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Bharath Somu

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Papers by Bharath Somu

Agentic AI and Financial Compliance: Autonomous Systems for Regulatory Monitoring in Banking

European data science journal, 2024

With the rapid development of smart and autonomous systems, the rise of increasingly autonomous s... more With the rapid development of smart and autonomous systems, the rise of increasingly autonomous software and hardware agents has raised concerns for societies in terms of ethics, regulation, accountability, and law enforcement. Agentic AI refers to those artificial intelligence systems that have some level of autonomy and can act on behalf of a human user in a certain context. Industries are already relying heavily on agentic AI to monitor regulation compliance of businesses through natural language processing and a knowledge graph of regulations. Similar AI agents can be deployed in the financial industry to ensure rules compliance. The financial industry is highly regulated. Financial market and institution supervision is crucial but requires astronomical cost and human effort. To lessen the burden, many regulatory agencies already publish guidelines for horizontal regulation to ease compliance. The resulting regulatory text flood is unprecedentedly big and complex and thus serves as great risk for financial misconduct. Banks, as the major subject of compliance, have already deployed advanced models to extract structured information from these regulations. Rules extraction and representation leverages a knowledge graph on regulations with multi-level granularity, knowledge schema development, and automated rule generation based on reinforcement learning techniques. Nevertheless, implementing compliance measures against this knowledge graph requires tremendous human effort on rule coding that costs institutions millions of dollars annually. Compliance rule coding varies across financial institutions and must be ingrained into internal policies, which rules out a consolidated solution. Thus, a novel approach to autonomous compliance implementation against the knowledge graph of regulations is unmet and essential. To achieve goal-oriented agentic AI agent design, a potential solution needs to serve as a benchmark testing ground with the capped regulation space and benign financial domain for human-like compliant AI agents. Regulators can monitor automated compliance efforts and intervene if exploitations are discovered, and a regulatory sandbox is thus proposed.

Towards Self-Healing Bank IT Systems: The Emergence of Agentic AI in Infrastructure Monitoring and Management

American Advanced Journal for Emerging Disciplinaries, 2023

Modern banking IT infrastructures are becoming increasingly complex, distributed, and mission-cri... more Modern banking IT infrastructures are becoming increasingly complex, distributed, and mission-critical, necessitating robust solutions for real-time monitoring, fault detection, and autonomous recovery. This paper explores the transformative potential of agentic artificial intelligenceautonomous, proactive, and adaptive AI agents-in realizing self-healing capabilities within banking IT ecosystems. We examine the limitations of traditional monitoring and management systems and highlight how agentic AI leverages machine learning, systems thinking, and continuous feedback loops to anticipate issues, orchestrate interventions, and ensure operational continuity. Case studies and conceptual frameworks are presented to illustrate the deployment of such agents in dynamic environments, including cloud-native architectures and legacy system integrations. The paper outlines the benefits, challenges, and governance implications of adopting agentic AI, ultimately making the case for a paradigm shift in how financial institutions approach infrastructure resilience.

Agentic AI in Retail Banking: Redefining Customer Service and Financial Decision-Making

Journal of Artificial Intelligence and Big Data Disciplines, 2024

Artificial Intelligence (AI) has become one of the most dominant enablers of digital transformati... more Artificial Intelligence (AI) has become one of the most dominant enablers of digital transformation across a significant number of industries in recent years. With the application of AI technology including natural language processing (NLP), creating human-like chat bots has become easier. Today, a greater portion of India banks have switched over to Chat Bot technology for their customer on-boarding, queries, complaints, fund transfers, etc., due their proficiency in handling numerous queries with high response time and round the clock service. This case study is based on the virtual assistant of State Bank of India (SBI)-State Bank Intelligent Assistant (SIA), which is engaged in providing personalized service to users based on their historical preferences, search frequency etc., via analyzing the data with the assistance of AI techniques. The recent developments and emergence of Virtual Banking in India and the current trends in the modern banking systems are explained along with the features of SBI-SIA virtual assistant. As banking is reshaped by technology, financial stability is a key priority for the Bank of England. The Bank is engaging with FinTech companies to gather information and understanding on the financial stability risks that might emerge from FinTech developments. Such risks are expected from the explosion of technology-enabled financial services. It has now become common for many banks to integrate FinTech, machine learning and AI into their services because customers want more choices, flexibility and control over their banking. AI is a branch of FinTech but not every FinTech is AI. AI is machine intelligence. Machine learning and AI are often treated as synonyms. AI in retail banking is at its nascent stage in the UK, though the potential is extraordinary. In the UK, some banks have launched banking applications using voice recognition. The Royal Bank of Scotland (RBS) has decided to roll out its "Luvo" AI customer service assistant, powered by its partner firm, RBS Group, wider in its branches. Bank of America, Capital One, Société Générale and Swedbank are some of the banks that have experimented with chatbots.

Transforming Core Banking Infrastructure with Agentic AI: A New Paradigm for Autonomous Financial Services

Advances in Consumer Research, 2025

Digital transformation has been changing the landscape of banking and the future of banking will ... more Digital transformation has been changing the landscape of banking and the future of banking will be very much different from what it is today. Banks are required to put in place process automation that will gain the confidence of its customers. The financial sector is becoming the least trusted sector. Financial services must embrace trust in its core business model in order to overcome this negative perception. Future of banking will include autonomous systems that must ensure trust at its core processing. The core of this technology ensures provenance, data integrity, auditability and trust. AI is already transforming the landscape of banking and will be one of the greatest driving forces of banking's transformation in the years ahead. The widespread incorporation of AI will give rise to a new era of banking, sometimes referred to as Intelligence Banking, which will be characterised by radical changes to banking products and services, processes, and their respective ecosystems. AI is a broad term that describes machines and systems that can learn from experience and act without human intervention. This capability, in conjunction with an increased availability of and access to vast amounts of data, is reshaping virtually every industry, banking included. Important benefits have already been achieved by investing in AI, with a wide range of applications emerging that span from front to back office. AI has the potential to enhance not only efficiency, but also vital parts of the value proposition such as trust, confidence, personalization, and resilience. Overall, it has the potential to improve productivity growth and offer new opportunities for innovation in the banking sector. ...

AI and Machine Learning for Predictive Banking: Infrastructure Challenges and Opportunities

International Journal of Science and Research, 2022

The volumes of data are unprecedented, both in numbers and in the variety of their formats. Such ... more The volumes of data are unprecedented, both in numbers and in the variety of their formats. Such a vast amount of data has opened up many opportunities in many fields including finance. In the financial services space, the rise of high-frequency trading accounts for the explosion of data combining price, liquidity, and news indicators reserves to explore trading opportunities. On a longer horizon, the aggregation of individual investor behavior and their sentiment in association with liquidity, volatility and expected returns constitutes a potential pool for risk assessment, equity market studies, and financial marketing. Such big data also bring before any analyst a serious challenge, namely the question of how to process, analyze and extract useful information out of it in real-time at some point. A line of answers to this pressing question comes from the area of Machine Learning (ML) and more generally, Artificial Intelligence (AI) [1]. Banks face profound challenges from the new competitive landscape and the abundance of financial technology (fintech) businesses. Traditional financial institutions have become the victims of these "fast wolves," shrinking profit margins and losing clients. No matter what kind of bank it is, changes are inevitable. Banks are eager to boost the development of intelligence-related goods and services to deal with external competitiveness and the demand for internal transformation. The Industrial and Information Technology Ministry has stated that the finance industry's firms are encouraged to employ AI to improve digital levels [2]. Deep Learning (DL) has made tremendous achievements in image recognition, voice recognition, natural language processing, and many other fields related to social life. As the core technique for AI, researchers have been focusing their attention on Deep Neural Networks (DNNs) in data training. With the help of DNNs, a multitude of products have emerged. Commercial banks' data are continuous, dimension-large, and temporal-variability, which are more harmonious with the DL model. The complexity of the DL model affords commercial banks novel applications in the fields of risk management and intelligent services. Exploring the scenario application of DL technologies in financial businesses may become a sharp weapon for improving the intelligent service level.

Resilient Banking IT Infrastructure: Integrating AI/ML Models for Scalable, Secure, and Intelligent Operations

Multidisciplinary, Scientific Work and Management Journal , 2024

Banking & Financial Services are very much core to the smooth functioning of any economy and ther... more Banking & Financial Services are very much core to the smooth functioning of any economy and therefore need to be reliable and trustworthy. A lot of systems and processes are involved in carrying out their smooth functioning. As the volume of transactions and the interconnectedness between the systems and processes have grown, the IT Infrastructure has also been transformed. Legacy systems have been replaced by state-of-the-art technologies that have brought efficiency, resiliency, and most importantly flexibility. Banking IT Infrastructure can now be scaled up and down based on the needs using Cloud Architecture. These technologies have also created new avenues for transaction channels that offer the customer a much more easy and efficient means of conducting transactions. However, along with the above advantages, this cyber transformation has opened up the IT infrastructure to vulnerabilities. Hence with financial institutions now facing the threat/repercussions of the cyberattacks, the pressure is on banks and regulators to build more resilient systems and to offer damage mitigating strategies. AI/ML /DL-based models are now providing the means to detect anomalies and malicious attacks. These models are also being built-in B2B financial systems to detect fraudulent transactions and money laundering. In this backdrop, this research work intends to study and analyze various models that can be integrated or embedded in the Banking IT Infrastructure for secure and intelligent operations.

Building Trustworthy Agentic Ai Systems FOR Personalized Banking Experiences

Metallurgical and Materials Engineering, 2025

Artificial Intelligence (AI) systems have become ubiquitous in contemporary society and have the ... more Artificial Intelligence (AI) systems have become ubiquitous in contemporary society and have the potential for transformative impact on user behavior. These systems are capable of learning autonomously to personalize their behavior to deliver improved user experiences. However, there exists the potential for unintended consequences, as the same agentic features associated with positive outcomes may also increase the capacity for negative outcomes. Financial services is an example of a domain where deploying AI systems with agentic features would be high risk. The automated decision-making capabilities of these systems could influence billions of dollars. Nonetheless, they would be entrusted with taking actions that affect users without human oversight, such as reallocating entire portfolios of assets in ways that users do not wish. Therefore, a foundational requirement for adopting these systems must be the capacity to build shared norms of beneficial behavior prior to their deployment.

Several commercially available AI systems with agentic features are already deployed in the domain of personal banking. Automated personal financial management combines categorization of transactions followed by predictions of future expenditure and savings to improve budgeting decisions, amongst other impacts. Digital banking assistants embed intelligent conversational agents used primarily for accessing banking information and services. These systems typically operate in conjunction with non-intelligent user interfaces, and thus the extent of agentic features in user-bank interactions is limited. However, envisionable advancements include wider adoption of natural language processing capabilities, comparative financial analysis, and customized query suggestions.

Agentic AI systems must operate under a formal specification of trustworthiness constraints. Therefore, AI agents must embody the technical requirements for trustworthy AI systems. Management of risks associated with agentic AI is a dangerous task given the scale of money flows in financial markets and the unprecedented scale, scope, and speed of analysis, prediction, and execution in such markets. At the same time, AI systems that endow agents with entity-level legal ownership and agency create a scarcity that could be captured in trust funds in the form of wealth to protect a material asset class from better prediction by other agents (all other predictions being sub-optimal). Glücksspiel unter Vertrauensbildung para-poker could be a conceptually rigorous game of chance. Thereby, the proposed system could help to promote beneficial forms of AI agency while governing risks effectively.

Machine Learning for Predictive Maintenance in Banking Infrastructure Services: A Data-Centric Approach

International Journal of Science and Research, 2020

The banking industry is undergoing a significant digital transformation, leading to the introduct... more The banking industry is undergoing a significant digital transformation, leading to the introduction of new microservices and software for infrastructure services. Although these changes can increase the efficiency of a bank's infrastructure, they can introduce new risks related to system operation. To mitigate risks effectively, an institution must properly monitor system operation and facilitate rapid troubleshooting. One key aspect of ensuring the high operability of banking infrastructure is managing the "invisible zone of uncertainty" that lies between operational monitoring and reliable failure detection, prioritization, and prediction. The growing microservice architecture in contemporary banking infrastructure systems leads to an exponential increase in number and complexity of the monitoring events being generated. Current approaches to predictive maintenance do not scale adequately. Serve Allocation Problem is an NP-hard one which attempts to allocate costs with a solution working in any kind of model, making it adaptable to any data format. Moreover, few of the applied approaches are focused on the context of banking services. Recent advances in the data-centric field of machine learning and natural language processing with Large Language Models has led to better contextualization of data, making it more understandable to humans. Aiming to mitigate the gap between operational monitoring and failure prediction, this study proposes the first data-centric approach to predictive maintenance on banking infrastructure services. In particular, a working proof-of-concept solution is presented that suggests mapping production monitoring events to predictive maintenance onboarding data using large language models to create and enrich monitoring data contextually. Three unsupervised approaches are implemented to identify false positive monitoring events at the second level of a three-level hierarchy on monitoring event criticality. These methods utilize clustering, dimensionality reduction visualization, and modelling with a probabilistic graphical model to achieve interpretability of "black box" algorithms and contrast false positives and true positives with an illustrative example. Ultimately, the human-centered nature of banking infrastructure development and operation is acknowledged. Further development paths are suggested in the latent area between the increasing demand for monitoring and deeper contextualization of the monitored systems through AI techniques.

Modernizing Core Banking Infrastructure: The Role of AI/ML in Transforming IT Services

MathematicalStatisticianandEngineeringApplications, 2022

Modernizing Core Banking Infrastructure: The Role of AI/ML in Transforming IT Services In today's... more Modernizing Core Banking Infrastructure: The Role of AI/ML in Transforming IT Services In today's rapidly evolving digital landscape, retail banks are increasingly focused on modernizing their core banking infrastructure. As part of this effort, financial institutions are moving forward with a component-based architecture and interoperability layer, supported by data lake and new-age analytical frameworks [1]. Adopting a componentbased architecture framework mitigates the high cost of technology replacement and effectively handles scalability and security issues. A component-based architecture with an interoperability layer facilitates easy integration with new outside components, offers rapid access to real-time data, and supports the utilization of new-age analytics frameworks. Significant investments are being made to improve customer relationships, bolster risk frameworks, and optimize governance, budgeting, and strategic planning. Along with the component-based architecture, financial institutions are amassing data lakes that provide centralized storage for unified data management and retail banking analytics. A new-age data science and analytics framework will help parse large volumes of data spread across disparate, multi-structured data sources into structured data formats, allowing organizations to glean actionable business insights [2]. Banks can strategically use these insights, thereby significantly improving customer experience and engagement, agency performance, asset quality, fraud management, and other key operational and business performance metrics. This paper summarizes the role that AI/ML can play in retail banking transformation through modernization of the IT services domain. The objective is to lay out specific AI/ML capabilities that can be harnessed to create a future-ready core banking architecture, either by making process/technology investments or by collaborating with newer, niche solution providers.

Bridging Traditional Infrastructure And Intelligent Automation: The Role Of AI/ML In Banking IT System

Migration Letters, 2022

AI/ML technologies are now maturing, available in performance and price, and secured as well as e... more AI/ML technologies are now maturing, available in performance and price, and secured as well as enabled by evolving and well-accepted cloud environments based on shared or hybrid infrastructures. AI/ML and advanced analytics are rapidly introducing new capabilities in banking as part of the digital transformation of traditional IT systems. On the one hand, IT systems traditionally have been a siloed set of specific applications, but during the last 50 years the R&D efforts were often focused on coping with the increasing complexity of legacy systems, accomplishing virtualization, and ensuring security and reliability. On the other hand, in recent years the emergence of new methodologies and new technologies has generated the possibility of advancing the functionality of already available systems. The first products of this opportunity have been realized as AI/ML systems that provide online deployment of pretrained models. While the cost could be high, especially in the case of generating the training data set, for many use cases it could be well worth the investment. However, generative AI and other new capabilities are now challenging the banks in providing sufficient robustness and reliability in the production environment of on-premise systems. The speed and applicability of these newer technologies are foreseen by the R&D department of banks that made the necessary investment into the knowledge and resource foundation.

Beyond Automation: Exploring the Potential of Agentic AI in Risk Management and Fraud Detection in Banks 1

EKSPLORIUM-BULETIN PUSAT TEKNOLOGI BAHAN GALIAN NUKLIR, 2025

As the banking system is undergoing a seismic transformation, a considerable body of research and... more As the banking system is undergoing a seismic transformation, a considerable body of research and literature has emerged in the past few years on the topic of operational risk. Risk management is a high-profile activity for any banking or financial institution, due to stringent regulations on the amount of equity capital to be held, and the amount of trading or operational losses that can be sustained each year. A bank's risk monitoring and pricing methodologies remain critical in the changing market context of steadily increasing trading volumes and speeds, growing diversity of instruments, emergence of new financial products, and collection of myriad data sources. Understanding risk is key to building confidence with clients. Hence risk management will always be a hot topic in the banks. The rapid investor adoption of agentic AI technologies has far exceeding the banks' expectations. In parallel, a rise in adverse or unwanted activities has become more visible. This poses a new set of risks that impact the financial industry. Financial institutions are faced with a "success-liability equation," where the effective use of agentic AI tools can have both significant upside but equally significant downside. Banks need to rethink AI as a technology that is no longer solely in their control. Furthermore, regulatory and risk management challenges are relevant today, as they will concern most players in the financial industry next year. The rapid spread of artificial intelligence (AI) using large language models (LLMs) poses risks that banks must address to avoid financial, operational, regulatory, reputational, compliance, and engagement impact. Banks are currently as dominant as other industries, and their response will have ripple effects. The topic is timely, relevant, and considerable in scope. To manage the risk of agentic AI tools, the banks' existing Cultivating Confidence controls and processes for more traditional AI implementations should be refreshed or strengthened in certain areas. These include Auditability, Explainability, Transparency, Supervision, and Human-in-the-loop. New operational risk considerations should also be introduced, including Speed, Externality, Disruption paths, and Reliability checks. Although many banks will consider similar risk and control enhancements, institutions will differ in their current maturity, resulting in potentially different levels of risk exposure.

Autonomous Agent-Based Systems for Real-Time Credit Risk Assessment and Decisioning

Global Research Development Journals, 2019

Autonomous agent-based systems represent a groundbreaking approach in the realm of real-time cred... more Autonomous agent-based systems represent a groundbreaking approach in the realm of real-time credit risk assessment and decision-making. These systems leverage advanced computational algorithms, machine learning, and real-time data analytics to autonomously evaluate the creditworthiness of individuals and organizations, thereby enhancing the efficiency and accuracy of risk assessment processes. By integrating multi-agent systems that function autonomously and cohesively, financial institutions can process vast amounts of data and identify emerging patterns that human analysts may overlook. This automated capability is especially critical in dynamic markets where timely decision-making is essential, as it reduces the latency associated with traditional risk assessment methods. The architecture of these agent-based systems is predicated on the principles of autonomy, adaptability, and intelligence. Agents are designed to function independently while communicating with one another to share insights and refine their decision-making processes. The incorporation of machine learning models enables these agents to continuously learn from historical data, evolving their understanding of risk factors and improving predictive accuracy over time. Furthermore, they can be programmed to simulate various market conditions, allowing organizations to stress test their credit assessments and adapt to changing economic landscapes. This adaptability not only contributes to the minimization of financial losses but also supports more equitable lending practices by providing transparent and data-driven insights into credit risks. In summary, autonomous agent-based systems for real-time credit risk assessment stand at the forefront of financial technology innovation. They encapsulate a paradigm shift from reactive to proactive credit assessment strategies, fostering a more responsive financial ecosystem. As these systems continue to evolve, their implications extend beyond mere risk assessment; they pave the way for new paradigms of decision-making in finance, enhancing overall operational efficiency while promoting sustainable credit practices. Consequently, the integration of such systems is poised to become an indispensable component of modern financial infrastructure, redefining standards and expectations in credit risk management.

A Secure and Scalable IT Infrastructure Model for AI-Powered Banking Services

International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2022

In the rapidly evolving landscape of financial services, the integration of artificial intelligen... more In the rapidly evolving landscape of financial services, the integration of artificial intelligence (AI) has become imperative for maintaining competitiveness and ensuring customer satisfaction. This abstract presents a comprehensive overview of a secure and scalable IT infrastructure model tailored for AI-powered banking services. The model addresses the dual challenges of safeguarding sensitive financial data while supporting the computational demands of AI systems. The traditional banking IT infrastructure is no longer sufficient; it must transition to a robust architecture that embraces cloud computing and advanced security protocols. At the core of the proposed model lies a multi-layered architecture designed to balance performance and security. The infrastructure incorporates advanced encryption algorithms and robust firewalls alongside machine learning algorithms that detect anomalies in real-time, thereby enhancing threat detection capabilities. Seamless integration of these technologies enables financial institutions to process large volumes of data efficiently while ensuring compliance with regulatory frameworks. Additionally, the adoption of containerization and microservices architecture supports the modular deployment of AI applications, allowing for rapid scaling in response to fluctuating user demands. This infrastructure not only prioritizes security but also ensures scalability, allowing banks to adapt to the increasing complexities associated with AI-driven analytics. As AI continues to reshape customer interactions and operational processes, organizations must reconsider their infrastructure strategies. The proposed model provides a strategic blueprint that not only meets current demands but also anticipates future advancements in AI technologies. Through a thorough examination of security measures, deployment strategies, and performance optimization, this abstract sets the groundwork for a detailed exploration of how a secure and scalable IT infrastructure can catalyze the effective implementation of AI in banking services. This synthesis of security, scalability, and efficiency is essential for leveraging AI's transformative potential in the financial sector, ensuring that institutions can thrive in an increasingly digital economy.

From Data Lakes to Smart Decisions: Architecting AI/ML-Enabled Infrastructure for Future-Ready Banking

Kurdish Studies, 2022

The four pillars of the banking and finance sector, i.e., risk, return, asset quality, and compli... more The four pillars of the banking and finance sector, i.e., risk, return, asset quality, and compliance, are enormous and diverse in nature. Traditional research techniques or methods are falling short to process and analyze such data, owing to shear volume and velocity of transactions. AI/ML methods and modeling help fulfill these criteria, and interestingly, banks globally seem to show keen interest in adopting these technologies in their research framework in a big way. In this pursuit, various models are developed with a structured study at different banks, presenting a comprehensive big-data analytics of each insight, so that banks will be ready to take the next big jump in AI/ML-enabled infrastructure. Big data today plays a vital role in all the industries to enhance their growth in all aspects. Banking, the oldest sector across the globe, is also shifting to big data methodologies to improve governance, operation, and customer satisfaction. Shadow banking, virtual banking, and crypto banking are examples of the latest technologies applied to improve day-today banking requirements. Traditional banking research work involves risk, growth return, asset quality, and compliance, but banks struggle to handle big data banking research outcomes]. There are plenty of algorithms, libraries, and data cleaning techniques for intelligent data science models, but direct relevance to banking models is limited. AI and ML technologies seem to be the new set of buzzwords often used to denote the future of technology. However, for banking data, it is still a distant dream owing to huge volume fluctuations, overwhelming data cleaning requirements, and interaction with various legacy systems in multiple formats. The failure or fraudulent transaction occurrence of any entity reveals a gap in governance and risk management documentation. Enabling the event and transaction data in AI infrastructure can enhance timely actionable knowledge-based monitoring to avoid future failure and fraud. Using testing data and building algorithms may take too much time to monitor real-time data, prompting actors to miss early signs of warning failure or fraud while planning big data handling, data journeys, and governance approaches to fill the gaps.

From Legacy Systems to Intelligent Infrastructure: A Roadmap for AI-Driven Banking IT

Kurdish Studies, 2022

The banks are still using legacy systems as the core of their business. One of the reasons is tha... more The banks are still using legacy systems as the core of their business. One of the reasons is that the financial and operational data are stored here and can be used for decision making. Other reasons are that applications developed for many years and very high costs for these applications to be retired. On the other hand, technology and in the past year, a cataclysmic evolution of technology and client demands. This trade-off between stability on legacy systems or competitive advantage on modern architectures, as there are many known cases of banks that have collapsed due to not adapting quickly enough is still a hot topic for many industries, not just banks. The risk and costs of switching to new system is high enough. The objective of this article is to present the main risks and costs of using legacy systems, the preferences of the bank users in terms of software reliability and subjective assessment of the software systems' modernity, an overview on new intelligent IT infrastructures, the preferences for the modernization of systems and a SOLUTION of the modernization of legacy banking systems using an open architecture [1]. Artificial Intelligence (AI) is now widely acknowledged as one of the most important digital transformation enablers across a significant number of industries including the banking sector. AI technologies are assisting the banks in India in upgrading their operations across the board from accounting to sales to contracts to cybersecurity. This paper aims to analyze the application of AI in Banks and to take a case study based on the virtual Assistant of SBI-SIA. The evolution of banking and the emergence of virtual banking was explained in this and the trends in the modern Banking systems were explained along with a Rationale use of AI in Banking [2].

Next-Gen Banking Infrastructure: Designing AI-Native IT Architectures for Financial Institutions

Journal for Re Attach Therapy and Developmental Diversities, 2023

The rapid advancement of artificial intelligence (AI) is fundamentally transforming the financial... more The rapid advancement of artificial intelligence (AI) is fundamentally transforming the financial services industry. As traditional banking infrastructures struggle to keep pace with the demands of real-time analytics, personalized services, and enhanced security, there is a pressing need to design AI-native IT architectures that are scalable, resilient, and agile. This paper explores the core principles and design considerations for next-generation banking infrastructures that are purpose-built for AI integration. It addresses key components such as data architecture, cloud-native platforms, AI-driven automation, cybersecurity frameworks, and compliance mechanisms. Through analysis of case studies and emerging technologies, the paper outlines strategic pathways for financial institutions to modernize their IT ecosystems, reduce operational costs, and foster innovation while maintaining regulatory compliance and customer trust. The goal is to provide a blueprint for banks and fintechs to harness the full potential of AI in delivering intelligent, adaptive, and customer-centric financial services.

Scalable Infrastructure for AI in Banking: Bridging Cloud Computing and Regulatory Demands

Educational Administration: Theory and Practice, 2023

Over the past few years, the financial services industry has been buzzing with buzzwords around a... more Over the past few years, the financial services industry has been buzzing with buzzwords around artificial intelligence (AI). Banks' media releases profess their cover to be on the cutting edge of AI in order to make better use of customer data, understand customer needs and preferences, and deploy responsive products and services. AI's applications in banking include management of risk (credit, market), compliance with AML regulations, cyber fraud detection, marketing communications, workforce optimization (scheduling of tellers), and treasury functions (fund and liquidity management). Most of these applications involve process automation, allowing for the work to be done more efficiently, but they do not fundamentally change the mode of operation of banks. The rise of better and noisier predictors in the history of financial markets has historically been met with restrictions and disqualifications so as to uphold a level playing field. As financial services institutions continue to explore ways to enhance their existing predictive models using ML technology, regulators and industry standards boards also take interest in the challenge of ensuring that ML models champion the same principles and objectives as their predecessors. A long list of AI governance challenges stimulated the work described here. Their chronic presence as detected by too few FRTB, AML and other governance abuses would seem to call for new solution approaches. This work presents a high-level AI system framework and modular building blocks that map emerging self-regulation practices and tooling to assist efforts to mitigate governance risk in financial services. Focus is placed on the design of tooling for financial services ML models for autonomous, ongoing operations. A financial services firm's AI systems ought to abide by a host of regulatory and ethical principles, requirements and obligations akin to the algorithmic proxies employed to execute trades. To that end, building blocks are developed that are suggested for integration into the AI systems of banks and other financial institutions in order for them to assure compliance with regulation and upholding of ethics through self-regulated behaviour. Building blocks are defined broadly in that they aim at reusable, off-the-shelf automation scripts that allow for the independently and easily executable instantiation of governance processes tailored to model and regulation specifics [2]. The rooting of the tooling and best practices in the governance risks faced in the financial services industry, and in their consequences, might also provide a step towards prioritizing and demystifying the immensely daunting challenge of AI governance for firms across industries.

Transforming Customer Experience in Digital Banking Through Machine Learning Applications

International Journal Of Engineering And Computer Science, 2020

In the contemporary landscape of digital banking, the transformation of customer experience has e... more In the contemporary landscape of digital banking, the transformation of customer experience has emerged as a pivotal focus for financial institutions seeking competitive differentiation. Through the integration of machine learning applications, banks are now able to analyze vast datasets to improve service delivery, enhance customer engagement, and personalize user interactions. Leveraging algorithms capable of discerning patterns within customer behavior, banks can proactively offer services tailored to individual needs, thereby fostering an environment that prioritizes customer satisfaction and loyalty. Machine learning technologies serve multiple purposes in bolstering customer experience. Firstly, they enable predictive analytics that forecast customer needs, reduce churn rates, and inform product development. By employing natural language processing, banks can assess sentiment from customer communications, allowing for targeted interventions that address concerns before they escalate. Additionally, machine learning models facilitate real-time transaction monitoring to detect fraudulent activities, thereby building trust and security in banking products. Furthermore, through automated customer service channels, such as chatbots, banks enhance operational efficiency while providing immediate support, mitigating common issues faced by users. Consequently, the application of machine learning in digital banking is reshaping the customer experience by creating more intuitive, responsive, and secure banking environments. As banks embrace these technologies, they not only streamline internal processes but also cultivate a deepened understanding of their clientele, leading to more meaningful interactions. This essay delves into the intricate relationship between machine learning applications and customer experience enhancement in digital banking, examining case studies, best practices, and the inherent challenges faced by institutions navigating this transformative journey. By focusing on actionable insights derived from data-driven methodologies, it posits that successful digital banking strategies hinge upon the effective integration of machine learning, ultimately defining the future of customer interaction in the financial sector.

Optimizing Infrastructure Services in Banking IT with Federated Learning and AI Governance

International Journal of Advanced Research in Computer and Communication Engineering, 2023

The banking sector is increasingly leaning on advanced technologies to optimize infrastructure se... more The banking sector is increasingly leaning on advanced technologies to optimize infrastructure services, particularly through the deployment of Artificial Intelligence (AI) and machine learning methodologies. This movement is driven by the need for enhanced decision-making capabilities, improved operational efficiencies, and robust risk management strategies. Federated Learning emerges as a pivotal framework within this context, allowing institutions to collaboratively train AI models without compromising sensitive customer data. This decentralized approach not only mitigates privacy risks, but also enriches the training datasets, ultimately yielding more accurate and reliable predictive models. Such models are crucial for applications that encompass fraud detection, customer segmentation, and algorithmic trading. Furthermore, the implementation of AI governance frameworks is instrumental in navigating the ethical and regulatory complexities that accompany the integration of AI in banking. Effective governance ensures that AI systems operate transparently, with accountability mechanisms in place to address potential biases and ethical dilemmas. This is particularly important in a sector that manages vast amounts of personal and financial information. By instituting comprehensive oversight policies, banks can foster trust among stakeholders while enhancing compliance with regulatory requirements. The synergy between Federated Learning and AI governance thus not only fortifies the technological backbone of banking IT but also aligns operational practices with ethical standards and consumer protection mandates. This interplay between collaborative AI initiatives and stringent governance encapsulates the future of banking infrastructure. As institutions embrace these innovative solutions, they position themselves to harness the full potential of data-driven insights while safeguarding customer interests. Consequently, the dual focus on optimizing infrastructure services through Federated Learning and enforcing AI governance emerges as a key strategic approach within the banking landscape. This comprehensive framework is essential for navigating the complexities of a rapidly evolving financial ecosystem, ultimately facilitating sustainable growth and enhancing competitive advantage in the marketplace.

Agentic AI-Enabled Fraud Prevention: Multi-Agent Collaboration Models for Real-Time Threat Detection and Response in Digital Banking

In the era of digital banking, ensuring the security and integrity of financial activities has be... more In the era of digital banking, ensuring the security and integrity of financial activities has become paramount. Financial frauds,
particularly in online banking and credit card transactions, pose serious threats to the global economy and the financial well-being
of individuals. Billions are lost annually due to fraudulent activities