to gain a sustainable competitive advantage in the evolving landscape of banking and financial services.
... Efforts to develop explainable AI techniques are ongoing to address this challenge and improve the transparency of AI-driven AML systems. Another challenge is the potential for algorithmic bias [10], [11]. Machine learning models trained on historical data can inadvertently perpetuate existing biases present in the data. ...
... Data fusion techniques will enable the combination of structured and unstructured data from multiple sources, providing a holistic view of potential risks. This multi-source approach will enhance the ability to detect sophisticated laundering techniques and improve the overall effectiveness of AML measures [11], [10]. Explainable AI and Transparency: As regulatory scrutiny on AI-driven systems intensifies, there will be a growing emphasis on developing explainable AI models. ...
Integrating artificial intelligence (AI) and data mining into anti-money laundering (AML) solutions represents a significant leap forward in combating financial crime. Traditional AML methods, often reliant on manual processes and rule-based systems, struggle with the vast and complex data associated with financial transactions. AI and data mining technologies offer advanced capabilities for analyzing large volumes of data, detecting patterns, and identifying suspicious activities with greater precision. Machine learning algorithms can learn from historical data to recognize subtle anomalies and emerging trends indicative of money laundering, enhancing the effectiveness of AML efforts. Data mining techniques enable the extraction of meaningful insights from diverse datasets, such as transaction records, customer profiles, and external data sources. This integration not only improves the accuracy of detecting illicit activities but also streamlines the investigation process by automating routine tasks and prioritizing high-risk cases. However, the implementation of AI and data mining in AML also presents challenges, including the need for robust data governance, privacy concerns, and the potential for algorithmic bias. Addressing these issues is crucial for developing effective and fair AML solutions. This paper explores the benefits and challenges of integrating AI and data mining into AML practices, highlighting their potential to enhance financial crime prevention and compliance.
... Similar observations were made by Denning (2023) who argued that artificial intelligence (AI) plays a crucial role in data analysis and insight generation, giving organizations useful insights and predictions to guide decision-making. In the banking industry, Abbas (2024) claimed that machine learning models might save costs and improve scalability by automating repetitive procedures, optimizing loan decisions, and streamlining back-office operations. According to Denning (2023), artificial intelligence and machine learning have the potential to enhance human abilities and elevate the standard of output, freeing up workers to concentrate on more important duties that call for creativity and analytical thinking. ...
In today's fast-paced and ever-evolving business landscape, effective risk management and pricing strategies have become crucial for organisations to stay competitive and maximise profitability. In order to effectively manage risks, identify possible threats, 250 and seize opportunities, businesses need to take a proactive and holistic approach to risk management. The current business landscape has left businesses of any size or type, anywhere in the world, facing a wide range of risks which could cause them long-term harm, from financial penalty to reputational damage. Through a systematic review of the literature, this book chapter delved into the ways in which technology is being leveraged to ensure business resilience in risk management, exploring insights from different perspectives and providing an in-depth understanding of the options available. This book chapter also proposed strategic approaches to leveraging and integrating these technologies and innovations into effective risk management and ensure business continuity and resilience.
... AI algorithms can analyze large volumes of data from various sources, including transaction records, customer profiles, and external data feeds, to identify potential risks and assess their impact. By leveraging machine learning models, AI systems can detect patterns and anomalies that might indicate money laundering activities, allowing financial institutions to proactively address these risks before they escalate [10]. ...
The fight against money laundering has become increasingly complex with the rise of sophisticated financial crimes and the global reach of illicit activities. Traditional Anti-Money Laundering (AML) systems rely heavily on rule-based frameworks that often struggle to adapt to the evolving tactics of criminals. In this context, integrating Artificial Intelligence (AI) and predictive analytics represents a transformative approach to modernizing AML practices. AI can enhance the efficiency and accuracy of AML efforts by automating complex processes, detecting anomalies in real-time, and reducing false positives. Predictive analytics, leveraging vast datasets and machine learning algorithms, can proactively identify patterns indicative of potential money laundering activities, enabling financial institutions and regulators to intervene before illicit transactions occur. The integration of AI-driven models into AML systems provides several advantages over conventional methods. Machine learning algorithms can continuously learn from new data, adapting to emerging threats and identifying previously undetected risks. Additionally, AI can help automate the labor-intensive aspects of AML, such as customer due diligence, transaction monitoring, and regulatory reporting, freeing up human analysts to focus on higher-level decision-making. Predictive analytics further empowers AML frameworks by enabling the forecasting of suspicious behavior based on historical data and real-time transaction analysis, allowing for more proactive risk management. Despite the potential benefits, the implementation of AI and predictive analytics in AML also presents challenges, such as the need for high-quality data, algorithmic transparency, and regulatory compliance.
... This adaptability will be essential for maintaining effectiveness in a rapidly changing environment and for addressing unforeseen challenges. In summary, the future of AI and predictive analytics in financial services promises significant advancements that will enhance operational efficiency, decision-making, and customer experiences [14], [15]. As technology evolves, financial institutions will need to address challenges related to transparency, bias, and regulatory compliance while embracing opportunities for innovation and collaboration. ...
Operational efficiency in financial services is increasingly being enhanced through the integration of artificial intelligence (AI) and predictive analytics. Traditional financial operations, characterized by manual processes and static decision-making, are being transformed by AI technologies that automate routine tasks, optimize workflows, and provide actionable insights. Predictive analytics further augments these capabilities by analyzing historical data to forecast future trends, enabling financial institutions to make informed decisions and anticipate market movements. AI-driven automation improves accuracy and reduces operational costs by streamlining processes such as transaction processing, fraud detection, and customer service. Predictive models enhance decision-making by identifying potential risks and opportunities, allowing institutions to proactively manage their resources and strategies. This combination of AI and predictive analytics not only increases operational efficiency but also enhances customer experiences and supports strategic planning. However, the implementation of these technologies presents challenges, including data privacy concerns, integration complexities, and the need for skilled personnel. Addressing these challenges is crucial for maximizing the benefits of AI and predictive analytics in financial services. This paper explores how leveraging these technologies can drive operational efficiency and outlines key considerations for successful implementation.
Financial inclusion is crucial for both economic growth and decreasing poverty levels, however, around 1.7 billion adults worldwide still do not have access to banking services. AI-driven financial technologies offer creative solutions to close this divide through offering easy, cost-effective, and effective financial services. This chapter delves into the present situation of financial inclusion, the main AI technologies driving changes, successful examples, obstacles, and future outlook. Through the use of artificial intelligence, we have the ability to develop a financial system that is more inclusive, giving power to individuals and promoting economic growth.
In today's fast-paced and competitive business environment, companies are increasingly relying on advanced machine learning (ML) algorithms and predictive analytics to gain actionable insights into market trends and consumer behavior. The application of these technologies enables businesses to analyze vast amounts of historical and real-time data, predict future market movements, and make informed decisions. This paper explores the integration of machine learning techniques such as supervised learning, unsupervised learning, and deep learning, in combination with predictive analytics methods, to enhance market analysis. By leveraging these technologies, organizations can identify patterns in consumer preferences, optimize pricing strategies, and forecast demand with greater accuracy. Machine learning algorithms can process structured and unstructured data from various sources, including social media, sales records, and customer interactions, to create robust predictive models. Predictive analytics then utilizes these models to anticipate future outcomes, such as market shifts, customer churn, and product demand, thus providing a competitive edge. Furthermore, the incorporation of real-time analytics allows businesses to adjust their strategies dynamically, ensuring agility and responsiveness to changing market conditions. This research also examines the challenges and limitations of applying machine learning and predictive analytics in market analysis, such as data quality, algorithm bias, and the need for domain expertise. Despite these challenges, the potential benefits in terms of improved decision-making, increased efficiency, and enhanced customer satisfaction are significant. Ultimately, the fusion of machine learning and predictive analytics represents a powerful approach to transforming market analysis into a more strategic and data-driven process.
The integration of Artificial Intelligence (AI) in business consulting has transformed decision-making processes, offering data-driven insights through predictive analytics. However, this advancement comes with ethical dilemmas, including bias, privacy concerns, and accountability. This paper explores the challenges associated with ethical AI in predictive analytics and provides strategies for businesses to navigate them. It emphasizes the importance of maintaining data integrity, transparency, and fairness while utilizing AI tools for forecasting and decision support. By examining real-world case studies, the paper highlights the consequences of overlooking ethical principles and the benefits of implementing responsible AI practices. Furthermore, it discusses the role of regulatory frameworks and organizational policies in ensuring compliance with ethical standards. The paper concludes by recommending a multi-stakeholder approach, integrating AI ethics into corporate governance to promote sustainable business practices. Addressing these challenges is critical for building trust, enhancing business value, and driving innovation in a competitive landscape. Moreover, maintaining compliance with data protection regulations such as GDPR is essential to safeguarding consumer privacy and maintaining the ethical standards of data usage. The responsibility of AI-powered systems in business consulting extends beyond just the accuracy of predictions to the broader impact these decisions can have on individuals, communities, and society at large. Businesses must prioritize ethical AI governance frameworks that balance innovation with responsibility. By adopting AI with a focus on ethical standards, businesses can harness the power of predictive analytics to enhance their decision-making processes while preserving integrity, fairness, and trust with customers and stakeholders.
In the rapidly evolving landscape of data-driven decision-making, cloud-based Business Intelligence (BI) platforms have emerged as essential tools for organizations seeking to gain a competitive edge. SnowflakeDB, with its unique architecture and advanced features, has revolutionized the way businesses manage, process, and analyze data in the cloud. This paper explores the potential of SnowflakeDB as a transformative tool for enabling scalable, secure, and efficient BI solutions. It highlights key benefits such as seamless data integration, elasticity, and near-infinite scalability, empowering enterprises to extract actionable insights in real-time. Furthermore, the discussion delves into Snowflake's multi-cluster shared data architecture, which enables concurrent workloads without compromising performance, thereby catering to diverse business needs. Practical use cases, including customer behavior analysis, predictive analytics, and supply chain optimization, are examined to illustrate the tangible benefits of leveraging SnowflakeDB for BI. By combining advanced data warehousing capabilities with cloud flexibility, SnowflakeDB offers a compelling proposition for businesses aiming to future-proof their BI infrastructure. Organizations adopting SnowflakeDB can unlock enhanced decision-making, improved operational efficiency, and sustained competitive advantage in today's dynamic market environment. With SnowflakeDB, businesses can adopt advanced analytics techniques, such as predictive analytics and machine learning, to enhance forecasting, optimize operations, and improve decision-making. SnowflakeDB revolutionizes business intelligence by providing a flexible, scalable, and powerful platform for data analytics. By harnessing its capabilities, organizations can not only streamline their BI processes but also unlock new opportunities for growth and innovation.
The integration of Business Intelligence (BI) with Generative Artificial Intelligence (GenAI) is a transformative approach to advancing decision-making in organizations. By combining BI's data-driven insights with the creative capabilities of GenAI, businesses can unlock novel solutions for enhancing operational efficiency, innovation, and strategic planning. This fusion not only enables more accurate predictions but also empowers organizations to generate actionable insights from vast datasets in real-time. The potential of GenAI to simulate various scenarios and suggest optimized solutions adds an extra layer of sophistication to BI, leading to more adaptive and intelligent decision-making processes. However, as these technologies evolve, ethical considerations must be at the forefront. The reliance on AI-generated insights raises important questions about transparency, bias, and accountability. Ensuring ethical practices in the deployment of GenAI within BI frameworks is crucial to avoid unintended consequences and to foster trust among stakeholders. Businesses must be proactive in establishing clear guidelines for the ethical use of AI, focusing on fairness, privacy, and the minimization of biases in decision-making processes. This paper explores how the combination of BI and GenAI can revolutionize business operations while emphasizing the importance of ethical frameworks to guide their integration. By understanding both the opportunities and risks, organizations can harness the full potential of these technologies while maintaining responsibility and fairness.
In the era of digital transformation, the integration of Artificial Intelligence (AI) and data analytics has emerged as a game-changer in competitive intelligence, redefining how businesses gather, process, and utilize market insights. This paper explores the transformative role of AI-driven tools and data-centric strategies in providing organizations with actionable intelligence to navigate competitive landscapes effectively. By leveraging advanced technologies such as machine learning, natural language processing, and predictive analytics, businesses can uncover hidden patterns, anticipate market trends, and make informed decisions with unprecedented accuracy and speed. AI-powered systems enable the automation of labor-intensive tasks such as competitor monitoring, sentiment analysis, and market segmentation, allowing businesses to allocate resources more efficiently. Moreover, real-time analytics facilitates dynamic adaptation to shifting market conditions, empowering organizations to maintain a competitive edge. Data visualization and dashboarding tools provide stakeholders with intuitive insights, fostering data-driven decision-making across all organizational levels. The paper also highlights the challenges of adopting AI and data analytics, including data privacy concerns, integration complexities, and skill gaps, while offering strategic recommendations for overcoming these hurdles. Case studies from diverse industries illustrate the tangible benefits of adopting AI-powered competitive intelligence, emphasizing its potential to drive innovation, enhance customer engagement, and optimize business strategies. As businesses strive to thrive in an increasingly volatile market environment, AI and data analytics are not merely tools but essential enablers of sustainable growth and competitive advantage.
Generative Artificial Intelligence (AI) and data mining are transforming how businesses analyze markets, uncover trends, and drive growth. These technologies enable organizations to harness large volumes of unstructured and structured data, generating actionable insights with unprecedented speed and accuracy. By leveraging generative AI, businesses can create predictive models that simulate market scenarios, customer behaviors, and competitive dynamics. Data mining complements this by identifying patterns and relationships within datasets, providing a solid foundation for strategic decision-making. Together, these tools empower companies to enhance customer engagement, optimize marketing strategies, and uncover new revenue streams. This paper explores the synergies between generative AI and data mining, focusing on their applications in market analysis and business growth. It examines case studies highlighting how leading enterprises have utilized these technologies to achieve competitive advantages. Challenges such as data quality, algorithmic biases, and integration with legacy systems are also addressed, along with solutions to overcome them. The study concludes by emphasizing the importance of adopting these technologies as part of a broader digital transformation strategy to stay ahead in today's data-driven economy. Businesses that effectively implement generative AI and data mining can not only anticipate market trends but also adapt to changing demands, ensuring sustained growth and innovation.
The integration of AI technologies, including machine learning, chatbots, and virtual assistants, has revolutionized decision-making processes, enhanced analytical capabilities, and automated various business operations. The review emphasizes the numerous benefits of AI, ranging from improved customer experiences and personalized banking to efficient fraud detection and risk mitigation. Despite the many advantages, Gen-AI’s ethical considerations, algorithmic bias, transparency, and potential job displacement pose challenges that warrant careful assessment. This study explores the transformative impact of Gen-AI on the financial services industry, focusing on key applications such as customer relationship management, fraud detection, algorithmic trading, and risk management. It aims at studying how does Gen-AI affect the market share of the most innovative banks in Lebanon; how does Gen-AI play its role in moderating the relationship between innovative financial processes and the banking sector in response to the rise of the Fintech organizations.
This review research paper investigates the dynamic relationship between regulatory reforms and bank profitability in the post-financial crisis era. The aftermath of the 2008 global financial crisis prompted significant regulatory changes globally, with the aim of fortifying the banking sector and preventing future systemic failures. This paper synthesizes empirical evidence to discern the impact of these regulatory reforms on bank profitability, offering insights into the nuanced interactions between regulatory frameworks and financial institutions. The review begins by contextualizing the regulatory landscape post-2008, outlining key reforms such as Basel III, Dodd-Frank Act, and other jurisdiction-specific initiatives. It provides a comprehensive overview of the structural and prudential measures implemented to enhance financial stability, transparency, and risk management within the banking sector. Subsequently, the paper synthesizes empirical studies that assess the impact of regulatory reforms on various dimensions of bank profitability. It explores how changes in capital adequacy requirements, liquidity standards, and risk management practices influence net interest margins, return on assets, and return on equity. The analysis also considers the role of regulatory compliance costs and the potential trade-offs between increased stability and reduced profitability. Furthermore, the review addresses the heterogeneity in the impact of regulatory reforms across different types of banks, including large systemically important institutions and smaller regional banks. It investigates how the regulatory environment interacts with bank-specific characteristics, business models, and risk profiles, shaping the overall profitability dynamics within the banking industry. The paper concludes by synthesizing the main findings, identifying gaps in the existing literature, and suggesting avenues for future research. By shedding light on the intricate relationship between regulatory reforms and bank profitability, this review contributes to the ongoing discourse on the effectiveness and unintended consequences of post-financial crisis regulatory measures.
As financial landscapes continue to evolve, Central Bank Digital Currencies (CBDCs) have emerged as a focal point in the discourse surrounding the future of money and banking. This review paper delves into the multifaceted dimensions of CBDCs, exploring their implications, challenges, and potential transformative impacts on the global financial ecosystem. By synthesizing a diverse array of academic literature, policy documents, and industry reports, this research aims to provide a comprehensive overview of the current state and future trajectories of CBDCs. The paper begins by delineating the conceptual underpinnings of CBDCs, elucidating 1 the distinctions between retail and wholesale variants, and examining the motivations driving central banks towards their development. It navigates through the technological architectures underpinning CBDCs, assessing the role of blockchain and distributed ledger technologies in enhancing transparency, security, and efficiency within financial transactions. An in-depth analysis of the potential implications and challenges associated with CBDC adoption is presented, encompassing aspects of monetary policy, financial stability, privacy, and the reshaping of traditional banking models. The exploration of cross-border considerations sheds light on the potential for CBDCs to facilitate international transactions and redefine the dynamics of global finance. The review also considers the broader societal impacts, contemplating the democratization of financial services, financial inclusion, and the potential shift in the power dynamics between central banks and commercial banks. Regulatory perspectives and the evolving stance of different nations towards CBDCs are scrutinized to understand the global regulatory landscape surrounding these digital currencies. As CBDCs continue to be piloted and researched by central banks worldwide, this review serves as a timely and insightful resource for policymakers, academics, and industry practitioners seeking a comprehensive understanding of the intricate web of factors surrounding the emergence, adoption, and potential future scenarios of Central Bank Digital Currencies.
Artificial Intelligence is a significant area of computer science that is expanding into several industries. Understanding the intricacies of AI and its integration into several commercial applications is crucial due to its complexity and extensive scope. This paper aims to examine Artificial Intelligence and its use in Enterprise Resource Planning (ERP). This study provides an extensive and in-depth examination of Artificial Intelligence, machine learning, deep learning, and neural networks. This research is a literature review that examines various papers and online articles about Artificial Intelligence in Enterprise Resource Planning (ERP). The research concludes that firms achieve a higher level of analytical productivity in many ERP domains due to significant advancements in AI, machine learning, and deep learning, thus demonstrating the influence of AI. A number of ERP domains are heavily using AI, with sales forecasting, predictive analysis, and customer service being three of the most prominent.
This paper examines the transformative role of Workday Financials within the broader context of cloud-based Enterprise Resource Planning (ERP) adoption, highlighting its significant implications for organizational efficiency, scalability, and innovation. By leveraging cloud technology, Workday Financials offers a comprehensive solution that enhances financial management, fosters collaborative work environments, and provides predictive analytics for strategic decision-making. The study underscores the economic benefits of transitioning to cloud-based ERP systems, including cost efficiency, improved data visibility, and strengthened security measures. Through qualitative analysis and industry case studies, the research delineates how Workday Financials not only streamlines operational processes but also contributes to the digital transformation agenda, propelling businesses and the U.S. economy toward a future of increased competitiveness and resilience.
As artificial intelligence (AI) systems become increasingly ubiquitous and influential, ensuring their safe, secure, and trustworthy development and deployment is of paramount importance. This paper explores the multifaceted challenges and considerations involved in fostering a robust AI ecosystem in the United States. It delves into key aspects such as ethical considerations, technical robustness and security, data privacy and security, and strategies for building public trust. The paper presents a comprehensive analysis of these issues, supported by relevant research and best practices from various stakeholders. Additionally, it provides recommendations and highlights existing initiatives aimed at promoting responsible AI development and deployment. Furthermore, the paper includes three block diagrams to visually represent the technical robustness and security considerations, data privacy and security concerns, and the importance of stakeholder engagement and public trust in AI systems. By addressing these critical aspects, the United States can harness the transformative potential of AI while mitigating risks and upholding ethical principles, ultimately positioning itself as a global leader in responsible AI innovation. INTRODUCTION Artificial Intelligence (AI) has rapidly evolved from a niche technological domain to a transformative force reshaping various aspects of our lives, ranging from healthcare and transportation to finance and national security. As AI systems become increasingly sophisticated and integrated into critical decision-making processes, it is imperative to ensure their safe, secure, and trustworthy development and deployment. The United States, as a global leader in AI innovation, recognizes the urgency of establishing a robust framework to harness the immense potential of AI while mitigating its risks and addressing ethical concerns [1].
In the rapidly evolving landscape of financial services, Financial Technology (Fintech) has emerged as a transformative force, reshaping the future of banking services. This review paper provides a comprehensive analysis of the role of Fintech in revolutionizing traditional banking practices, exploring its impact on customer experiences, financial inclusion, regulatory frameworks, and the overall trajectory of the banking industry. The review begins by tracing the historical evolution of Fintech and its intersection with traditional banking. It delves into the disruptive technologies driving Fintech innovation, including blockchain, artificial intelligence, and mobile applications, and their transformative effects on the efficiency, accessibility, and scope of banking services. Furthermore, the paper analyzes the influence of Fintech on customer experiences, emphasizing the shift towards digital platforms, personalized services, and real-time transactions. It explores how Fintech has democratized financial services, enabling broader access to banking solutions and fostering financial inclusion, particularly in underserved populations. The regulatory landscape is a critical focus of this review, examining how authorities are adapting to the dynamic nature of Fintech. It scrutinizes the challenges and opportunities posed by regulatory frameworks, highlighting the need for balance between innovation facilitation and risk management. Moreover, the paper investigates the emergence of new banking models such as neobanks and the collaborative relationships between traditional banks and Fintech startups. It explores how these partnerships have synergized to offer innovative solutions while navigating challenges related to competition, data security, and regulatory compliance. The paper concludes by forecasting the future trajectory of banking services in the era of Fintech dominance. It discusses the potential impact on job roles, business models, and the overall stability of the financial system, emphasizing the imperative for continuous adaptation and collaboration in this dynamic ecosystem. This paper provides valuable insights into the multifaceted role of Fintech in shaping the future of banking services, offering a holistic perspective on the opportunities, challenges, and transformative potential of this technological revolution. The banking landscape has undergone a transformative evolution in recent years, driven by the relentless march of financial technology, or Fintech. The convergence of cutting-edge technologies and financial services has ushered in a new era, fundamentally reshaping the way banking services are conceptualized, delivered, and experienced. This review explores the pivotal role of Fintech in THE ROLE OF FINTECH IN SHAPING THE FUTURE OF BANKING SERVICES 188 shaping the future of banking services, unraveling the multifaceted impacts and transformative potential that have positioned Fintech as a driving force in the financial sector. The emergence of Fintech has disrupted traditional banking paradigms, challenging established norms and fostering an environment of innovation and competition. As a fusion of technology and finance, Fintech encompasses a diverse range of services, including digital payments, peer-to-peer lending, robo-advisors, blockchain-based solutions, and mobile banking applications. These innovations have not only revolutionized the customer experience but have also engendered a seismic shift in the operational frameworks of financial institutions. One of the key areas where Fintech has exerted its influence is in enhancing financial inclusion. The ubiquity of smartphones and the democratization of access to financial services have empowered individuals previously excluded from traditional banking systems. Fintech has pioneered solutions that transcend geographical barriers, providing the unbanked and underbanked populations with unprecedented access to essential financial tools and services. Moreover, the review delves into the impact of Fintech on the efficiency and cost-effectiveness of banking operations. Automation, artificial intelligence, and data analytics have streamlined processes, reducing operational costs and enhancing the speed and accuracy of financial transactions. This efficiency dividend has not only benefited financial institutions but has also translated into improved services for customers. In addition to exploring the positive aspects, this review critically assesses the challenges and risks associated with the integration of Fintech in banking services. Issues such as data security, regulatory frameworks, and the potential concentration of financial power are examined to provide a balanced understanding of the Fintech-driven banking landscape. As we stand on the precipice of a banking revolution, understanding the intricate dynamics between Fintech and traditional banking becomes imperative. This review contributes to the ongoing discourse by synthesizing existing knowledge, offering insights into the transformative potential of Fintech, and paving the way for informed discussions on the future trajectory of banking services.
The advent of Industry 4.0 has brought about a revolution in the Industrial Internet of Things (IIoT) driving digital transformation, which now includes data analytics, cloud computing, artificial intelligence, and mobile connectivity. This paper delves into the effectiveness of existing privacy protection measures, as well as the challenges and opportunities presented by emerging technologies such as unified encryption and machine learning. Additionally, the paper provides insights into the processes required for industry-specific compliance with relevant laws and regulations. The findings emphasize the crucial role privacy plays in AI applications for IIoT systems and shed light on the strategies, obstacles, and prospects that organizations must navigate in this rapidly evolving landscape.
In an example embodiment, machine learning is utilized to automatically identify pain points of an entity. More particularly, a random forest search algorithm receives inputs from a number of different outside-in data engines. These inputs are segregated in terms of key performance indexes (KPIs), which is then fed to a random forest decision tree. The random forest search algorithm calculates a p-value for each KPI. This p-value may then be used to identify the most relevant KPIs (such as by ranking the KPIs by their respective p-values). Rules are utilized not to identify the pain points themselves, but instead to define sorting preferences (refine the ranking). Based on a combination of the p-values and the evaluation of the rules, the top-N pain points may be identified.
Businesses are moving online, opening up more channels for growth and a wider customer reach. With a global market, there is a need for a streamlined and automatic payment system. Integrating ERP systems and automatic payment solutions offers an efficient way to carry out financial management, which is crucial for business growth. ERP systems have evolved with technological advancement. Today, ERP incorporates cloud computing, artificial intelligence, and data analytics technologies to facilitate automation in ERP payment systems. Blockchain is another technology that FinTech companies can explore to enable automatic payment processing in ERP systems. Oracle Netsuite, a SaaS company, currently offers an ERP payment system, which is a great reference for the study.
Ghana, like most other developing countries, is not isolated from the global financial crisis through the impact of such a crisis on economies. This paper examines the financial sector reforms and its effect on the Ghanaian economy, within the developing country context in general and in sub-Saharan Africa (SSA) in particular. The paper seeks to enhance the understanding of relevant policy measures and reflects on what else could be done. The article further studies the effect of change in the institutional environment on bank governance practices primarily to improve the industry’s supervision and regulation, related to the post-crisis exit strategies. This paper discusses the development of ICT infrastructure and application as a basis for the main dimension of Ghana’s digital transformation in financial services. This paper is, therefore, motivated by the lack of empirical studies that examines how the impact of the banking reforms play a substantial role in promoting innovative digital payment systems to replace cash transactions. From the perspective of institutional theory, the study looked at why (and how) a number of policy measures have a significant impact on the financial performance of banks? And how the applications of e-finance in ICT and financial practices, provides several benefits within the banking sector improve the sector’s image and leads to a broader, faster and more efficient market? The application of Koppenjan and Groenewegen (2005) ‘s four-layer model ‘levels of institutional analysis’ perspective seems to be the most useful starting point, which provides the basis for an improved understanding of revealing the inefficient delivery of Ghanaian banking industry in the past. A combination of a review of secondary and empirical data, interviewed used in the analysis. Findings indicate that the financial and banking sector reforms help the industry advance digital banking culture and impact on the general expansion of the financial and the infusion of financial inclusion in Ghana. These conclusions would be particularly useful in a similar picture in other developing countries, as well as by the bank authorities to create their future policy. It also joins the debate on the impact of the banking reform, a key turning point towards better regulation to refine crisis prevention and resolution mechanisms.
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