Implementation Cost Research Articles

A zero-knowledge proof federated learning on DLT for healthcare data

With the increasingly widespread adoption of Healthcare 4.0 practices, new challenges have arisen for the utilization of collected sensitive data. On the one hand, these data have immense potential to unlock valuable insights for personalized medicine, early disease detection, and predictive analysis thanks to the use of Artificial Intelligence. On the other hand, ensuring the protection of patient privacy is of paramount importance to maintain trust and uphold ethical practices within the healthcare system. Classical centralized learning approaches do not fit well with the privacy and security requirements imposed by the law and the sensitivity of treated data, which is why decentralized learning approaches are gaining ground. Among these, Federated Learning (FL) stands out as a viable alternative, providing greater security and performance comparable to classic centralized learning approaches. However, there are still various attacks targeting the local parameters or gradients updated by the participants. Therefore, we present our architecture based on the conjunction of Zero-Knowledge Proof, FL, and blockchain that implements also the decentralized identifier standard. The adoption of this architecture can grant the execution, management, supervision, and updating of the FL process, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the performance, robustness, and implementation costs of the proposed architecture, we develop a case study on the prediction of blood glucose levels in people with Type-1-diabetes. The results of our analysis show an improved system in terms of balance between performance privacy and security, guaranteeing high levels of verifiability, therefore proving the proposed architecture suitable for most of the FL processes needed in the healthcare field.

Provably efficient security-aware service function tree composing and embedding in multi-vendor networks

Multicast greatly benefits many emerging applications such as federated learning, metaverse, and data warehouse. Recently, due to frequent cyber-attacks, multicast services have tended to request rigorous security agreements, which likely differ among the destinations. To meet such agreements, one can employ security-aware service functions (SFs) to construct the security-aware SF tree (S-SFT) for multicast services. A security-aware SF can be provided by various vendors with diverse configurations and implementation costs. The multi-configured SFs and the various security agreements will add significant complexity to the deployment process of the security-aware multicast request. In this work, for the first time, we study how to effectively compose and embed an S-SFT over the network with multiple vendors. We formulate the problem of security-aware SFT composing and embedding. We develop a new technique called cost-security-centrality (CSC) based on the pigeonhole’ s principle and propose a heuristic algorithm called CSC-based S-SFT deployment (CSC-SD). Via thorough mathematical proofs, we show that CSC-SD is logarithm approximate. Extensive simulations show that CSC-SD significantly outperforms the benchmarks and reveal that more function sharing facilitates saving implementation cost, but more routing sharing does not indicate saving routing cost.

Investigation of Different Throat Concepts for Precipitation Processes in Saturated Pore-Network Models

AbstractThe development of reliable mathematical models and numerical discretization methods is important for the understanding of salt precipitation in porous media, which is relevant for environmental problems like soil salinization. Models on the pore scale are necessary to represent local heterogeneities in precipitation and to include the influence of solution-air-solid interfaces. A pore-network model for saturated flow, which includes the precipitation reaction of salt, is presented. It is implemented in the open-source simulator DuMu$$^{\textrm{X}}$$ X . In this paper, we restrict ourselves to one-phase flow as a first step. Since the throat transmissibilities determine the flow behaviour in the pore network, different concepts for the decreasing throat transmissibility due to precipitation are investigated. We consider four concepts for the amount of precipitation in the throats. Three concepts use information from the adjacent pore bodies, and one employs a pore-throat model obtained by averaging the resolved pore-scale model in a thin-tube. They lead to different permeability developments, which are caused by the different distribution of the precipitate between the pore bodies and throats. We additionally apply two different concepts for the calculation of the transmissibility. One obtains the precipitate distribution from analytical assumptions, the other from a geometric minimization principle using a phase-field evolution equation. The two concepts do not show substantial differences for the permeability development as long as simple pore-throat geometries are used. Finally, advantages and disadvantages of the concepts are discussed in the context of the considered physical problem and a reasonable effort for the implementation and computational costs.

Comparative study on scenarios for rotating gantry mechanical structures

Background This paper outlines a comparative study on scenarios of gantries mechanical design for ion-therapy of cancer, which is a crucial step toward the development of the next ion medical machine. Methods Multiple scenarios were considered based on robustness of the design, size, weight and complexity, deformation and precision performances and costs, as well as environmental impact. Four prospective scenarios were identified, each of them capable of providing beam to at least 220°around the patient. One scenario is capable of providing treatment angles of 360°. This report will describe the unified methodology performed during the study in order to achieve unbiased results in a comprehensive manner. Results Results show that in statically balanced scenarios, considerable improvements can be reached in terms of safety, deformation, precision performances, complexity and costs of implementation. Conclusions All scenarios are deemed suitable for further gantry design development.

Decentralized Integrity: Blockchain-Based Frameworks for Enhancing Software Update Security

This article examines the application of blockchain technology as a solution for secure and transparent software update distribution. As cyber threats evolve, traditional centralized update mechanisms face increasing vulnerabilities to tampering and unauthorized modifications. We propose a blockchain-based framework that leverages distributed ledger technology to create an immutable, decentralized environment for software updates. Our article demonstrates how this approach can eliminate single points of failure, ensure update integrity through cryptographic signing and network-wide verification, and enhance transparency in the update process. We discuss the system architecture, including update creation, distribution, and verification mechanisms, and evaluate its integration with existing software ecosystems. While acknowledging challenges such as scalability and implementation costs, we argue that blockchain-based solutions significantly advance securing software distribution. Our findings suggest that this approach has the potential to become a standard practice in the industry, substantially improving trust and reliability in software update systems. This article contributes to the growing body of research on blockchain applications in cybersecurity and provides insights for future developments in secure software distribution methods.

Multicore-fiber submarine systems [Invited

This paper reviews and analyzes multicore-fiber technologies in the context of submarine networks. As global/transcontinental capacity continues to grow at a large and steady pace, the subsea industry is challenged to deliver technologies that provide large-capacity networks in a faster, greener, and cost-effective fashion. Multicore fiber has been considered as a serious candidate for next-generation systems with the ability to maintain standard cable size at large core counts. This paper analyzes the technoeconomics of next-generation submarine systems including variables such as equipment size versus marine cost and other critical elements (not analyzed before, to our knowledge) for submarine system implementation. In this context, this paper also analyzes the state-of-the-art of multicore subsystems and components and discusses their roadmaps, requirements, and evolution toward the realization of a multicore ecosystem for next-generation SDM systems.

AI-Enhanced GraphoText Analysis for Tracking Counselling Therapy Progress: Integrating Multimodal Graphology and Machine Learning

Despite the robustness of existing instruments, routine manual observation in psychotherapy faces significant challenges, including lack of time, labour, high implementation costs, and potential biases such as response bias and social desirability. This study proposes a dynamic assessment approach using machine learning and multimodal features extracted from handwriting, incorporating graphology-based and content-based features. The integration of content-based and graphology-based features involves combining text and handwriting features with the Support Vector Machine (SVM) using the Radial Basis Function Kernel (RBF). The results show that this approach achieves an impressive accuracy rate of 86.25%. The proposed framework not only improves psychotherapeutic practise, but also offers new insights into human cognition and emotional dynamics by revealing intricate patterns in handwriting. This advance facilitates data-driven decision-making, improves the quality of patient care, and overcomes challenges associated with manual monitoring, including social desirability bias and response set bias. This research paves the way for innovative methods at the interface of mental health and technology, and promises a more objective and efficient approach to monitoring the progress of psychotherapy.

ВИКОРИСТАННЯ ЕЛЕКТРОННИХ СИСТЕМ ДОКУМЕНТООБІГУ ТА ЗВІТНОСТІ ЛОГІСТИЧНИМИ КОМПАНІЯМИ УКРАЇНИ

The article explores the possibilities and ways of using information technologies for document management and electronic reporting at logistics enterprises. Theoretical approaches to understanding the goals and tasks of logistics information systems in transport technologies have been considered. The main information technology systems that change the logistics industry in modern conditions have been characterized, namely: RPA; chat bots; visualization; Artificial Intelligence; predictive analytics. The electronic technologies used for information provision in Ukrainian practice, the IT-Enterprise ERP system of JSC ‘Ukrposhta’ and the CRM system of the company ‘Epicentr K’ have been analyzed. Their characteristics and advantages of use have been defined. It have been found that the basis of the functional component of the modern information system is a single module of operations between the links of the logistics system, which determines the relationship between the functional divisions of the company (in terms of the implementation of logistics functions), intermediate links and consumers of the company’s products. The implementation of a logistics information system in the work of a logistics enterprise makes it possible: - to increase the efficiency of its work, achieve a high level of service for customers and ensure effective control; - to ensure a modern approach to the management of implementation processes and the organization of various types of events; - to monitor the efficiency of commercial operations; - to conduct an analysis of the profitability of the activity; - to increase the effectiveness of price policy, etc. The problems of the introduction of electronic document flow and reporting systems in Ukraine have been noted: - the problem of interoperability in electronic document flow; - lack of clear standards and requirements for electronic reporting, companies may hesitate to switch to this type of reporting; – constant changes in legislation and requirements can create difficulties for enterprises in implementing stable electronic reporting systems; - inconsistency of electronic reporting formats with global ones; high implementation costs; - concerns about data privacy and security can deter companies from switching to electronic reporting, especially when the security of information on the network is at stake.

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%. AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

Implementation of Artificial Intelligence in Quality Management in SMEs: Benefits and Challenges

The adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI in the quality management of SMEs include: Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits, but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment.

Adaptive control with lower-order dynamics for nonlinear systems with unknown control coefficients

While adaptive control has been extensively studied for a variety of uncertain nonlinear systems, overparametrization still exists in many works. By overparametrization, it means that the order of the adaptive controller is (much) higher than the number of unknown parameters of the system, which is a defect from a theoretical perspective and indicates high cost in controller's implementation. This paper investigates adaptive control with lower-order dynamics for nonlinear systems with unknown control coefficients, managing to significantly reduce or eliminate overparametrization and showing the differences in controllers' dynamic orders of different control strategies. The uncertain nonlinear system in question has n unknown control coefficients and a p-dimension unknown vector in the system nonlinearities. We consider two cases of unknown control coefficients: case (a): their signs are known; case (b): their signs are unknown, i.e. unknown control directions. For case (a), first by compensating for unknown control coefficients and their reciprocals, as well as the unknown vector, we design an adaptive controller with ( 2 n + p − 1 ) -order dynamics, where no inequality estimate is used to avoid conservatism of the controller. Second, for case (a), via bounding the system uncertainties by a single unknown constant, we design an adaptive controller with only 1-order dynamics, where inequality estimates are used and hence the controller suffers conservatism. For case (b), by adopting multiple Nussbaum functions, we design a ( 2 n + p − 1 ) -order adaptive controller, where n-order dynamic compensation is employed to overcome n non-identical unknown control directions. As in case (a), because no inequality estimate is used, conservatism of the controller is avoided. Regardless of conservatism, for case (b), by bounding the uncertainties in the destabilizing terms, we design n-order adaptive controller, where, of course, inequality estimates have to be used.

The LIFE SILENT project's approach to sustainable noise mitigation

The main objective of the LIFE SILENT PROJECT is the development of sustainable and eco-friendly solutions to mitigate noise in complex urban environments, where multiple and diverse noise sources, mainly roads and railways, coexist across densely populated areas. Mitigating noise in such environments generally excludes the use of solutions that might interfere with the urban context, such as noise barriers. This is mainly due to the proximity of receivers to the noise source, which reduces the visibility of the surroundings and air circulation, leading to microclimatic modifications and social opposition. This is why noise mitigation measures acting directly on the source are recommended, such as low-noise pavements for roads, and dampers, rail grinding, and silent brakes for railways. However, these solutions suffer from either durability issues or high implementation costs. Thus, an improvement is necessary. In the LIFE SILENT project, innovative and sustainable low-noise and long-lasting pavements, as well as low-height noise barriers will be developed and demonstrated in a real test environment. Additionally, procedures will be established to facilitate their synergistic implementation, providing transport infrastructure owners and managers with solid information to support their widespread adoption. In this paper, a general overview of the LIFE SILENT project is given.

A Nationwide Physical Activity Intervention for 654,500 Adults in Singapore: Cost-Utility Analysis.

Increasing physical inactivity is a primary risk factor for diabetes and hypertension, contributing to rising health care expenditure and productivity losses. As Singapore's aging population grows, there is an increased disease burden on Singapore's health systems. Large-scale physical activity interventions could potentially reduce the disease burden but face challenges with the uncertainty of long-term health impact and high implementation costs, hindering their adoption. We examined the cost-effectiveness of the Singapore National Steps Challenge (NSC), an annual nationwide mobile health (mHealth) intervention to increase physical activity, from both the health care provider perspective, which only considers the direct costs, and the societal perspective, which considers both the direct and indirect costs. We used a Markov model to assess the long-term impact of increased physical activity from the NSC on adults aged 17 years and older. A Monte Carlo simulation with 1000 samples was conducted to compare two situations: the NSC conducted yearly for 10 years against a no-intervention situation with no NSC. The model projected inpatient and outpatient costs and mortality arising from diabetes and hypertension, as well as their complications. Health outcomes were expressed in terms of the quality-adjusted life-years (QALYs) gained. All future costs and QALYs were discounted at 3% per annum. Sensitivity analyses were done to test the robustness of our model results. We estimated that conducting the NSC yearly for 10 years with a mean cohort size of 654,500 participants was projected to prevent 6200 diabetes cases (95% credible interval 3700 to 9100), 10,500 hypertension cases (95% credible interval 6550 to 15,200), and 4930 deaths (95% credible interval 3260 to 6930). This led to a reduction in health care costs of SGD (Singapore dollar) 448 million (95% credible interval SGD 132 million to SGD 1.09 billion; SGD 1=US $0.73 for the year 2019). There would be 78,800 (95% credible interval 55,700 to 102,000) QALYs gained. Using a willingness-to-pay threshold of SGD 10,000 per QALY gained, the NSC would be cost-saving. When indirect costs were included, the NSC was estimated to reduce societal costs by SGD 1.41 billion (95% credible interval SGD 353 million to SGD 3.80 billion). The model was most sensitive to changes in the inpatient cost of treatment for diabetes complications, time horizon, and program compliance. In this modeling study, increasing physical activity by conducting a yearly nationwide physical activity intervention was cost-saving, preventing diabetes and hypertension and reducing mortality from these diseases. Our results provide important information for decision-making in countries that may consider introducing similar large-scale physical activity programs.

LEVERAGING ENTERPRISE ARCHITECTURE FOR DIGITAL TRANSFORMATION: A STRATEGIC APPROACH TO BUSINESS AGILITY

This paper examines the critical role of Enterprise Architecture (EA) in facilitating Digital Transformation (DT) and enhancing business agility. EA serves as a comprehensive framework that aligns IT infrastructure with business processes, enabling organizations to adapt effectively to rapid technological changes and market demands. Through a detailed literature review, the paper highlights key EA frameworks, such as TOGAF and Zachman, and discusses how these frameworks support the alignment of organizational goals with technology initiatives. The analysis reveals that leveraging EA not only improves adaptability and operational efficiency but also minimizes risks associated with digital transformations. Despite the numerous benefits, challenges such as implementation costs, resistance to change, and integration complexity are acknowledged. The paper concludes by offering recommendations for businesses to optimize their EA practices for successful digital transformation and suggesting areas for future research, including the exploration of EA's impact across various industries and the role of leadership in managing digital initiatives.

Calculating carbon: The value of information in precision for blue carbon restoration projects

Coastal wetland restoration projects can receive payments for ecosystem services but often occur in regions with limited data, and additional data collection can be financially prohibitive. Value of Information analysis can quantify the difference between the expected value of an action before and after new information has been collected, aiming to understand how much data is required to make decisions that balance the costs of implementation versus the benefits of the project. The Australian carbon market provides a method that uses reintroduction of tidal flows to restore coastal wetland ecosystems for their carbon sequestration functions. The method requires a hydrological assessment of prospective sites, which is employed to estimate carbon sequestration potential. This research investigates how different amounts of data collection and different levels of complexity in the hydrological assessment influence the carbon abatement emissions estimated using the method. The results indicate that tidal restoration for blue carbon credits on grazing land may not be financially viable. We found that tidal data collected onsite were important for decision-making while complex hydrological models have low value compared to more simplistic approaches. While investing in data collection provides more value than increasing the complexity of modelling approaches, the value of information was still low. Additionally, restoration of coastal wetlands is unlikely to be financially attractive at current carbon prices, and the land would have to be unsuitable for cattle to become profitable for restoration. This work provides a framework for evaluating the financial benefit of collecting on-site data and using robust methods for estimating inundation, that can be used to guide decision-making to achieve optimal income.

Transforming Crop Management Through Advanced AI and Machine Learning: Insights into Innovative Strategies for Sustainable Agriculture

The integration of artificial intelligence (AI) and machine learning (ML) into crop management is transforming modern agriculture by enhancing efficiency, sustainability, and resilience. This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization. The use of AI-driven predictive analytics allows for more accurate forecasting of crop yields, pest outbreaks, and weather conditions, enabling farmers to make data-driven decisions that optimize resource use and reduce environmental impacts. A significant advancement is the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment. These technologies enable real-time monitoring and precise interventions, enhancing productivity and minimizing labor costs. In crop breeding and genomics, AI accelerates the development of resilient crop varieties, which is crucial for adapting to climate change and increasing food demands. Despite the many benefits, challenges such as data quality, infrastructure limitations, and high implementation costs remain. The adoption of AI technologies is uneven, with small-scale farmers in developing regions facing barriers due to limited access to data and resources. Ethical concerns related to data privacy and the digital divide must also be addressed to ensure equitable access to these technologies. The future of AI and ML in agriculture lies in the development of more advanced predictive models, enhanced integration with the IoT, and the widespread use of autonomous farming systems.

Impact of international accounting standards on Hungary’s financial transparency

Acceptance and implementation of international financial reporting standards ensure a wider scope for financial transparency, accountability, and comparability on a global scale. Against this backdrop, this study looks at the implications of these standards on Hungary’s financial transparency by evaluating panel data from 716 private companies over the period 2013–2023. The Hausman test results suggest that Fixed and Random Effects models should be used.The analysis indicates that, on average, the sampled companies have improved financial transparency by 75%. Key determinants include standard adoption (0.025 coefficient, t = 8.333, p < 0.001), cost of implementation (2.400 coefficient, t = 24.000, p < 0.001), investor confidence (0.035 coefficient, t = 11.667, p < 0.001), and legislative changes (2.450 coefficient, t = 24.500, p < 0.001). Moreover, it is possible to obtain significant positive effects on the centered variables for implementation costs (coefficient = 2.498, p < 0.001) and government efficiency (coefficient = 0.036, p < 0.001).These results demonstrate a positive effect, which is significantly created by adopting these standards on financial transparency. They underline increased investor confidence and government efficiency as drivers of these improvements. Applying these standards in Hungary’s financial reporting system is classified as a strategic tool to foster economic stability and attract foreign investment, which ensures Hungary’s good standing in the global economy.

SYSTEMATIC LITERATURE REVIEW ON ARTIFICIAL INTELLIGENCE APPLICATIONS IN SUPPLY CHAIN DEMAND FORECASTING

This systematic review investigates the applications of artificial intelligence (AI) in supply chain demand forecasting, focusing on the performance of AI-driven models compared to traditional forecasting techniques. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted, yielding a final selection of 65 peer-reviewed articles for in-depth analysis. The review explores the advantages of AI models, particularly machine learning (ML) and deep learning (DL), in improving forecasting accuracy, scalability, and responsiveness to real-time data. It also examines AI’s applications across various industries, including retail, manufacturing, e-commerce, and logistics, where AI-driven models have significantly enhanced inventory management, production scheduling, and operational efficiency. However, the review highlights challenges related to data quality, model complexity, and high implementation costs, which limit the broader adoption of AI in demand forecasting. This study provides valuable insights into the current state of AI applications in supply chain management and suggests areas for future research, particularly in improving data management and developing more interpretable AI models to facilitate wider implementation.

Optimization of Coreless PCB Coils Based on a Modified Taguchi Tuning Method for WPT of Pedelec

The printed circuit board (PCB) winding coil offers advantages such as small size, high precision, high repeatability, and low cost, making it conducive to the miniaturization of electronic equipment and a popular choice in wireless power transmission systems. This paper aims to clarify the correlation between induction parameters and inductive capabilities using the orthogonal array of the modified Taguchi method for Pedelec applications. The conventional Taguchi method typically achieves only local optimization; however, this paper considers practical application conditions and combines experimental data to establish the initial values of the orthogonal array, thereby achieving global optimization. Additionally, the tuning process of the Taguchi method replaces physical experiments with simulations, enhancing optimization speed and reducing hardware implementation costs. The performance index for the proposed modified Taguchi tuning method is selected as a combination of the quality factor (Q) and coupling coefficient (k) to minimize AC resistance and improve system efficiency. To validate the proposed method, the designed coils were implemented and tested in a WPT system based on S–S compensation with a half-bridge topology. The experimental results demonstrate that the optimized PCB coil parameters derived from the proposed tuning method accurately validate the method’s effectiveness and accuracy. From the measured results with the proposed modified tuning method, the system efficiency is increased by 43.87% and the system transmitting power is increased by 28.51%.

LEVERAGING MANAGEMENT INFORMATION SYSTEMS FOR ENHANCING CREDIT RISK ASSESSMENT IN COMMERCIAL BANKS

This study explores the significant impact of Management Information Systems (MIS) on credit risk assessment in commercial banks, examining how these systems enhance decision-making accuracy, operational efficiency, and proactive risk management. By synthesizing findings from 50 peer-reviewed studies, the research reveals that banks using MIS experience a 20-30% reduction in non-performing loans and a 40-50% increase in loan processing speed due to automation and real-time data analysis. The integration of advanced technologies such as artificial intelligence (AI) and predictive analytics further improves credit risk forecasting accuracy by 15-20%, enabling banks to implement proactive risk mitigation strategies that reduce borrower defaults by up to 25%. However, the study also highlights significant challenges, particularly for smaller banks, which face high implementation costs and difficulties integrating MIS with legacy systems. Despite these challenges, the role of MIS in ensuring regulatory compliance, particularly under Basel III, and reducing overall credit exposure by 15-20% underscores its critical importance in modern credit risk management. The findings suggest that while MIS is essential for maintaining financial stability and competitiveness, scalable and cost-effective solutions are necessary for broader adoption across the banking industry.