235 research outputs found
Personalized Finance Advisory through Case-based Recommender Systems and Diversification Strategies
Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield
Case-based recommender systems for personalized finance advisory
Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the tasks, which largely require a deep knowledge of the financial domain, a trend in the area is the exploitation of recommendation technologies to support financial advisors and to improve the effectiveness of the process. The talk presents a framework to support financial advisors in the task of providing clients with personalized investment strategies. The methodology is based on the exploitation of case-based reasoning and the introduction of a diversification technique. A prototype of the framework has been used to generate personalized portfolios, and its performance, evaluated against 1,172 real users, shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings
A smart financial advisory system exploiting Case-Based Reasoning
In the financial advisory context, knowledge-based recommendations based on Case-Based Reasoning are an emerging trend. They usually exploit knowledge about past experiences and about the characterization of both customers and financial products. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SmartFasi. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile "similar" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SmartFasi. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users
ΠΠΎΠ²Π°Ρ Π²Π·Π²Π΅ΡΠ΅Π½Π½Π°Ρ Π³ΠΈΠ±ΡΠΈΠ΄Π½Π°Ρ ΡΠΈΡΡΠ΅ΠΌΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° Π¨Π°ΡΠΏΠ° Π΄Π»Ρ ΠΏΡΠΈΠ±ΡΠ»ΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΠ²Π΅ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΡΡΠ΅Π»Ρ
Identifying where to invest and how much to invest can be very challenging for common people who have limited knowledge in the domain. Portfolio managers are financial professionals who spend a lot of time and effort to help investors in investing funds and implementing investment strategies, but not all can afford to consult them. The study aims to develop a weighted hybrid recommendation system that recommends an optimized investment portfolio based on the investorβs preferences regarding risk and return. Generally, investors usually ask investment for advice from friends or relatives with similar risk preferences or if they are interested in a particular item, the investors ask for the experience of someone who already has invested in the same item. Therefore, the methodology considers the investorβs past behavior and the past behavior of the nearest neighbor investors with similar risk preferences. Using user-based collaborative filtering the number of stocks is recommended using Pearson correlation based on the investorβs income, then using another user-based collaborative filtering the number of stocks is recommended based on the investorβs age. Weights are assigned to the recommended number of stocks generated based on income and age and their weighted average is finally considered. Finally, the feasibility of the proposed system was assessed through various experiments. Based on the received results, the authors conclude that the proposed weighted hybrid approach is robust enough for implementation in the real world. The novelty of the paper lies in the fact that none of the existing approaches make use of more than one type of weighted recommendation algorithm. Additionally, the final results obtained this way have been never further fortified with the highest Sharpe ratio and minimum risk for the investor. This combination of hybrid and Sharpe ratios has never been explored before.ΠΡΠ±ΠΎΡ ΡΠΎΠ³ΠΎ, ΠΊΡΠ΄Π° ΠΈ ΡΠΊΠΎΠ»ΡΠΊΠΎ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΎΠ²Π°ΡΡ, ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΎΡΠ΅Π½Ρ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ Π΄Π»Ρ ΠΎΠ±ΡΡΠ½ΡΡ
Π»ΡΠ΄Π΅ΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΈΠΌΠ΅ΡΡ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΡΠ΅ Π·Π½Π°Π½ΠΈΡ Π² ΡΡΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ. ΠΠΎΡΡΡΠΎΠ»ΠΈΠΎ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΡΡ β ΡΡΠΎ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»Ρ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΡΠ°ΡΡΡ ΠΌΠ½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΈ ΡΡΠΈΠ»ΠΈΠΉ, ΡΡΠΎΠ±Ρ ΠΏΠΎΠΌΠΎΡΡ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°ΠΌ Π² ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΡΠ΅Π΄ΡΡΠ² ΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ, Π½ΠΎ Π½Π΅ Π²ΡΠ΅ ΠΌΠΎΠ³ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡΡ ΡΠ΅Π±Π΅ ΠΎΠ±ΡΠ°ΡΠΈΡΡΡΡ ΠΊ Π½ΠΈΠΌ Π·Π° ΠΊΠΎΠ½ΡΡΠ»ΡΡΠ°ΡΠΈΠ΅ΠΉ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ Π²Π·Π²Π΅ΡΠ΅Π½Π½ΡΡ ΡΠΈΡΡΠ΅ΠΌΡ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΡΡΠ΅Π»Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ΅Π½ΠΈΠΉ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ° ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠΈΡΠΊΠ° ΠΈ Π΄ΠΎΡ
ΠΎΠ΄Π½ΠΎΡΡΠΈ. ΠΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, ΠΈΠ½Π²Π΅ΡΡΠΎΡΡ ΡΠΏΡΠ°ΡΠΈΠ²Π°ΡΡ ΡΠΎΠ²Π΅ΡΠ° ΠΏΠΎ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΡΠΌ Ρ Π΄ΡΡΠ·Π΅ΠΉ ΠΈΠ»ΠΈ ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΠΈΠΊΠΎΠ² ΡΠΎ ΡΡ
ΠΎΠΆΠΈΠΌΠΈ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ΅Π½ΠΈΡΠΌΠΈ Π² ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΡΠΈΡΠΊΠ°, ΠΈΠ»ΠΈ, Π΅ΡΠ»ΠΈ ΠΈΡ
ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΠ΅Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΠΉ ΡΠΎΠ²Π°Ρ, Ρ ΡΠΎΠ³ΠΎ, ΠΊΡΠΎ ΡΠΆΠ΅ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΎΠ²Π°Π» Π² ΡΠΎΡ ΠΆΠ΅ ΡΠΎΠ²Π°Ρ. ΠΠΎΡΡΠΎΠΌΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ ΡΡΠΈΡΡΠ²Π°Π΅Ρ ΠΏΡΠΎΡΠ»ΠΎΠ΅ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ° ΠΈ Π΅Π³ΠΎ Π±Π»ΠΈΠΆΠ°ΠΉΡΠΈΡ
ΡΠΎΡΠ΅Π΄Π΅ΠΉ-ΠΈΠ½Π²Π΅ΡΡΠΎΡΠΎΠ² ΡΠΎ ΡΡ
ΠΎΠΆΠΈΠΌΠΈ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΡΠΈΡΠΊΠ°. Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠΎΠ»Π»Π°Π±ΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ² ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ Π°Π²ΡΠΎΡΡ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΡΡ Π²ΡΠ±ΡΠ°ΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π°ΠΊΡΠΈΠΉ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΠΈΡΡΠΎΠ½Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄ΠΎΡ
ΠΎΠ΄Π° ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°. ΠΠ°ΡΠ΅ΠΌ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π΄ΡΡΠ³ΠΎΠΉ ΠΊΠΎΠ»Π»Π°Π±ΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ² ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΠ΅ΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π°ΠΊΡΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°. Π Π΅ΠΊΠΎΠΌΠ΅Π½Π΄ΠΎΠ²Π°Π½Π½ΠΎΠΌΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Ρ Π°ΠΊΡΠΈΠΉ, ΡΠ³Π΅Π½Π΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΌΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄ΠΎΡ
ΠΎΠ΄Π° ΠΈ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°, ΠΏΡΠΈΡΠ²Π°ΠΈΠ²Π°ΡΡΡΡ Π²Π΅ΡΠ°, ΠΈ Π² ΠΈΡΠΎΠ³Π΅ ΡΡΠΈΡΠ°Π΅ΡΡΡ ΠΈΡ
ΡΡΠ΅Π΄Π½Π΅Π²Π·Π²Π΅ΡΠ΅Π½Π½ΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅. Π Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΎΡΠ΅Π½ΠΊΠ° ΡΠ΅Π°Π»ΠΈΠ·ΡΠ΅ΠΌΠΎΡΡΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ². ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π°Π²ΡΠΎΡΡ Π΄Π΅Π»Π°ΡΡ Π²ΡΠ²ΠΎΠ΄, ΡΡΠΎ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ Π²Π·Π²Π΅ΡΠ΅Π½Π½ΡΠΉ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ Π½Π°Π΄Π΅ΠΆΠ΅Π½ Π΄Π»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
. ΠΠΎΠ²ΠΈΠ·Π½Π° ΡΠ°Π±ΠΎΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΡΠΎΠΌ, ΡΡΠΎ Π½ΠΈ ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Π½Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅Ρ Π±ΠΎΠ»Π΅Π΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π²Π·Π²Π΅ΡΠ΅Π½Π½ΡΡ
ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΠΊΠΎΠ½Π΅ΡΠ½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΡΠ°ΠΊΠΆΠ΅ Π½ΠΈΠΊΠΎΠ³Π΄Π° Π½Π΅ ΠΎΡΠ»ΠΈΡΠ°Π»ΠΈΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΌ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠΌ Π¨Π°ΡΠΏΠ° ΠΈ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΠΌ ΡΠΈΡΠΊΠΎΠΌ Π΄Π»Ρ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°. Π’Π°ΠΊΠ°Ρ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΡ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΠΎΠΉ ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ ΠΈ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° Π¨Π°ΡΠΏΠ° Π½ΠΈΠΊΠΎΠ³Π΄Π° ΡΠ°Π½Π΅Π΅ Π½Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»Π°ΡΡ
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 managersβ advice on which financial product is most suitable for each of the bankβs 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 bankβs commercial efforts around customersβ
future requirements. By allowing for a better understanding of customersβ 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
Nudged to Win: Designing Robo-Advisory to Overcome Decision Inertia
Decision inertia is a serious problem in financial decision-making and thus a challenge for decision support systems. We discuss recent findings and review antecedents and consequences of decision inertia from a psychological perspective. We use these insights to develop IT-based methods designed to overcome decision inertia using psychologically optimized financial decision support systems. Furthermore, we propose an experimental study to evaluate the design features of such a system. Our work is a first step in designing adaptive decision support systems that detect situations in which the user is prone to decision inertia and react by adapting interface elements appropriately that might otherwise exacerbate decision inertia β for a specific user in a specific decision situation
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
Big Data and Artificial Intelligence in Digital Finance
This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance
Big Data and Artificial Intelligence in Digital Finance
This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance
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