Using a desktop review of literature, the effect of illegal capital flows on the economic performance of Sub-Saharan Africa is examined. The review focus on articles with attention to illegal capital flows and their effects on the economic performance of Sub-Saharan Africa as a whole. By way of sampling method, purposive sampling was used, and so the desktop review focused purposively on articles published on issues of illicit financial flows and their effects on the economic performance of Ghana and Sub-Saharan Africa as a whole. The review found a high propensity of trade mis-invoicing and thus high illicit financial flows, transactions across boarders from developing countries and for that matter Sub-Saharan Africa to the developed economies. Therefore, the research recommends that customs divisions in sub-Saharan Africa should have up-to-date commodity-level world pricing information to make relatively better comparisons to detect mis-pricing and avoid such falsification and manipulation in trade. Given the high propensity of trade mis-invoicing resulting in high illicit financial flows, we recommend that cross-border transactions from developing sub-Saharan African countries be subjected to heightened scrutiny to curtail any potential traces of falsification in trade for tax evasion.

With the rise of virtual currencies, Bitcoin has gradually become one of the safe-haven tools in the financial market. During situations of worldwide outbreaks of an infectious disease, investors pay special attention to asset allocation. Therefore, this study discusses the outbreak of COVID-19 in China, which has affected financial markets and has led investors to avoid risks through investing in traditional financial products or Bitcoin. We found that during the time of the COVID-19 pneumonia, Bitcoin and gold futures were used for hedging transactions in the face of unstable Chinese market conditions and under the pursuit of investors' maximization of return on investment. Furthermore, there was also no difference between hedging through Bitcoin or gold futures; however, investors had a preference to invest in gold futures for hedging under the assumption that an investor was absolutely risk averse.

In today’s world of a globalized economy and ubiquitous digital transactions, businesses are hungry for ways to increase transaction efficiency and security. In the real economy, solutions that scale to fit transaction volume or velocity are equally valuable. This is true for clearing and settlement and for the day-to-day needs of buyers and sellers alike. Clever observers of both cash and digital transactions can spot cases where technology that supports transaction security or safety can strengthen consumer-borrower ties, mitigate default risks, and reduce recidivism. In general, a cloud solution for payment processing and merchant services solves two major barriers to optimum business technology: lack of scalability and lack of security [1]. The extension of current practice has its advantages, but new solutions unlock significant opportunities for both consumers and financial institutions [2]. The focus of this work is on the provisioning of cloud-based payment processing and merchant services to financial institutions and established global organizations, although the options available with these services mean they are potentially applicable to a wide range of group entities, including non-trading organizations, pension administrators, and group treasurers. With the increased attention to cybersecurity, a mass of data is available to assist the IT departments of the major payment processors, merchants, and acquirers to get cybersecurity on the radar of C-level executives [3]. The case is put forward for the increased targeting of and reporting to the Board’s Audit, Risk, and Liability Committees of publicly held payment processors and merchants to reduce fraud losses and mitigate the reputation and class action lawsuit risk due to data breaches. The progress of technology in the payment sector requires all stakeholders to have a collective approach in order to mitigate fraud and cybersecurity-related risks in new products and services to enhance consumer confidence and the proportion of retail cashless transactions [4].

This study aimed to evaluate the Social Studies Curriculum for Senior High Schools in Ghana to determine whether the subject has attained its goal(s) or not, using Stake’s Countenance Evaluation Model. The study took place in the Asante Akim North District. The population for study were all Social Studies teachers and students in the district. The multi-stage sampling technique was adopted for the study and the sample size was 120, made up of 100 students and 20 teachers. The concurrent mixed method approach was adopted. The main instruments used for collecting data were questionnaires, interviews (focus group and unstructured interview), observation and documentary analysis. Quantitative data were tabulated, organised, analysed and interpreted to draw sound conclusions and generalisations. The data were presented in the form of tables and percentages. Words were used to interpret the tables and percentages for easy understanding. The qualitative data responses were described and explained in the form of themes. Sometimes responses were quoted verbatim to authenticate claims made. The study looked at the congruence between what was intended to occur and what was observed before and during the implementation of the curriculum in the Asante Akim North District. On the transactions, it was revealed that teachers in the district communicate the problem the topic seeks to address and the specific objectives to the learners during instruction. Also, teachers varied their teaching methods, techniques and strategies. However, teachers failed to take students out on field trips. Also, they failed to use video documentaries to deliver their lessons inhibiting students’ creativity as they are denied the opportunity to explore and get first-hand information to build up knowledge and develop the needed attitudes and values. From the findings, it is therefore recommended that the National Council for Curriculum and Assessment should increase the time allotted to the teaching of Social Studies at the SHS level from 3-periods of 40 minutes a week to 5-periods of 40 minutes a week to give teachers and learners ample time to discuss, debate, digest and solve problems during instructional hours to enable the goal of the subject attained.

The SAP HANA Application to handle operational workloads that are consistent with transactions while also supporting intricate business analytics operations. Technically speaking, the SAP HANA database is made up of several data processing engines that work together with a distributed query processing environment to provide the entire range of data processing capabilities. This includes graph and text processing for managing semi-structured and unstructured data within the same system, as well as classical relational data that supports both row- and column-oriented physical representations in a hybrid engine. The next-generation SAP Business Suite program designed specifically for the SAP HANA Platform is called SAP S/4HANA. The key features of SAP S/4HANA are an intuitive, contemporary user interface (SAP Fiori); planning and simulation options in many conventional transactions; simplification of business processes; significantly improved transaction efficiency; faster analytics.

Due to influence of Internet, this e-commerce sector has developed rapidly. Most of the online retailing or selling businesses are seeking for way for predicting their products demand. Sales forecasting may help retailers develop a sales strategy that will enhance sales and attract more money and investment. The current research work puts forward a machine learning framework to forecast E-commerce sales for strategic management using a dataset of E-commerce transactions. With 70 percent of the data for train and 30 percent for test, three models were produced, namely, Random Forest, Decision Tree, and XGBoost. In order to evaluate the models, performance measures inclusive of R-squared (R²) and Root Mean Squared Error (RMSE) were employed. Thus, the XGBoost model was the most accurate in marketing predictive capabilities for E-commerce sales with the R² score of 96.3%. This has demonstrated the increased capability of XGBoost algorithm to forecast E-commerce monthly sales more accurately than other models and can assist decision makers for managing inventory and arriving smart and quick decisions in this rapidly growing E-commerce market. The findings reiterate the importance of using advanced analytics in order to drive effectiveness and customer experience within E-commerce sector.

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].

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.

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].

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.