The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance

Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision.  Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness.  The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively.  A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process

Real-Time Risk Analysis and Hazard Management

Safety remains a critical priority for the Australian mineral resource industry and will receive increased focus in the future. This is particularly evident in underground coal mines where reserves are becoming deeper and more hazardous to extract. The CSIRO, through its Exploration and Mining Division, have recently delivered on two projects aimed at providing step-change capabilities in real-time risk management and hazard control. This paper describes the key outcomes of these projects

Aviation Automation and CNS/ATM-related Human-Technology Interface: ATSEP Competency Considerations

Abstract The aviation industry has, no doubt, undergone profound transformations ever since the first powered aircraft flight on December 17, 1903. An especially noticeable aspect of the transformations is in the area of automation. Remarkably, aviation operations are becoming increasingly automated and it is expected that the wind of change sweeping through the industry will be getting stormier as new technologies emerge especially within the context of the emerging prospects of intelligent technologies, which may ultimately enthrone complete automated or technology-based intelligent decision making. Perhaps, in no sphere of the aviation system has there been, in recent times, a much more lively and sustained exhibition of the spirit of automation than in the realm of communications, navigation, surveillance/air traffic management (CNS/ATM). This scenario, invariably, imposes far-reaching obligations on and have wide-ranging implications for air traffic safety electronics personnel (ATSEP) – the ICAO-recognized nomenclature for personnel involved and proven competent in the installation, operation, and/or maintenance of a CNS/ATM system. This paper explores, based on a systematic review of extant literature, the concept of aviation automation in the context of the broader conceptual and theoretical underpinnings of automation and with an emphasis on automated CNS/ATM systems. The primary aim is to examine the implications of an automated CNS/ATM environment on aspects relating to the roles, tasks, competence, and training of ATSEP within the framework of the safety-criticality of air traffic management. Based on arguments regarding ATSEP competency considerations in the context of an automation-rich CNS/ATM environment, a conceptual model of ATSEP competencies and a model of competency-based, human-technology ATSEP task flow are proposed

AN APPROACH FOR DESIGN AND MANAGEMENT OF A SOLAR-POWERED CENTER PIVOT IRRIGATION SYSTEM

Emerging financial and environmental challenges associated with conventional power sources have increased global interest in consuming unpolluted, renewable energy sources for irrigation sector. Solar energy may be an attractive choice in this regard due to its strong influence on crop water use and related energy requirement. However, a comprehensive approach for a reliable and economically viable photovoltaic (PV) system design to produce energy from solar source is required to accurately explore its potential. This thesis describes the development and application of a reliability assessment model, identifies a suitable solar irrigation management scheme, and provides guidelines for evaluating economic viability of a solar-powered center pivot irrigation system. The reliability model, written in MATLAB, was developed based on the loss of power supply probability (LPSP) technique in which various sub-models for estimating energy production, energy requirement and energy storage were combined. The model was validated with actual data acquired from the study site located at Outlook, Saskatchewan, Canada and an excellent agreement was found. For example, normalized root mean square error (NRMSE) for the battery current was found to be 0.027. Irrigation management strategies (irrigation depth, frequency and timing) were investigated by comparing the PV system sizing requirement for a conventional (25-35 mm per application) and for a frequent light irrigation management strategy (5-8 mm per application). The results suggest that the PV sizing can be reduced significantly by adopting frequent light irrigations which utilize the power as it is produced during daylight hours, rather than relying on stored energy. The potential of a solar-powered center pivot irrigation system was revealed for three different crops (canola, soybean and table potato) at the site by conducting a detailed economic analysis for the designed PV system. High value crops with moderate water requirements such as table potatoes appeared to be the most feasible choice for the study site. However, the potential may greatly vary for different crops in altered locations due to management, agronomic, climate, social, and economic variations. It can be concluded that a holistic approach described here can be used as a tool for designing an appropriate PV powered center pivot irrigation system under variable operating and meteorological conditions. Furthermore, its potential can be accurately explored by conducting a detailed economic analysis for a given location, considering different available crop choices

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

A Study of Factors Influencing the Adoption of Artificial Intelligence in Crisis Management

This paper presents a study on the Factors Influencing the Adoption of Artificial Intelligence (AI) in Crisis Management. The research identifies 28 AI usage factors categorized into seven groups: Large-Scale Machine Learning, Deep Learning, Reinforcement Learning, Robotics, Computer Vision, Natural Language Processing, and Internet of Things. The study conducted a questionnaire survey among 281 employees at the UAE National Crisis and Emergency Management Authority, using purposive sampling to assess their opinions regarding the impact of these usage factors on the adoption of AI in crisis management. The collected data underwent descriptive analysis to determine the ranking of AI usage factors within each of the seven groups. In terms of group rankings, Robotic emerged as the top-ranking factor, followed by Reinforcement Learning. Large-Scale Machine Learning occupied the next position, succeeded by Natural Language Processing, Deep Learning, Internet of Things, and Computer Vision, which held the lowest rank. Furthermore, when examining the correlation between these usage factor groups, it was discovered that most of them exhibited strong positive correlations, with correlation coefficients ranging from 0.634 to 0.934. This indicates that changes in one variable are associated with predictable changes in another variable. While this information can be instrumental in understanding relationships and making predictions, it does not establish a causal relationship

A Comprehensive Review and Analysis of Nanosensors for Structural Health Monitoring in Bridge Maintenance: Innovations, Challenges, and Future Perspectives

This paper presents a thorough review and detailed analysis of nanosensors for structural health monitoring (SHM) in the context of bridge maintenance. With rapid advancements in nanotechnology, nanosensors have emerged as promising tools for detecting and assessing the structural integrity of bridges. The objective of this review is to provide a comprehensive understanding of the various types of nanosensors utilized in bridge maintenance, their operating principles, fabrication techniques, and integration strategies. Furthermore, this paper explores the challenges associated with nanosensor deployment, such as signal processing, power supply, and data interpretation. Finally, the review concludes with an outlook on future developments in the field of nanosensors for SHM in bridge maintenance.publishedVersio

Buoyant Unmanned Distress Detection and Evacuation System

The Buoyant Unmanned Distress Detection and Evacuation (BUDD-E) System provides lifeguards with a solution for identifying and rescuing distressed swimmers. The BUDD-E System monitors swimmers’ locations and pulses with wristbands worn by each individual. The wristband is equipped with an emergency trigger to indicate that a swimmer requires help, and the system also prepares information for lifeguards as a live map of swimmers’ locations, color coded to indicate their safety status. In emergency situations, an unmanned robotic platform is dispatched to victims for support. As a comprehensive emergency response system, the BUDD-E System aims to prevent unnecessary loss of human life and minimize trauma from drowning incidents by complimenting the efforts of trained rescue personnel

Limitations Of Artificial Intelligence

Artificial Intelligence is a groundbreaking technology that is now an established field. It is being used to mimic human capabilities such as speaking, listening, learning, and planning by using different algorithms to process data and produce results depending on the information provided by the user. Artificial Intelligence has been used in several industries when it comes to data processing and decision making. Artificial Intelligence has been invented to help decision and solutionmaking processes using a problem-solving approach. The development of Artificial Intelligence software provides efficiency and acceleration on different kinds of workflows, which will help organizations increase their profit and reduce wastage and costs due to poor productivity. There are already many applications that Artificial Intelligence powers; some of these are Web Search, Cybersecurity, and Machine Translations. All people are now having the benefit of using Artificial Intelligence, and it is beneficial for humanity. Artificial Intelligence has many positive aspects as it produces substantial results in people\u27s daily lives and businesses today; some of the most common Artificial Intelligence technologies used by the industry are robots and Virtual Assistants. Artificial Intelligence are powered by Natural Language Processing (NLP) and Speech Recognition Platform (SRP), but it is not limited to these two (2); many factors need to be considered, but these branches help in interpretation and manipulation of the commands stipulated. Indeed, Artificial Intelligence is rapidly advancing, and many organizations are willing to try and test out what is available in the market. However, others are not convinced with the Artificial Intelligence as there are alleged ethical issues that might cause accountability in a particular manner. This thesis will explain how Artificial Intelligence is used in different fields like Law, Medicine, the Military, and others while discussing the limitations present

Decision Support and Systems Interoperability in Global Business Management

Globalization of business and volatility of financial markets has catapulted ‘cycle-time’ as a key indicator of operational efficiency in business processes. Systems automation holds the promise to augment the ability of business and healthcare networks to rapidly adapt to changes or respond, with minimal human intervention, under ideal conditions. Currently, system of systems (SOS) or organization of networks contribute minimally in making decisions because collaboration remains elusive due the challenges of complexity. Convergence and maturity of research offers the potential for a paradigm shift in interoperability. This paper explores some of these trends and related technologies. Irrespective of the characteristics of information systems, the development of various industry-contributed ontologies for knowledge and decision layers, may spur self-organizing SOS to increase the ability to sense and respond. Profitability from pervasive use of ontological frameworks and agent-based modeling may depend on the ability to use them through better enterprise and extraprise exchange