“To Be Twice as Good to Get Half” is a common mindset among high aspiration and ambition Black individuals in the U.S., capturing the lived reality of Minorities’ Diminished Returns (MDRs). This paper explains that MDRs reflect how, even with high levels of ambition, self-efficacy, education, and income, Black individuals and other marginalized groups do not experience the same protective benefits for health and well-being as White populations. Systemic obstacles embedded within U.S. society weaken the expected returns on socioeconomic achievements for racialized individuals, creating a reality where “being twice as good” still results in lesser outcomes. High-SES Black individuals, for instance, continue to face significant risks for adverse outcomes, such as depression and chronic disease, due to structural inequities across domains like labor market discrimination, segregation, and accumulated disadvantage from childhood. Our analysis identifies key mechanisms—including interpersonal discrimination, lower-quality education, and structural racism in sectors like banking, policing, and real estate—that erode the protective effects of SES across racial lines. Mediating factors, such as chronic stress, allostatic load, and epigenetic changes over the life course, further compound these diminished returns, weakening the expected physical and mental health benefits. Drawing on extensive evidence from U.S. national and local datasets and corroborated by international studies, this paper underscores the necessity of policies that dismantle structural barriers rather than relying solely on SES improvements. Recommendations include implementing multi-sectoral policies, recognizing the unique challenges of middle-class non-White populations, and approaching policy with humility, acknowledging that achieving equity is a long-term endeavor. By challenging the “bootstraps” narrative, this paper advocates for structural interventions aimed at genuine health and economic equity for all racial and socioeconomic groups. While we provide an in-depth analysis of MDRs’ phenomena, mechanisms, mediators, and policy implications, the experience is often distilled as, “I have to be twice as good to get half.”

The current financial services sector is realising considerable changes in its operations due to development in technology and embracing of digital platforms. This evolution is changing the established concepts of business, consumers and channels of delivery of services. Financial services firms are changing the way they work through digital transformation due to developments in technology, changes in customer needs, and an increase in emphasis on sustainability. Understanding the opportunities, risks, and new trends in digital transformation is the focus of this paper. Opportunities include efficient real-time decision-making processes, increased transparency and better process controls, which are balanced by the threats of change management, dubious organization-technology fit, and high implementation costs. The study also examines recent advancements, including the application of machine learning and artificial intelligence, developments in mobile and online banking, integration of blockchain, and increasing focus on security and personalised banking. A literature review yields some findings from different studies on rural financial services, the evolution of the blockchain, drivers of digital transformation, cloud-based learning approaches, and emerging sustainability practices. All of these results suggest that more strategic planning, analytics, and more focus on ensuring that organisational objectives are met with transformations should be pursued. Hence, this research findings add to the existing literature in determining how innovative and digital technologies are likely to transform the financial services sector and advance sustainability.

In today's fast-changing world, digital has become a way of life in every single field, and it is affecting all industries by providing multi-channel connectivity with people. In the banking industry, moving to the digital age allows for more improvements in customer-related operations and transaction-related operations within a day. These studies are from the perspective of customers. Customers prefer the flexibility of using digital financial services. Banking clients are commonly given technology-related services, whether they are online or not. Now, banks are focused on providing instant credit card issuance and personalized financial solution services to their clients. They are responsible for managing mass affluent clients who conduct transactions approximately the same as mass retail clients. Providing personalized services on time to individual end users will significantly enhance customer value with the banks. Customers who use the bank digitally perform more operations than those who go to the branch. Thus, they become more valuable clients for the banks. This strategic approach to the digitization process takes place in this fast-changing environment, and the major steps of this journey will be explained in the next chapters [1].

We investigate and analyze the trends and behaviors in credit card fraud attacks and transactions. First, we perform logical analysis to find hidden patterns and trends, then we leverage game-theoretical models to illustrate the potential strategies of both the attackers and defenders. Next, we demonstrate the strength of industry-scale, privacy-preserving artificial intelligence solutions by presenting the results from our recent exploratory study in this respect. Furthermore, we describe the intrinsic challenges in the context of developing reliable predictive models using more stringent protocols, and hence the need for sector-specific benchmark datasets, and provide potential solutions based on state-of-the-art privacy models. Finally, we conclude the paper by discussing future research lines on the topic, and also the possible real-life implications. The paper underscores the challenges in creating robust AI models for the banking sector. The results also showcase that privacy-preserving AI models can potentially augment sharing capabilities while mitigating liability issues of public-private sector partnerships [1].