Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Semantic recommender systems Provision of personalised information about tourist activities.

Aquesta tesi estudia com millorar els sistemes de recomanació utilitzant informació semàntica sobre un determinat domini (en el cas d’aquest treball, Turisme). Les ontologies defineixen un conjunt de conceptes relacionats amb un determinat domini, així com les relacions entre ells. Aquestes estructures de coneixement poden ser utilitzades no només per representar d'una manera més precisa i refinada els objectes del domini i les preferències dels usuaris, sinó també per millorar els procediments de comparació entre els objectes i usuaris (i també entre els mateixos usuaris) amb l'ajuda de mesures de similitud semàntica. Les millores al nivell de la representació del coneixement i al nivell de raonament condueixen a recomanacions més precises i a una millora del rendiment dels sistemes de recomanació, generant nous sistemes de recomanació semàntics intel•ligents. Les dues tècniques bàsiques de recomanació, basades en contingut i en filtratge col•laboratiu, es beneficien de la introducció de coneixement explícit del domini. En aquesta tesi també hem dissenyat i desenvolupat un sistema de recomanació que aplica els mètodes que hem proposat. Aquest recomanador està dissenyat per proporcionar recomanacions personalitzades sobre activitats turístiques a la regió de Tarragona. Les activitats estan degudament classificades i etiquetades d'acord amb una ontologia específica, que guia el procés de raonament. El recomanador té en compte molts tipus diferents de dades: informació demogràfica, les motivacions de viatge, les accions de l'usuari en el sistema, les qualificacions proporcionades per l'usuari, les opinions dels usuaris amb característiques demogràfiques similars o gustos similars, etc. Un procés de diversificació que calcula similituds entre objectes s'aplica per augmentar la varietat de les recomanacions i per tant augmentar la satisfacció de l'usuari. Aquest sistema pot tenir un impacte positiu a la regió en millorar l'experiència dels seus visitants.Esta tesis estudia cómo mejorar los sistemas de recomendación utilizando información semántica sobre un determinado dominio, en el caso de este trabajo el Turismo. Las ontologías definen un conjunto de conceptos relacionados con un determinado dominio, así como las relaciones entre ellos. East estructuras de conocimiento pueden ser utilizadas no sólo para representar de una manera más precisa y refinada los objetos del dominio y las preferencias de los usuarios, sino también para aplicar mejor los procedimientos de comparación entre los objetos y usuarios (y también entre los propios usuarios) con la ayuda de medidas de similitud semántica. Las mejoras al nivel de la representación del conocimiento y al nivel de razonamiento conducen a recomendaciones más precisas y a una mejora del rendimiento de los sistemas de recomendación, generando nuevos sistemas de recomendación semánticos inteligentes. Las dos técnicas de recomendación básicas, basadas en contenido y en filtrado colaborativo, se benefician de la introducción de conocimiento explícito del dominio. En esta tesis también hemos diseñado y desarrollado un sistema de recomendación que aplica los métodos que hemos propuesto. Este recomendador está diseñado para proporcionar recomendaciones personalizadas sobre las actividades turísticas en la región de Tarragona. Las actividades están debidamente clasificadas y etiquetadas de acuerdo con una ontología específica, que guía el proceso de razonamiento. El recomendador tiene en cuenta diferentes tipos de datos: información demográfica, las motivaciones de viaje, las acciones del usuario en el sistema, las calificaciones proporcionadas por el usuario, las opiniones de los usuarios con características demográficas similares o gustos similares, etc. Un proceso de diversificación que calcula similitudes entre objetos se aplica para generar variedad en las recomendaciones y por tanto aumentar la satisfacción del usuario. Este sistema puede tener un impacto positivo en la región al mejorar la experiencia de sus visitantes.This dissertation studies how new improvements can be made on recommender systems by using ontological information about a certain domain (in the case of this work, Tourism). Ontologies define a set of concepts related to a certain domain as well as the relationships among them. These knowledge structures may be used not only to represent in a more precise and refined way the domain objects and the user preferences, but also to apply better matching procedures between objects and users (or between users themselves) with the help of semantic similarity measures. The improvements at the knowledge representation level and at the reasoning level lead to more accurate recommendations and to an improvement of the performance of recommender systems, paving the way towards a new generation of smart semantic recommender systems. Both content-based recommendation techniques and collaborative filtering ones certainly benefit from the introduction of explicit domain knowledge. In this thesis we have also designed and developed a recommender system that applies the methods we have proposed. This recommender is designed to provide personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labelled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. A diversification process that computes similarities between objects is applied to produce diverse recommendations and hence increase user satisfaction. This system can have a beneficial impact on the region by improving the experience of its visitors

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

ASA 2021 Statistics and Information Systems for Policy Evaluation

This book includes 25 peer-reviewed short papers submitted to the Scientific Opening Conference titled “Statistics and Information Systems for Policy Evaluation”, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Department of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring

Public Health

Public health can be thought of as a series of complex systems. Many things that individual living in high income countries take for granted like the control of infectious disease, clean, potable water, low infant mortality rates require a high functioning systems comprised of numerous actors, locations and interactions to work. Many people only notice public health when that system fails. This book explores several systems in public health including aspects of the food system, health care system and emerging issues including waste minimization in nanosilver. Several chapters address global health concerns including non-communicable disease prevention, poverty and health-longevity medicine. The book also presents several novel methodologies for better modeling and assessment of essential public health issues

Challenges and prospects of spatial machine learning

The main objective of this thesis is to improve the usefulness of spatial machine learning for the spatial sciences and to allow its unused potential to be exploited. To achieve this objective, this thesis addresses several important but distinct challenges which spatial machine learning is facing. These are the modeling of spatial autocorrelation and spatial heterogeneity, the selection of an appropriate model for a given spatial problem, and the understanding of complex spatial machine learning models.Das wesentliche Ziel dieser Arbeit ist es, die Nützlichkeit des räumlichen maschinellen Lernens für die Raumwissenschaften zu verbessern und es zu ermöglichen, ungenutztes Potenzial auszuschöpfen. Um dieses Ziel zu erreichen, befasst sich diese Arbeit mit mehreren wichtigen Herausforderungen, denen das räumliche maschinelle Lernen gegenübersteht. Diese sind die Modellierung von räumlicher Autokorrelation und räumlicher Heterogenität, die Auswahl eines geeigneten Modells für ein gegebenes räumliches Problem und das Verständnis komplexer räumlicher maschineller Lernmodelle