A service oriented architecture to provide data mining services for non-expert data miners

In today's competitive market, companies need to use discovery knowledge techniques to make better, more informed decisions. But these techniques are out of the reach of most users as the knowledge discovery process requires an incredible amount of expertise. Additionally, business intelligence vendors are moving their systems to the cloud in order to provide services which offer companies cost-savings, better performance and faster access to new applications. This work joins both facets. It describes a data mining service addressed to non-expert data miners which can be delivered as Software-as-a-Service. Its main advantage is that by simply indicating where the data file is, the service itself is able to perform all the process. © 2012 Elsevier B.V. All rights reserved

FIN-DM: finantsteenuste andmekaeve protsessi mudel

Andmekaeve hõlmab reeglite kogumit, protsesse ja algoritme, mis võimaldavad ettevõtetel iga päev kogutud andmetest rakendatavaid teadmisi ammutades suurendada tulusid, vähendada kulusid, optimeerida tooteid ja kliendisuhteid ning saavutada teisi eesmärke. Andmekaeves ja -analüütikas on vaja hästi määratletud metoodikat ja protsesse. Saadaval on mitu andmekaeve ja -analüütika standardset protsessimudelit. Kõige märkimisväärsem ja laialdaselt kasutusele võetud standardmudel on CRISP-DM. Tegu on tegevusalast sõltumatu protsessimudeliga, mida kohandatakse sageli sektorite erinõuetega. CRISP-DMi tegevusalast lähtuvaid kohandusi on pakutud mitmes valdkonnas, kaasa arvatud meditsiini-, haridus-, tööstus-, tarkvaraarendus- ja logistikavaldkonnas. Seni pole aga mudelit kohandatud finantsteenuste sektoris, millel on omad valdkonnapõhised erinõuded. Doktoritöös käsitletakse seda lünka finantsteenuste sektoripõhise andmekaeveprotsessi (FIN-DM) kavandamise, arendamise ja hindamise kaudu. Samuti uuritakse, kuidas kasutatakse andmekaeve standardprotsesse eri tegevussektorites ja finantsteenustes. Uurimise käigus tuvastati mitu tavapärase raamistiku kohandamise stsenaariumit. Lisaks ilmnes, et need meetodid ei keskendu piisavalt sellele, kuidas muuta andmekaevemudelid tarkvaratoodeteks, mida saab integreerida organisatsioonide IT-arhitektuuri ja äriprotsessi. Peamised finantsteenuste valdkonnas tuvastatud kohandamisstsenaariumid olid seotud andmekaeve tehnoloogiakesksete (skaleeritavus), ärikesksete (tegutsemisvõime) ja inimkesksete (diskrimineeriva mõju leevendus) aspektidega. Seejärel korraldati tegelikus finantsteenuste organisatsioonis juhtumiuuring, mis paljastas 18 tajutavat puudujääki CRISP- DMi protsessis. Uuringu andmete ja tulemuste abil esitatakse doktoritöös finantsvaldkonnale kohandatud CRISP-DM nimega FIN-DM ehk finantssektori andmekaeve protsess (Financial Industry Process for Data Mining). FIN-DM laiendab CRISP-DMi nii, et see toetab privaatsust säilitavat andmekaevet, ohjab tehisintellekti eetilisi ohte, täidab riskijuhtimisnõudeid ja hõlmab kvaliteedi tagamist kui osa andmekaeve elutsüklisData mining is a set of rules, processes, and algorithms that allow companies to increase revenues, reduce costs, optimize products and customer relationships, and achieve other business goals, by extracting actionable insights from the data they collect on a day-to-day basis. Data mining and analytics projects require well-defined methodology and processes. Several standard process models for conducting data mining and analytics projects are available. Among them, the most notable and widely adopted standard model is CRISP-DM. It is industry-agnostic and often is adapted to meet sector-specific requirements. Industry- specific adaptations of CRISP-DM have been proposed across several domains, including healthcare, education, industrial and software engineering, logistics, etc. However, until now, there is no existing adaptation of CRISP-DM for the financial services industry, which has its own set of domain-specific requirements. This PhD Thesis addresses this gap by designing, developing, and evaluating a sector-specific data mining process for financial services (FIN-DM). The PhD thesis investigates how standard data mining processes are used across various industry sectors and in financial services. The examination identified number of adaptations scenarios of traditional frameworks. It also suggested that these approaches do not pay sufficient attention to turning data mining models into software products integrated into the organizations' IT architectures and business processes. In the financial services domain, the main discovered adaptation scenarios concerned technology-centric aspects (scalability), business-centric aspects (actionability), and human-centric aspects (mitigating discriminatory effects) of data mining. Next, an examination by means of a case study in the actual financial services organization revealed 18 perceived gaps in the CRISP-DM process. Using the data and results from these studies, the PhD thesis outlines an adaptation of CRISP-DM for the financial sector, named the Financial Industry Process for Data Mining (FIN-DM). FIN-DM extends CRISP-DM to support privacy-compliant data mining, to tackle AI ethics risks, to fulfill risk management requirements, and to embed quality assurance as part of the data mining life-cyclehttps://www.ester.ee/record=b547227

The research on customer structure characteristics and marketing measures of regional bank agency: a case from the Agricultural Bank of China

With intensified opening degree and increasingly fierce market competition of commercial banks, commercial banks innovate their products constantly and improve their service quality at the same time. The Agricultural Bank of China (ABC) is a state-owned commercial bank that has built branches in all county-level districts. Instead, branches of ABC in county-level have become the weakest links that reduce ABC’s competitive power. If the flaws in customer and market maintenance in the county-level branches are ever to be repaired, in my opinion, meeting customer perceived service quality and customer demands efficiently based on understanding of customer needs should be put in the first priority currently. Firstly, this part studies the customer segmentation of ** branch of Agricultural Bank of China. This thesis puts forward approach to segment bank customers based on the improved k-means clustering. The results show that the improvement algorithm effectively overcomes the defect that traditional k-means algorithm easily falls into local optimal value, increasing the accuracy of customer classification, and contributing to more reasonable clustering results. Secondly, this thesis uses the econometric panel data model to study the relationship between customer structure and bank performance. The results indicate that a good customer structure can bring benefits for banks and improve their competitiveness. Thirdly, this part analyzes different service quality requirements of different types customer in the ** branch of Agricultural Bank of China. We combine service quality evaluation theory and the background of Chinese commercial banks, establishing the SERVQUAL model for the ** branch. The Study has shown that the correlation coefficient between overall perception of service quality and customer satisfaction is positive; the overall perception of service quality and customer willingness to recommend are also positively correlated, but the degree of correlation is lower than the correlation between the overall perception of service quality and customer satisfaction; the correlation of overall perceived quality of service for all samples and willingness to accept the services of other banks correlation was not significant. At the same time, there is still a gap between the customer perceived service quality and customer expectation in the ** branch of Agricultural Bank of China. Finally, according to the results of customer structure classification and service quality survey of the ** branch of Agricultural Bank of China, the marketing strategies for different customer groups are proposed.Com o crescente grau de comercialização da indústria bancária chinesa e a entrada continua de bancos estrangeiros, a competição entre bancos está a tornar-se cada vez mais feroz, e as estratégias dos bancos comerciais com vista a ganhar vantagens competitivas muda gradualmente. Para além do lançamento de uma variedade de produtos financeiros, os bancos comerciais utilizam serviços diferenciados para poder dar resposta á procura do mercado diversificado de consumidores. Estes bancos estão igualmente a começar a entender que para os bancos gradualmente convergirem devem não só atingir uma vantagem competitiva através da oferta de produtos financeiros bem como serviços diferenciados de alta qualidade. Este meio tornou-se na única forma forma que o banco dispõe para poder vencer a sua competição. Portanto, para os bancos comerciais, estamos num período de inovação onde o aumento da qualidade de serviço é inevitável. O Agricultural Bank of China é um banco comercial do estado que possui uma filial em todas as regiões administrativas a nível de condado. Ligações e serviços, citadinos e urbanos tem sido a maior vantagem do Agricultural Bank of China, mas a situação actual não é favorável. A filial a nível de condado tem-se tornado na ligação pior e mais fraca da fundação deste banco. Ao mesmo tempo, bancos privados têm emergido em paridade com o rápido desenvolvimento dos instrumentos financeiros online e, o Agricultural Bank of China, como o representante dos bancos tradicionais está a enfrentar competição feroz. Em especial desvantagem no que toca a recursos ao consumidor e instrumentos online que os outros bancos oferecem. Os bancos comerciais tradicionais, estão desta forma confrontados com a perda de clientes bem como o elevado custo de adquirir novos clientes. O risco operacional do banco aumenta á medida que a estabilidade do mercado consumidor piora. Se querem mudar o status quo das filiais a nível de condado, necessitam entender a actual necessidade da qualidade de serviço ao cliente, analisar as características da procura do consumidor e estabelecer um mecanismo de ciclo virtuoso de mercado-consumidor-beneficio - são as maiores prioridades agora. Baseado nisto, este estudo usará marketing, processo de decisão da gerência, teoria e métodos, mineração de dados, técnicas estatísticas e métodos econométricos para analisar as características de procura do consumidor do Agricultural Bank of China. Primeiro, utilizar a análise de cluster de mineração de dados para efectuar uma estratificação analítica do grupo de consumidores do banco para manter a estrutura da procura dos consumidores e serviços; classificação da informação de procura dos consumidores, acesso ás tendências de procura dos consumidores do banco e tendência de produtos competitivos; na base de quantificar os requerimentos do consumidor, usamos o painel de dados econométricos para efectuar uma análise empírica sobre a estrutura de procura dos consumidores e a performance do Agricultural Bank of China

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics

Six Human-Centered Artificial Intelligence Grand Challenges

Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies

Інформатика, математика, автоматика

Матеріали та програма науково-технічної конференції, м. Суми–Нур-Султан, 19–23 квітня 2021 р

Design of an information system that connects retail banks and customers

The Swedish company Skyforge Financial Solutions AB (www.skyforge.se) is working on the new product MyBudget that will be part of an internet banking solution. The current version of the software extracts the spending information from the bank's transactions and then organize it graphically. My work is aimed to design a new concept the next major vision of MyBudget. It will be analyzed the stream of information between banks and customers, it will be evaluated if it is possible to have a deeper analysis of the available information about the customers so that the banks can improve their business, and then there will be some comments about the implication of the usage of this prototype by the customers in their way of spending money. The policy of research agreed with the company is to nd a "win-win" solution between banks and customers. Both of them should receive more information and then more advantages. MyBudget will have the technology that will make this possible. Sweden and nordic countries will be the rst reference market. The best practices of money management will be borrowed from companies processes to design solutions to improve the banks' customers spending behavior. This thesis is based on the work done in my whole university career but, mostly in the following courses taken in Lund University: - Strategic Management and Information Systems - Decision Support Systems - Design of Business Rules Systems - Business Intelligence and Securit

AI in marketing, consumer research and psychology: A systematic literature review and research agenda

This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross-fertilization of theories used across fields, and neglected research topics