The aim of this article is to present a case study of usage of one of the data mining methods, neural network, in knowledge discovery from databases in the banking industry. Data mining is automated process of analysing, organization or grouping a large set of data from different perspectives and summarizing it into useful information using special algorithms. Data mining can help to resolve banking problems by finding some regularity, causality and correlation to business information which are not visible at first sight because they are hidden in large amounts of data. In this paper, we used one of the data mining methods, neural network, within the software package Alyuda NeuroInteligence to predict customer churn in bank. The focus on customer churn is to determinate the customers who are at risk of leaving and analysing whether those customers are worth retaining. Neural network is statistical learning model inspired by biological neural and it is used to estimate or approximate functions that can depend on a large number of inputs which are generally unknown. Although the method itself is complicated, there are tools that enable the use of neural networks without much prior knowledge of how they operate. The results show that clients who use more bank services (products) are more loyal, so bank should focus on those clients who use less than three products, and offer them products according to their needs. Similar results are obtained for different network topologies.

We live in a world where we collect huge amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. With new methods and techniques, we can use this data, analyze it and get a great advantage. The perfect method for this is data mining. Data mining is the process of extracting hidden and useful information and patterns from large data sets. Its application in various areas such as finance, telecommunications, healthcare, sales marketing, banking, etc. is already well known. In this paper, we want to introduce special use of data mining in education, called educational data mining. Educational Data Mining (EDM) is an interdisciplinary research area created as the application of data mining in the educational field. It uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. Educational Data Mining is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.The goal of this paper is to introduce educational data mining and to present its application and benefits.

SAŽETAK:Zbog integriranosti i globalne povezanosti Þ nancijskih subjekata i tržišta, monetarna politika više nije dostatna za o uvanje Þ nancijske stabilnosti (posebice kod malih otvorenih zemalja poput Hrvatske) te je za postizanje i o uvanje Þ nancijske stabilnosti nužno uvesti makroprudencijalne mjere i instrumente. Obzirom da monetarna i makroprudencijalna politika u ostvarivanju svojih ciljeva djeluju na iste i/ili povezane varijable, nužno je koordinirati politike kako bi se smanjila odstupanja od ciljeva kod obje politike. U ovome radu je metodom nelinearnog kvadratnog programiranja dokazano da se kooperativnim modelom u kojemu monetarna i makroprudencijalna politika sura uju, cjenovna i Þ nancijska stabilnost ostvaruju uz niža odstupanja od zadanih razina nego u slu aju ne-kooperativnog modela gdje svaka politika izolirano ostvaruje svoj cilj. Suradnja je ostvarena optimalnom primjenom kamatne stope (kao glavnoga instrumenta monetarne politike) i kapitalnih zahtjeva (kao glavnoga instrumenta makroprudencijalne politike) uz utjecaj deviznoga te aja.KLJU NE RIJE I: Þ nancijska stabilnost, monetarna politika, makroprudencijalna politika.ABSTRACT: Due to the integration and global interconnection of Þ nancial entities and markets, monetary policy is no longer sufÞ cient for maintainig Þ nancial stability (especially in small open countries like Croatia). Hence, in order to achieve and maintain Þ nancial stability, it is necessary to introduce macroprudential measures and instruments. Since for achieving their objectives both monetary and macroprudential policies affect the same

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Statistics is an old scientific discipline, but its application has never been more topical. Statistics is a branch of mathematics that deals with the collection, analysis, interpretation and presentation of data. Increased computing power had a huge impact on the popularisation of the practice of statistical science. With new technologies such as the internet of things, we start to collect data from various sources like web server logs, online transaction records, tweet streams, social media, data from all kinds of sensors. With increased access to big data, there is a need for professionals with applied statistics knowledge who can visualize and analyze data, make sense of it, and use it to solve real complex problems. Applied statistics is the root of data analysis, and the practice of applied statistics involves analyzing data to help define and determine organizational needs. Today we can find applied statistics in various fields such as medicine, information technology, engineering, finance, marketing, accounting, business, etc. The goal of this paper is to clarify the applied statistics, its principles and to present its application in various fields.