Leveraging distributed cloud intelligence for hyper-personalized wealth management and robo-advisory services

Financial literacy has become a critical factor influencing individuals’ financial decision-making and the effectiveness of financial service marketing. With the increasing complexity of wealth management products and services, clients’ ability to understand financial concepts significantly affects their awareness, perception, and acceptance of professional financial advisory services. The present study examines the role of financial literacy in enhancing the marketing effectiveness of wealth management services, with special reference to Parinita Finance and Wealth Management, Amravati. The study aims to assess the level of financial literacy among clients and analyze its influence on customer awareness, perception, decision-making behavior, and acceptance of wealth management products. Further, it evaluates the impact of financial literacy on key dimensions of marketing effectiveness, namely customer acquisition, retention, and satisfaction. A descriptive and analytical research design was adopted, using primary data collected through a structured questionnaire administered to 120 clients of the firm. Secondary data were sourced from academic journals, reports, and relevant financial literature. Statistical tools such as percentage analysis, mean scores, correlation analysis, regression analysis, and chi-square tests were employed for data interpretation. The findings reveal that higher financial literacy significantly enhances customers’ understanding, trust, and confidence in wealth management services, leading to improved decision-making and greater acceptance of financial products. Financially literate clients also contribute positively to marketing effectiveness through higher satisfaction, retention, and positive word-of-mouth promotion. The study concludes that strengthening financial literacy initiatives can serve as an effective marketing strategy for wealth management firms. Keywords: Financial Literacy, Wealth Management Services, Marketing Effectiveness, Customer Awareness, Decision-Making

Robotic Process Automation (RPA) is rapidly transforming the landscape of investment and wealth management by enhancing efficiency, accuracy, and decision-making. This chapter explores how RPA is being integrated into portfolio management strategies. It examines the impact of RPA on client onboarding, compliance, portfolio rebalancing, and risk management, highlighting real-world use cases in asset and wealth management firms. The discussion also covers challenges related to implementation, such as regulatory compliance, data security, and integration with legacy systems. Furthermore, the chapter investigates how RPA, when combined with artificial intelligence and analytics, can support personalized investment solutions and improve customer experience. By redefining traditional processes, RPA not only reduces operational costs but also empowers financial advisors to focus more on strategic planning and client engagement. This chapter offers practical insights for institutions looking to adopt RPA-driven solutions in their portfolio strategies.

The paper reports on a case study regarding the integration of heterogeneous data sources, coming from different institutions, needed to support several studies related to the health status of the population of L’Aquila (Italy) after the earthquake of April 6th 2009. In detail, the paper initially describes all the health studies, then the data sources required to support them, finally proposes a simplified federated architecture and a straightforward technological solution to implement it.

This study explores customer behavior towards secondary banking products, including credit cards, insurance, and wealth management services, among clients of commercial banks. It identifies key factors influencing adoption decisions, such as product awareness, perceived value, and trust in the bank, while also examining barriers to uptake. The findings provide insights for banks to enhance cross-selling strategies, improve customer satisfaction, and increase retention through tailored product offerings. This case study investigates the impact of credit cards, insurance, and wealth management products on customer retention within commercial banks. Through a comprehensive analysis of customer behavior and preferences, the study identifies key factors that influence retention rates. This descriptive study, aimed to analyze customer behavior towards secondary banking products such as credit cards, insurance, and wealth management services. Utilizing a simple random sampling method, a sample of 330 customers was surveyed using a questionnaire as the primary data collection tool. The findings revealed that these secondary products have a minimal impact on customer retention, indicating the need for banks to develop more attractive schemes to enhance customer loyalty and retention. Furthermore, effective wealth management strategies tailored to individual customer needs contribute significantly to long-term retention. The study concludes by highlighting the importance of personalized financial services in fostering strong relationships and reducing customer attrition in the competitive banking sector.

The chapter addresses how the zakat institution is perhaps the main institution relevant to the field of investment and wealth management. The benefits underlying zakat are not fully appreciated and the concept remains bound by a narrow understanding of zakat’s charity aspect in alleviating poverty. By highlighting the unique features, the institution of zakat brings to the discourse, it argues for the critical importance of Islamic practices, ethics, and motivations found in zakat. The paper first outlines the notion of investment and wealth management, especially as it concerns a more productive human and capital development. Secondly, the chapter argues that, despite the redistribution of wealth benefits inherent in zakat as an investment and wealth management strategy, it appears not to be fully explored. This paper conceptualizes and clarifies the various facets of zakat in sustaining investment and wealth management. The conclusion outlines the values of zakat to investment and wealth managers as a way of improving wealth as well as investment portfolio management or strategies, especially when targeting Muslim clients and engaging the Islamic market.KeywordsInvestmentWealth managementZakatIslamic economic system

A method and software framework for enriching private biomedical sources with data from public online repositories

The multi-national MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate the utilisation of a wide range of health and social care data. The platform will enable the integration of heterogeneous data sources, providing privacy-preserving analytics, forecasting tools and bespoke visualisations of actionable epidemiological data. An evaluation framework starting with a logic model and using the principles of realist evaluation developed working with users, and software developers. The tools used are a series of parallel case studies to address the requirements of stakeholder groups at critical time points during the project to ensure IT systems development is in line with user’s requirements. The process includes longitudinal interviews with stakeholders, regular feedback to users and developers, and measurement of stakeholder’s attitudes to the project using Q-methodology.

In recent years, research on digital twins in the construction sector has grown significantly, particularly since 2018. However, most studies focus on isolated lifecycle phases and give limited attention to developing practices that address all stages of the building lifecycle. Current applications of digital twins in construction are largely concentrated on design and engineering, where Building Information Modeling (BIM) serves as the primary input. This dominance stems from BIM's role as an existing digital representation of built assets, which can be extended into a digital twin. One of the major barriers to wider implementation lies in data integration and interoperability—specifically, the challenge of consolidating information from diverse sources, formats, and lifecycle stages. Future research should prioritize the standardization of data formats, protocols, and application programming interfaces (APIs) to facilitate seamless exchange across digital twin systems. Additionally, the development of advanced data governance frameworks is essential to ensure accuracy, completeness, and security of information. Greater exploration into semantic data modeling and ontologies will also support the integration of heterogeneous data sources. Beyond technical issues, it is equally important to investigate the costs, benefits, and potential drawbacks of adopting digital twins across the entire building lifecycle. Expanding the scope of applications beyond design and engineering to include construction, operation and maintenance, as well as demolition and recovery phases, will be crucial. This paper therefore outlines the current state of digital twin research in construction, identifies critical challenges, and highlights key directions for future development.

Businesses find it hard to combine data from different systems, formats, and sources. Some examples of these sources are structured data in relational databases, semi-structured data like JSON or XML, and unstructured data like text or multimedia files. Businesses need to be able to handle and mix all of these different types of data in order to get the most out of them. You can now utilize an architecture to put together data from languages that are distinct. This framework is meant to help you find a way to handle a lot of various types of data and sources without slowing down or losing consistency. Cloud storage, APIs, and machine learning are just a few of the cutting-edge technologies that the framework leverages to make sure that data systems can work together without any hassles. It allows businesses to combine their data while ensuring that all of their systems are accurate and high-quality. The platform also solves the problem of scalability, which means that companies can simply handle more and more data without any issues or delays. This plan helps businesses maintain better track of their data, which makes it easier to put together and less likely to make mistakes. Companies should always be able to see their data in the same way, no matter where it comes from or what format it is in. This makes it easier to choose. The answer also helps with data governance since it allows people ways to keep track of data history, set security standards, and make sure they follow the rules. When you have to interact with diverse kinds of data, the polyglot data integration framework is an excellent method to deal with problems that come up. In a world where data is king, it helps businesses get the most out of their data, keep things running smoothly, and remain ahead of the competition

In an era where data is the cornerstone of decision-making, organizations face the challenge of integrating diverse data sources from heterogeneous environments. Traditional Extract, Transform, and Load (ETL) processes focus primarily on syntactic transformations, ensuring compatibility between disparate datasets. However, these approaches fall short in addressing semantic inconsistencies that arise from differences in meaning, context, and relationships among data [6][7]. This paper proposes a semantic-aware ETL framework that integrates ontology-based reasoning and dynamic rule generation to bridge this gap. The framework enhances data coherence and alignment by leveraging cutting-edge semantic technologies, including Apache Jena for ontology management, SPARQL for semantic querying, and Protégé for ontology design. Detailed analysis, design, and evaluation of the framework showcase its ability to revolutionize ETL processes in data warehousing, enabling robust integration of heterogeneous data sources.

Ontology matching is the process of finding semantic relationships between entities of different ontologies. It plays an important role in the integration of heterogeneous data sources. To find semantic correspondences between entities, various matchers can be used. When multiple matchers are integrated into a matching system, the issue of combining these matchers together and determining the importance of every basic matcher arises. In a parallel composition of basic matchers, several matchers are used independently and then their results are aggregated. In this paper, we propose a method for automatically determining weights of basic matchers in their parallel composition and the architecture of the matching system. An evaluation of the method is performed on case study examples concerning the matching of four ontology pairs and a comparison with other aggregation methods is given. Additionally, the architecture of the matching system based on our automatic weighted aggregation method is presented.

Artificial intelligence (AI) has rapidly emerged as a powerful tool in the global effort to prevent and control infectious diseases. With the growing burden of pandemics, antimicrobial resistance (AMR), and travel-related outbreaks, traditional public health responses face significant challenges in speed, scale, and coordination. AI technologies—ranging from machine learning (ML) and natural language processing (NLP) to deep learning and generative models—offer new capabilities across surveillance, early outbreak detection, diagnostics, modeling, vaccine development, public communication, and resource allocation. This review synthesizes the expanding applications of AI in infectious disease prevention and control and highlights its transformative potential in improving preparedness and response. AI enables real-time integration of heterogeneous data sources, supports precision diagnostics in low-resource settings, accelerates vaccine target identification, and enhances public health communication during crises. However, its implementation is not without challenges. These include data quality and bias, privacy and security, technical limitations, transparency and trust, ethical concerns, misinformation, regulatory gaps, cost and infrastructure constraints, and system fragmentation. To maximize AI’s benefits, policy frameworks should promote equitable data access, interdisciplinary collaboration, and inclusive governance mechanisms. Technical development should also prioritize transparency, adaptability, and contextual relevance. AI has the potential to reshape global infectious disease prevention and contr

In this paper we propose an ontological OLAP (on-line analytical processing) framework to integrate distributed energy sensor data. The OLAP data cube in the framework annotated with semantics with other supporting mechanisms can deal with the issues of the schema inconsistency that may result from integration of heterogeneous data sources. The proposed approach provides a way of storing, reusing and composing OLAP cubes in order to increase system usability. A prototype of the proposed framework based on a number of existing tools such as protégé, Jess, and Fuzzy J has been developed to demonstrate its feasibility.

ObjectivesCutaneous leishmaniasis is a vector-borne parasitic disease whose lasting scars can cause stigmatization and depressive symptoms. It is endemic in remote rural areas and its incidence is under-reported, while the effectiveness, as opposed to efficacy, of its treatments is largely unknown. Here we present the data management plan (DMP) of a project which includes mHealth tools to address these knowledge gaps in Colombia. The objectives of the DMP are to specify the tools and procedures for data collection, data transfer, data entry, creation of analysis dataset, monitoring and archiving.ResultsThe DMP includes data from two mobile apps: one implements a clinical prediction rule, and the other is for follow-up and treatment of confirmed cases. A desktop interface integrates these data and facilitates their linkage with other sources which include routine surveillance as well as paper and electronic case report forms. Multiple user and programming interfaces are used, as well as multiple relational and non-relational database engines. This DMP describes the successful integration of heterogeneous data sources and technologies. However the complexity of the project meant that the DMP took longer to develop than expected. We describe lessons learned which could be useful for future mHealth projects.

Large-scale energy infrastructure projects such as oil and gas developments, power generation facilities, and transmission networks are characterized by high hazard intensity, complex socio-technical interactions, and multi-contractor delivery environments. These conditions create persistent challenges for effective occupational safety risk control, particularly when traditional safety management approaches remain reactive, compliance-driven, and reliant on lagging indicators. In response, this paper develops a conceptual model for data-driven occupational safety risk control tailored to the operational and governance realities of large-scale energy projects. The proposed model conceptualizes safety risk as a dynamic and measurable system property that can be proactively managed through the systematic integration of heterogeneous data sources. These include operational process data, workforce and competency records, incident and near-miss reports, environmental conditions, and real-time monitoring technologies. The model is structured around four interrelated layers: (1) risk sensing and data acquisition, (2) analytics and risk interpretation, (3) decision support and governance integration, and (4) risk control and intervention mechanisms. Emphasis is placed on the use of leading indicators, predictive analytics, and feedback loops to enable early identification of emerging hazards and weak signals across project phases. By explicitly linking data-driven insights to decision authority, escalation pathways, and safety governance structures, the model addresses a critical gap between analytical capability and practical risk control. It also highlights the importance of organizational learning, continuous model recalibration, and cross-contractor data integration in complex project ecosystems. The conceptual contribution of this work lies in framing occupational safety management not merely as an operational function, but as an adaptive, intelligence-enabled control system embedded within project governance. The model provides a foundation for future empirical research and offers practical guidance for project owners, EPC contractors, and regulators seeking to enhance safety performance, improve risk predictability, and strengthen resilience in large-scale energy infrastructure delivery.