Next-Generation Personalized Investment Recommendations

Recent advances in Big Data and Artificial Intelligence have created new opportunities for AI-based agents, referred to as Robo-Advisors, to provide financial advice and recommendations to investors. In this chapter, we will introduce the concept of investment recommendation and describe how automated services for this task can be developed and tested. In particular, this chapter covers the following core topics: (1) the legal landscape for investment recommendation systems, (2) what financial asset recommendation is and what data it needs to function, (3) how to clean and curate that data, (4) approaches to build/train asset recommendation models and (5) how to evaluate such systems prior to putting them into production

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

Technology Enabled Social Responsibility Projects and an Empirical Test of CSR\u27s Impact on Firm Performance

Multinational firms publish annual corporate social responsibility (CSR) reports to signal to stakeholders they are ‘doing better by doing good.’ However, many firms have not effectively integrated technology with CSR to generate impactful long-term solutions. The era of mindful consumption is about creating hi-tech opportunities to satisfy consumers as well as limit resource use. In this research we examine how CSR is revolutionized by technology. We present research based on in-depth conversations with experts and illustrative case studies on how AI is disrupting the world of CSR. Specifically, we examine how the latest technologies in artificial intelligence (AI) and machine learning (ML) are changing perspectives on CSR for countries, industries, firms, and nongovernmental organizations (NGOs). We present an extended stakeholder framework to display the way technology is fundamentally changing how international business is conducted. This research also quantitatively examines the financial impact that CSR has on tangible returns for multinational enterprises (MNEs). Through the lens of institutional theory, we examine which industries CSR and sustainability yield the most beneficial returns over time

Taking Good Works to the Next Level: Increasing Investment in and Support for Higher-Risk Innovation

This thesis explores the possible avenues available to corporations and capital-managing entities seeking to increase their commitment to good works. These organizations have the potential to fill the gap in societal needs by supporting and investing in good works, including environmental protection and highly-innovative energy technologies, beyond the traditional corporate social responsibility (CSR) norm. These means include charitable giving, working with disadvantaged communities, corporate assistance to environmental or other social non-governmental organizations, and more. This thesis discusses the advantages and limitations of various corporate structures (C Corporations, S Corporations, LLCs, B Corps, L3Cs, and benefit corporations) and capital-managing organizations (mutual funds, foundations, and pension funds). Recommendations are provided for each to encourage good works with greater impact

Market basket analysis : trend analysis of association rules in different time periods

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMMarket basket analysis (i.e. Data mining technique in the field of marketing) is the method to find the associations between the items / item sets and based on those associations we can analyze the consumer behavior. In this research we have presented the variability of time, because with the change in time the habits or behavior of the customer also changes. For example, people wear warm clothes in winter and light clothes in summer. Similarly, customers purchase behavior also changes with the change in time. We study the problem of discovering association rules that display regular cyclic variation over time. This problem will allow us to access the changing trends in the purchase behavior of customers in a retail market, and we will be able to analyze the results which will display the changing trends of the association rules. In this research we will study the interaction between association rules and time. We worked on transactional data of a Belgian retail company and analyzed the results which will help the company to build up time period specific marketing strategies, promotional strategies, etc. to increase the profit of their company

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

Using Marketing Strategies to Advance Millennial Prospects at Credit Unions in Jamaica

Content of marketing strategies not only appeals to consumers based on their demographics and identity but consumers can also respond more positively to marketing strategies that target their purchasing behaviors. The purpose of this qualitative multiple case study was to explore how financial advisors at credit unions in Jamaica are using marketing strategies to advance millennial prospects, in an environment where consumers recognize commercial banks as the dominant institution in the financial industry. The conceptual framework for this study was the sustainability theory, with a direct focus on economic sustainability. The data collection process involved semistructured face-to-face-interviews with 5 financial advisors from credit unions in Jamaica to explore marketing strategies they used to increase the sale of financial services to millennials to improve their businesses\u27 performance. Analysis of the audio recordings and hand-written field notes included methodological triangulation and grouping information into themes that were prevalent in the data. The coding process yielded 5 major themes -marketing strategies and funding, financial literacy, the impact of information technology, product design, development and modification and measures of success. The study results provided by the financial advisors to millennials could show how marketing communication strategies can contribute to millennials\u27 financial literacy and enhance their financial stability and extend their economic sustainability