This research is conducted with main aim of assessing the economic consequences of Covid-19 pandemic in Western Ethiopia. Primary data is collected through questionnaire and interview from 320 respondents living in three zones of Western Ethiopia. The study areas (zones) are selected purposively from Oromia region; however, the respondents are sampled by employing random sampling technique. The respondents were stratified as community members, daily laborer, business owners, government sector and NGOs employees. Exploratory research design was adopted to achieve the research objectives. Simple descriptive statistics and ordinary least square regression model are used to analyze and interpret the collected data. The study results disclose that majority of community have good awareness about the pandemic and social interaction is reduced due to social distancing. Majority of respondents realize the negative impact of Covid-19 on their economy; reduction of office services; and reduced access to market; and absence of strong support from the government. The great severity of Covid-19 impacts is failed on daily laborers. The regression result shows that sales, experience in business, education level in years, employment status of the respondent, number of workers in the business and work hours per week are positively and significantly influencing daily income of business owner before and after the pandemic outbreak. It is advised the stakeholders to give frequent follow-up and support particularly for daily laborers and small business holders to reduce the future socio-economic impacts of Covid-19 pandemic.

The linkage between fiscal policy and economic growth has attracted the attention of empirical investigators in economic literatures. This study systematically reviewed sub-Saharan African literatures just to examine the relationship between fiscal policy and economic growth. To achieve the objective of the study, 11(eleven) empirical literatures in 7(seven) Sub-Saharan African literature studied between the year 2013 and 2020 were selected. As regard to sampling, random sampling was used to enhance the representatives of the sample. The criteria for selection were the relevance of the topic and the geographical area of studies. In this procedure, the first geographical area and then studies were selected. In the second stage relevance of the studies was considered as inclusion crateria. Descriptive statistics was used for data analysis. The result shows that the studies selected for review are more interested in the long-run relationship between fiscal policy and economic growth than its short-run effect. This implies that Sub-Saharan African countries are using fiscal policy for economic growth rather than stabilization. Regarding consensus on the relationship between the two variables, majority of the literature selected for review found that fiscal policy is positively and significantly affecting the economic growth of the Sub-Saharan African countries. The major fiscal policy tools used in the selected literature are government expenditure and tax reflecting the similarity of economic structures and compositions in sub-Saharan Africa. In conclusion, the compositions of fiscal policy instruments are almost similar in sub-Saharan Africa. The policy implication is that policymakers in sub-Saharan Africa should give due attention to the composition of fiscal policy tools.

In mid 2000s, a new Fintech era has commenced which is known as “Crowd lending” or “FinTech Credit” whereby credit activities are realized online through internet platforms that match borrowers with lenders (investors). Those kinds of lending activities are named Peer to Peer Lending (P2P). The purpose of this study to elaborate the functioning and regulatory framework of P2P lending in US and China. Those two countries can be considered as two conspicuous example of the application of P2P lending especially in terms of regulation. China transformed its P2P market in 2015 after a long loose regulation period and US from the very beginning applied a strict regulation on the market. By that way, a set of terms of regulation is aimed to be proposed especially for the emerging market countries. It is thought that P2P lending can contribute to the economic development of the emerging market countries if it is applied properly. The contribution of this study to newly developing literature is to provide a comparison and also a set of terms of regulation to be applied in the emerging market countries.

This article provides an overview of the opportunities and risks of Diversity Management. It also attempts to close the research gap that results from the interrelationship between Diversity Man-agement and the War of Talents. The thesis is that the full benefits of Diversity Management in the War of Talents can only be real-ized if an appropriate diversity strategy has been established and communicated. Through teleological and historical perspectives as well as causal research of the topics and inter-faces, research questions will be answered and research gaps will be closed. The article thus pre-sents the essential theories on the significance of Diversity Management in the War of Talents. Mutual dependencies can thus be identified in order to assess the impact of Diversity Manage-ment. The bottom line is that diversity efforts must be concentrated and qualitative. A solid strategy forms the basis for this. However, external, non-operational influences are also of decisive im-portance for successful Diversity Management.

The savant syndrome is a syndrome that is associated with certain cognitive disorders and is as-sociated with limitations but also with individual significant abilities. The nature and expression of the syndrome is very heterogeneous, which means that many facets of the syndrome have not yet been researched. The object of the study described below was to approach the research gap on the topic of "work and employment" in particular with initial results, since up to now both topics have only been adequately researched in isolation. In doing so, the influence of the profit orienta-tion of organisations on the employability of savants was investigated. Correlations between non-profit organisations and companies with other parameters such as the implementation of a company health management or the general employment of severely disabled people could al-ready be proven in previously conducted studies. The method used was a quantitative survey of 465 dependent employees. The ability to integrate was expressed by a total score, which was an additive index consisting of the dimensions strengths, weaknesses and framework conditions. Although the proportion of severely disabled employees is higher in the public sector and compa-ny health management is also partly obligatory, no significant differences in the employability of island gifted people could be found compared to the free economy.

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).

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.

A short review concerning the distinction between ancient and modern grains or ancient and modern varieties of wheat, cereals and pseudocereals, along with their quality related mainly to human health and that of the environment is provided. Modern plant breeding, especially that started before the Green Revolution, based mainly on gross selection and very few crosses among local varieties/populations may be considered ancient grains, while those obtained after the Green revolution, based on intensive and multiple crosses among local and foreign varieties, including those obtained by induced mutation, may be considered modern grains. According to recent researches, it seems that ancient grains are healthier than the modern ones. The former would also have a lower environmental and agricultural impact than the latter. Since the picture on this topic is not yet clear I was asked to throw on it more light. In fact, most of researchers in the field do not understand difference among ancient and modern varieties. Thus, the objective of this short paper was to clarify and stimulate more research, possibly with a multidisciplinary approach. In Italy, for instance, there are ad hoc research projects that should be more adequately financed and supported, during their development. To make it easy, I have mentioned and listed more than thirty references.

This study raises the problem of the non-repayment of agricultural credits by producers who are members of the Community Growth Mutual (MC²), in this period of COVID-19. It questions the economic mores in force in most member countries of the Organization for the Harmonization of Business Law in Africa (OHADA), where credit has become difficult for small rural farmers; And refers to the theory of the vicious circle of poverty, which advocates an indispensable recourse to foreign capital in the event of financial breakdown, as a means of increasing capital. Since the capital of rural producers remains insufficient and their possibility of reinvestment decreases, then becomes zero because of agricultural credit. To understand the factors of the non-repayment of these credits, data were collected from 100 agro-sylvo-pastoral producers of the Bayangam group (West-Cameroon) of both sexes, aged at least 18 years, having obtained an unpaid credit from the MC² since 2019, and a manager of this microfinance institution. After analysis, it appears that beyond overproduction and anti-COVID-19 measures that lead to the missale or fall in prices on the market, the conditions of access to credit, the non-possession of acceptable guarantees, the misuse of the object of credit and the practice of financial cavalry by the borrower, as well as the rigidity of the procedures for prosecuting debtors significantly explain this non-repayment. It is associated with determinants such as age, level of education, marital status, type of agricultural activity of the debtor. Hence the need for flexibility of microfinance institutions vis-à-vis rural agro-sylvo-pastoral producers, who are severely affected by the economic shock of the COVID-19.

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.

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.

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.

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].

Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.

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.

Retirement planning can be a complex endeavor. One consideration is whether or not to invest in an Individual Retirement Account (IRA). The present study compares the effect of several contributions to a traditional, Roth, and rollover IRA. The returns generated for each model are derived from the historic growth rates of the S&P 500 over 40 years. Results are presented in terms of employer match, taxes due, and the number of shares utilized in the long-term investment strategy for each withdrawal method. Results show traditional IRA contributions or Roth IRA contributions are equally matched until employment termination. Taking an active role in managing the investment strategy, possibly by working with a financial representative, suggests a more favorable positioning upon employment termination [1]. Traditional and other pre-tax plans usually do not have an employer match, are usually paired with decreased taxes paid, and the number of shares available to the long-term investment strategy is somewhat reduced. In all cases, risk is increased. Rollover IRAs enjoy a match, lower taxes, and decrease the amount of calculated risk involved. A certified financial planner should be the resource of choice to determine how corporate retirement planning programs fit into the overall investment strategy.

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.

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.

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].

Generative AI refers to deep learning technology that can automatically produce original text, images, audio, video, and other outputs. With its emerging capabilities, Generative AI can radically change the dynamics of key operational processes in most industries. In this document, we illustrate how it is possible to integrate Generative AI technologies into the Financial Reporting System (FRS) of a corporation. The integration will allow the FRS to deliver on demand concise and lucid insights to its associated users on what is happening in the corporation and different aspects of the corporation performance assessment, such as its liquidity, solvency, profitability, organizational structure, and share buy back decision. The integration will also facilitate the delivery of what-if analyses associated with different strategic and tactical decisions taken by the corporation management, such as capital budgeting and profit distribution decisions. The unique added value of several attributes of these insightful analytics is automating the responses to ongoing requests of the FRS users on the corporation. Generative AI capabilities are rapidly expanding. The integration can be applied not only for the corporate FRS but any FRS at the national or global levels delivered by a central bank or an accounting standards setter. Any of these FRS can be made into a unique “hub” for the integrated Generative AI technologies. An equally innovative possible generalized integration could put any corporate process at the center and its supporting FRS tasks and deliverables in its periphery.

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.

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.

While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. To this end, in this paper authors present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders [1]. Large information databases on various public issues exist, but their usage for public policy formulation and impact analysis has been limited so far, as no cloud-based service ecosystem exists to facilitate their efficient exploitation. With the increasing availability and importance of both public big and traditional data, the need to extract, link and utilize such information efficiently has arisen. Current data-driven web technologies and models are not aligned with the needs of this domain, and therefore, potential candidates for big data, cloud-based and service-oriented public policy analysis solutions should be investigated, piloted and demonstrated [2]. This paper presents the conceptual architecture of such an ecosystem based on the capabilities of state-of-the-art cloud and web technologies, as well as the requirements of its users.

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.

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.

Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, & Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, & Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, & Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.

The global banking industry is subject to ever-growing regulatory requirements, designed to prevent financial tour de force repeats tearing through the world economy. The changes are incomplete and new rules being enacted each year. Implementing and executing these rules and regulations requires the guiding principles from senior management to reach the product desks in a clear and efficient way. Technical systems must implement these rules. Differences in interpretation, implementation, and warnings must be addressed during normal operations. Most importantly, systems must provide warning alerts to management and the business as early as possible, to allow for proper handling. History has shown that the importance of early warnings has been overlooked repeatedly. Real-time capabilities are essential to meet these business needs. Organizations must therefore be ready to embrace a next-generation architecture that enables real-time alert and warning generation. Systems based on a streaming architecture, combined with systems enabling the real-time flow of events between domains supported by orchestration, provide a solid foundation to meet these requirements.

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.