In February 2020, a family of four required between $14,196 pesos and $15,667 pesos per month to obtain a salary that exceeds the welfare or poverty line. This is an income that most Mexican households do not obtain. There is little social mobility in Mexico, where three quarters of the population born in households at the bottom of the social ladder do not manage to rise above the poverty line, even so, I believe that it is advisable to pursue undergraduate and especially graduate studies. By 2020, a person with a graduate degree will receive 6.6 times more than a person with a primary school education, 4.5 times more than a person with a secondary school education, 3.8 times more than a person with a high school education and 2 times more than a person with a bachelor's degree. In terms of gender, women with high school, undergraduate and graduate degrees receive the equivalent of 70% of the salary of men.In December 2021, more than 1.8 million Instituto Mexicano del Seguro Social (IMSS) affiliates receive a salary higher than $30,170 pesos, of which more than 830 thousand receive a salary higher than $47,140 pesos, corresponding to 3.2% and 1.5% of the 56.9 million people that make up the economically active employed population (formal and informal).Full article

The Nigerian Government both previous and present has introduced several policies and programmes to reduce or proffer remedial measures to militate against the negative impact of high inflationary levels on the Nigerian economy. All these measures have not led to a productive result as the inflation rate has continued to sour higher over the years. This paper aimed at examining the economic influence of the determinant factors that influence inflationary trends that are multi-dimensional and dynamic which continue to defy solutions. The data used for this work was sourced from the National Bureau of Statistics and Central Bank of Nigeria, from 1983 to 2020. The ordinary least square approach was used to analyze the data and the result shows that consumer’s price index, interest rate and total export has a positive effect on Nigeria inflation, but only the Consumer’s Price Index (CPI) have a statistically significant effect on the Nigeria inflation at 99% confidence interval. Result also shows that the exchange rate, foreign reserve, money supply, real GDP, real income and total imports has a negative effect though not statistically significant on the Nigeria inflation rate. The result of the granger causality test shows exchange rate and total imports to granger cause Nigeria inflation. It is recommended that Government should improve locally manufacture products to meet international demands to reduce total imports.Full article

The present work abounds in lathe comments on the ecological, economic policy. The first refers to the layers of thermodynamics and the economic process, but does not describe the importation of analyzing the dynamics of the economic process in terms of the transfer of matter and energy, and it is a natural system. Secondly, it is a revision of the main plant-like portraits by Marx and Engels with the historical background that plays naturalness in the process of social reproduction. The third radical commentary on reflecting the theory of value, considering that the system can count with a theory of value based on quantities of energy, is limited. The reason is sensible: as long as the capital is valued at no cost from the exploration of the work, it is natural without embargo as a limitation. And as in the last comment, we only see the political economy from a green perspective. Green in the sense that to perform economic analysis, even historical ones, it is necessary to include the natural resource variable and keep the manager accountable with critical info.Full article

The concept of the green economy is one of the global strategies to face contemporary societies' economic and environmental crises. Methodologically, the conceptualization, objectives, implementation, and criticism of various sectors of society to this new economic paradigm are addressed. It was found that authors and civil organizations have great expectations in the face of the challenges and challenges of this global strategy that has within its objectives sustainability, the eradication of poverty, and the inclusion of vulnerable social sectors. It is concluded that the green economy can contribute to maintaining a healthy environment and the proper use of ecosystem services, both for the present generation and for future generations.Full article

The closure of physical stores due to lockdown and social distancing measures led consumers to ramp up online purchasing intention, which in turn accelerated global e-commerce market growth, but caution must be ensured to prevent cyber frauds.Full article

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].Full article

Innovations in computing and communication technologies are reshaping finance. The seismic changes are casting uncertainty about the future of financial services. On one hand, fintech evangelists project a rosy future, asserting that the fast-moving algorithms can deliver low-cost financial services intuitively, customized to meet robust consumer expectations. On the other hand, many finance veterans fret that the traditional banking model could disintermediate, bleeding banks via a ‘death by a thousand cuts’, reducing them to passive portfolio holders with no direct customer relationship, eclipsed by digital giants which use their enormous treasure troves of customer data to offer banking as an added service with nearly free cost. Amidst the upbeat technological promises and apocalyptic forebodings, there are two constant, mostly agreed-upon, truths. The first is the vital importance of data. Advances in the internet, cloud computing, and record-keeping technologies are producing an ‘exponential growth in the volume and detail of data’. Some of this big data are personal information. Smartphones are deployed in almost all developed and emerging economies, serving as little spies; tracking, recording location histories, social networks, and app usage of their unsuspecting owners; often with a great degree of precision. ‘People are walking data-factories’ in this ‘mobile digital society’. Data are the fermentation of these global exchanges, electronic commerce and communication, and financial transactions. To just take Facebook as an example, it shares 30 million people a day through updates and posts, hosting personal information on 2.23 billion users. To the alarm of the uninformed public, much of this information is available for commercial harvest. The second constant is the rise of intelligent solutions. Consumers today—be it disclosed or not—are fed tailored clothes, music, film, holiday packages—almost anything you like, notably dynamic pricing, varying in accordance with individual profiles, or personalized search results. The availability of powerful computers has enabled comparable applications that are intended to make the system more responsive to their customer profiles and desires, or to capitalize competitive business possibilities. Such changes will transform the financial industry and occupy a prominent position among the mechanisms of policy competition, reshaping the way in which financial services are bestowed and led on the demand side.Full article

This paper includes the impacts of the Internet of Things (IoT), Big Data, and other emerging technologies in the vehicle production sector, digital connectivity, and sustainable transport system. Automated and intelligent transportation for safe, efficient, and sustainable transport systems will be stressed. Key factors to promote automated or connected vehicles including connected environment, integration of all transport modes, advanced cooperative systems, and policy enforcement will be discussed. This paper contains the Axiomatic Categorisation Framework (AFS) for the dynamic alignment in a collection of disparate functions in cyber-physical systems (CPS). Developed system is enhanced for breaking the rules within autonomous vehicles (AV). It means the human personal injury is inevitable while the vehicle does not do any rules. Especially in complicated traffic situations, many of the constraints are mutually exclusive, and there is no way to obey all of them at a time. Also, there is no way to ensure that the self-driving vehicle has priority in all situations [1]. Public distrust in AV systems has to be increased and the investment in this technology has to slow down. Instead, a human driver should be partially responsible for operation. The development of a driver-behavior assistant (DBA) system is proposed, which should be able to break the rules for the distances of such slow development. It is intended to be effective in non-deterministic situations while maintaining the safety of the AV and those involved in the event. A driver's actions would not only be acceptable as a driving strategy but also would be predictable, and therefore other road users could unambiguously react.Full article

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.Full article

Through a digitally connected ecosystem, the innovative realm of fintech significantly enhances human capabilities across various dimensions. AI-based fintech solutions are increasingly proving to be invaluable by providing effective enforcement of regulations that ensure compliance and protect stakeholders involved. Numerous expert investigations conducted in the arena of high-technology litigation have reinforced both the pressing need and the immense value of enforced compliance in today's fast-paced digital landscape. Open banking APIs have boldly pioneered this critical regulatory enforcement role, allowing broader access and improved services for consumers. Predictive AI certainty, facilitated through sophisticated validation systems, represented a fundamental evolution in their rule-based legal formulations that govern many aspects of financial transactions. These advanced products were deployed within global legislative codes, allowing for standardized practices, and consequently, all market sectors quickly adopted them to ensure they remain competitive and compliant. During the latest of these professionals' encouraging comments, it became clear that awareness of the inception of these groundbreaking innovations must be convened into a steadfast commitment to continue launching natural language processing products that can refine consumer interaction. Since this pivotal point, the increasing dependency of the financial expert community on these incisive factors underscores the paramount importance they now hold for their clients and end users alike, shaping the future of finance in profound ways [1].Full article

This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without considering external regulatory frameworks. This research draws on the cognitive model of regulation that looks at regulatory compliance as a social construct. It uses a triangulation research method comprising literature review, interview of trade compliance experts, and questionnaire survey of compliance practitioners to understand how regulation affects compliance and what role ICTs play in implementing compliance. The findings of this study present a regulatory compliance framework comprising four cognitive stages and a conceptual regulatory compliance system that presents how BDA and AI automation are applied to mitigate regulatory complexity and enhance regulatory compliance. The conceptual regulatory compliance system shows how BDA and AI enable institutions to dynamically assess regulatory risk, automatically monitor compliance, and intelligently predict risk violations mitigating regulatory complexity and preventing producing unnecessary documents. It provides theoretical contributions to understanding regulatory evolution and compliance and practical implications for understanding how regulation evolves to be more complicated and elements of a regulatory compliance system mitigate proliferating regulations. Additionally, it provides avenues for future research into the relationship between competing regulatory mandates and how institutions cope with that. Regulations are important for ensuring compliance and governance in finance and to curb systemic risk. Complying with regulations is difficult due to their growing volume, complexity, and fragmentation. Institutions use large-scale Information and Communication Technologies (ICTs), such as Big Data Analytics (BDA) and Artificial Intelligence (AI) automation, to monitor compliance and mitigate regulatory complexity. However, less is known about how firms comply with regulation. Most literature does not thoroughly investigate regulatory elements nor explicitly relate them to compliance.Full article

With unclaimed property assets reaching record levels, businesses have become, in some cases, overwhelmed and hamstrung by stagnant, unoptimized processes. That sentiment is compounded by ever-evolving regulatory changes, resulting in organizations struggling to hit compliance deadlines while delivering an optimal claimant experience. Often, early systems had periods of short-term success but are on the verge of obsolescence, resulting in stressed workflows and cumbersome integrations. Deploying an integrated IT infrastructure, supported by cloud-enabled AI, represents the quickest path to modernizing unclaimed property management. A fully integrated IT infrastructure is crucial to optimize the management of unclaimed property [1]. When lone solutions exist across an organization, companies miss out on automation opportunities generated through the interconnectedness of systems and data. AI presents organizations with the opportunity to traverse these gaps, enabling a vast library of applications to improve the perturbed workflows of unclaimed property teams. Automated data extraction, document comparison, fraudulent claim detection, and workflow completion analysis are just a few popular applications well suited for the unclaimed property space. In addition to the lagging technology currently deployed by many organizations, the unclaimed property landscape itself is evolving. Compliance issuance, asset availability, rates, the ability to collect fraudulently posted claims, and the claimant experience have all become hot-button items that are now front of mind for regulation agencies and businesses alike. Issuing duplication letters in a compliant manner, accommodating claimant inquiries regarding held assets, and managing, processing, and understanding the operational impact of rate changes are vexing problems many organizations now find themselves playing catch-up to address. The opportunity posed by cloud-enabled AI is furthered by economic, regulatory, and report cycle pressures on unclaimed property teams to do more with the same size or fewer resources. It’s now no longer simply a case of hitting the audit date deadline and checking off a box but an emerging priority for businesses at all sides of the market, from Fortune 500 to mid-market firms. In-house shared service teams are comfortable in areas of monitoring and curating business data; however, unclaimed property is an unknown territory with a learning curve, compliance gaps, and operational holes that, if ignored, stand to scale up exponentially. The combined fallout from regulatory changes and the recent pandemic have only made the situation riskier, with increased volatility in balancing time-sensitive tasks against stringent regulatory deadlines and growing claimant outreach.Full article

Imagine a consumer financial services company with 20 million customers. Its sales and marketing organizations collaborate across product lines, deploying hundreds of marketing campaigns each quarter that aim to increase customer product usage and/or cross-buying of products. Each campaign is based on forecasts of customer responses derived from predictive models updated every quarter. The goals of these models are to achieve large return on investment ratios and to maximize contribution to local profit centers. What’s important is that their modeling is based only on data created, curated and maintained by these marketing organizations. The difference today is that the modeling is no longer based solely on a small number of response-determined variables that are constantly assessed in terms of importance. A quarterly campaign update generates hundreds of statistical models — involving campaign responses, purchase-lag time, the relative magnitude of the direct effect, and the cross-buying effects — using thousands of variables, including customer demographics, life stage, product transactions, household composition, and customer service history. It’s a network of models, not just a table of variable-by-residual importance values. But that’s only part of the story of data products. The predictive modeling utilized by these campaign plans is based on analytics and data preparation, which are data products in their most diminutive form. These products would be even more elementary were they not crafted quarterly by highly skilled, experienced modelers using advanced software and processes. Most companies have enough data to create models that contain not simply hundreds of variables, but thousands, so that the focus can return to information instead of data reduction. These models largely replace the internal econometric models previously used to produce advanced forecasts in the absence of campaign modeling. People used these forecasts to simulate ROI and contribution forecasts for the planned campaigns. In the old days, reliance on econometrically forecast ROI-guideline contribution values reduced the reliance on the marketing campaign modelers because of a lack of trust in their predictive ability.Full article

Digital signal processing played a central role in two practical studies addressing challenging problems related to high-volume SWIFT financial messaging flows conveyed by the interconnected banking network. Technical methods and results are summarized here for each study, with the links to fundamental concepts underlying the work shown in parentheses. The first addresses real-time fraud detection, integrating pattern recognition and anomaly scoring procedures into a latency conscious processing system. The second focuses on minimizing delay without degrading detection accuracy, balancing speed and fidelity in filter design and control. Together, they demonstrate the potential for applying a DSP perspective to broad classes of problems encountered in processing financial messaging data. The first study extends work on a signal representation of financial messaging data streams and the associated noise characteristics by developing a vocabulary that translates real-world fraud patterns into DSP operations. Examination of the resulting choice of signal features, combined with considerations of detection speed, form the basis for details about implementing the pattern-recognition and anomaly-scoring tasks within a streaming-processing architecture.Full article

Data quality and data lineage are critical concerns for organizations mandated to comply with stringent regulatory regimes. This paper analyses the latest developments in the governance of data quality and data lineage within a regulated financial services organisation. It sets out the underlying regulatory context, describes the concepts employed in the business environment, summarizes how data quality is captured and monitored, examines the artefacts that record data lineage, reviews the roles and responsibilities of staff who implement the necessary processes, and maps areas where improvements are possible. The internal organization and processes of regulated data platforms are shaped not only by the capabilities prescribed by their technical architecture but also by the regulatory regimes under which they operate. These mandates, in particular, require rigorous examination of four aspects of data quality — accuracy, completeness, consistency, and timeliness — and detailed documentation of how data arrives in its final form in the repository. Although data monitoring, alerting, assessment, and remediation are well established, provenance capture remains an area ripe for further investment.Full article