The integration of artificial intelligence (AI) into financial risk management has revolutionized the sector, offering unprecedented efficiency, accuracy, and scalability. This paper explores the transformative role of AI technologies—such as machine learning, natural language processing, and predictive analytics. Through a synthesis of existing literature and case studies, the study highlights AI's ability to enhance fraud detection, automate compliance, and enable real-time risk assessment. However, algorithmic bias, data privacy concerns, and regulatory complexities persist. The paper also evaluates the comparative advantages of AI-driven methods over traditional risk management techniques, emphasizing improvements in predictive accuracy and proactive risk mitigation. Case studies Raze Banking and Mastercard illustrate practical applications, while future trends underscore the importance of human-AI collaboration. This work advocates for responsible AI adoption to ensure financial stability and inclusivity by addressing ethical and operational challenges.

Designing of parameters plays an important role in economic design of control charts for lowering the cost and time. Manipulating sample size (n) and sampling interval (h), the effect of double exponentially weighted moving average (DEWMA) model was studied for the Economic Design (ED) of X̅ control chart. Optimum sizes and level were obtained when the characteristics of an item possesses DEWMA model. When shifts are uncertain the optimal design for DEWMA chart should be more conservative and should be implemented for benefiting the consumers as well as producers.

A variety of factors influence mental health and well-being, and the interplay of these factors can vary from person to person. This study investigates various factors that are sleep patterns, feelings of loneliness, social support in promoting mental well-being and academic pressure on mental health. A quantitative research design was used to investigate the impact of academic pressure, sleep patterns, social support, and demographic factors, including gender on the mental health and well-being of 150 students. To assess the relationships between these factors and mental health outcomes, numerical data were gathered through a questionnaire and statistical analysis was done by analysis of variance (ANOVA) at a 95% confidence interval. The findings of this study provided fascinating insights into the factors influencing mental health and well-being, shedding light on how these elements interact. Finally, the study investigates the multifaceted dynamics of factors influencing mental health and well-being, with a special emphasis on sleep patterns, social support, and academic pressure.

Transformative breakthroughs appear every few decades, revolutionizing the globe and vastly enhancing our quality of life. With the introduction of ChatGPT, another paradigm-shifting event has now occurred. This Study examine the importance of GPT-3 in the context of business. In essence, GPT-3 has the potential to transform a variety of business processes by streamlining workflows, enhancing client relationships, and spurring creative breakthroughs. This transformative tool can substantially advance businesses by amplifying effectiveness, refining customer engagements, fostering innovation, and simplifying an array of operations. This research will explore the capacity to influence diverse sectors, notably the financial market. Moreover, GPT-3 holds the potential to profoundly shape the functions of financial brokers, augmenting their efficiency, customer service, and decision-making protocols. Ultimately, we will examine how GPT-3 can elevate effectiveness, customer engagement, decision-making, and creative advancement

The sampling plan for the mean for correlated data is studied. The Operating Characteristic (OC) of the variable sampling plan for mean for correlated data are calculated and compared with the OC of known σ case.

Financial data leaks have emerged as one of the most critical risks for individuals, corporations, and governments in the digital economy. This chapter examines the nature, types, and consequences of financial leaks, drawing on high-profile cases such as the Panama Papers, LuxLeaks, and the Equifax breach. The discussion traces how leaks occur through cyberattacks, insider threats, weak security systems, and third-party vulnerabilities, while also highlighting the role of underground markets in trading stolen information. A distinct focus is placed on artificial intelligence, which is now central to both the problem and the solution. Criminal groups are increasingly utilizing AI for phishing, deepfakes, and automated intrusion, while organizations are applying AI for anomaly detection, behavioral monitoring, and predictive risk management. By integrating governance, compliance, and AI-driven security measures, the chapter outlines a comprehensive approach to safeguarding financial systems against data breaches in an era where digital threats continue to grow in scale and speed.