mogućnosti primene dejta majning procesa u bankarstvu

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MOGUĆNOSTI PRIMENE DEJTA MAJNING PROCESA U BANKARSTVU

Rezime

Konkurentska prednost i implementacĳa poslovne inteligencĳe predstavljaju najvažnĳa merila uspešnosti u savremenom poslovanju. Jedan od procesa koji firme primenjuju radi povećanja prodaje je i proces dejta majninga. Cilj ovog rada je da opiše proces dejta majninga, definiše njegove osnovne korake i faze, i demonstrira tehnike ovog procesa koje mogu da se koriste u bankarstvu sa svrhom da se poveća kvalitet u procesu odlučivanja i planiranja. Dejta majning se bitno razlikuje od sličnih postupaka kao što su OLAP i primena statističkih modela, jer daje mogućnost interaktivnog učenja i predviđanja. Osnovni materĳal koji se u ovom procesu koristi su velike baze podataka, kojima svaka banka raspolaže. Analizom mogućnosti dejta majninga u bankarskom sektoru utvrđeno je da on može biti korišćen u traganju za vrednim informacĳama u velikim bazama podataka radi poboljšanja poslovanja banke. Ključni činioci za uspešnu primenu ovog procesa su detaljno planiranje i analiza. Dejta majnig proces u bankarstvu može se koristiti za potrebe marketinga, analize i unapređenje prodaje i predviđanje rizika.

Ključne reči: dejta majning, bankarstvo, marketing, unapređenje prodaje, upravljanje rizikom, poslovno odlučivanje

JEL Classification Codes: M31, L190, G21

Mr Saša Raletić[email protected]

Mr Predrag Radojević[email protected]

stručni priloziUDK 005:336.7

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POSSIBILITIES OF DATA MINING

PROCESS IMPLEMENTATION

IN BANKING

Summary

Competitive advantage and implementation of business intelligence are the most important measurement of success in modern business. Data mining is one of process which enterprises use in order to increase sale. Goal of this the paper is to describe data mining process, define its main steps and phases, and demonstrate data mining techniques that could be in use in banking for purpose of increasing quality of decision making and planning. Data mining is essentially different from similar processes, OLAP and statistical models, because it give an opportunity of interactive learning and prediction. Basic materials for data mining process are large data bases which every bank obtains. By analysis of possibility of data mining in banking sector it was found that this process can be helpful in searching for useful information in large data bases for improving business performance of the bank. Key factors for successful implementations of this process are detail planning and analyze. Data mining process in banking can be used for marketing, sale analysis and improving and risk predictions.

Key words: data mining, banking, marketing, sales improvement, risk management, decision-making in business

JEL Classification Codes: M31, L190, G21

Predrag Radojević [email protected]

Saša Raletić [email protected]

expert contributionsUDC 005:336.7

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Proces dejta majning (data mining) predstavlja široko rasprostranjenu analizu koju sprovode firme u razvĳenim

ekonomĳama kako bi pokušale da unaprede sopstveno poslovanje. Proces nalazi primenu nezavisno od vrste posla koji neka firma obavlja, pa se stoga može reći da je njegova primena gotovo univerzalna. Jedini preduslov je da firma raspolaže bazama podataka o svojim klĳentima i da su u tim bazama zabeležene poslovne aktivnosti klĳenata.

Trenutno domaće poslovne banke imaju uglavnom stabilne tržišne udele. Mogućnosti za razvoj novih bankarskih proizvoda i usluga sužene su usled prezasićenosti tržišta, pada kupovne moći i rapidnog rasta poslovanja tokom prethodnih nekoliko godina. U takvoj situacĳi donošenje poslovne odluke samo na osnovu sprovedenog marketing istraživanja bilo bi rizično. Dejta majning (data mining) proces u tom slučaju značajno smanjuje rizik. Ovaj proces zasnovan je na detaljnoj obradi podataka, a svaka poslovna banka već raspolaže gomilama podataka o svojim klĳentima.

Prilikom prvog kontakta sa klĳentom, bilo da je reč o otvaranju računa, podnošenju zahteva za kredit ili platnu karticu, banka od klĳenta dobĳe solidnu količinu ličnih, demografskih i socioekonomskih podataka. Tokom poslovanja sa bankom svaki klĳent dodatno uvećava bazu „svojih” podataka tako što se u nju „beleže” sve transakcĳe koje je klĳent napravio. Te vrste podataka ostaju zabeležene u transakcionim bazama banaka. Svaki od tih podataka posmatran sam za sebe ne predstavlja nikakvu novu informacĳu, niti neku novu vrednost, i poslovne banke koje rade u Srbĳi najčešće nedovoljno koriste podatke kojima raspolažu kako bi putem selekcĳe, analize, indukcĳe i generalizacĳe iz njih stvorile informacĳe. Ekonomskom terminologĳom rečeno, svi ti zabeleženi podaci predstavljaju samo sirovine, odnosno repromaterĳal ili u najboljoj varĳanti poluproizvode, dok se na osnovu njih ne pokušaju dobiti informacĳe, odnosno gotov proizvod. A upravo raspolaganje informacĳama važno je za proces poslovnog odlučivanja.

Definicĳa dejta majninga

Dejta majning predstavlja proces kojim

se u gomili podataka zabeleženim u bazama firme traže informacĳe koje mogu biti korisne za unapređenje poslovanja. Naime, u velikim količinama podataka traže se skrivene informacĳe koje “zlata vrede” (J. Han, M. Kamber, 2006. str. 5)

Neki autori skloni da većinu savremenih poslovnih procesa i aktivnosti posmatraju sa aspekta menadžmenta dejta majning svrstavaju u područje poslovne inteligencĳe. Drugi, s obzirom na njegovu bliskost marketnig istraživanju i mogućnosti korišćenja rezultata dobĳenih ovim procesom u marketinške svrhe smatraju da je pogodno svrstati ga upravo u ovu oblast. Treći je smatraju nezavisnom metodom vezanom pre svega za IT sektor i mogućnost razvoja matematičkih modela za analizu računarskih baza.

Bez obzira na različita mišljenja svi oni se slažu po pitanju korisnosti dejta majninga. Osnovna korist koju primena ovog procesa u bankarstvu donosi je mogućnost povećanja profita i tržišnog udela u uslovima pojačane konkurencĳe na tržištu i to upotrebom dejta majninga za procenjivanje rizika, marketing i unapređenje poslovanja sa klĳentima. Snaga primene dejta majninga proizlazi i iz činjenice da je ovaj proces nezavisan od područja primene, jer se naglasak stavlja na podatke, a ne na područje u kome se proces sprovodi.

Zadatak dejta majninga je otkrivanje faktora i njihovih karakteristika s obzirom na postavljeni cilj, odnosno rešenje određenog poslovnog problema. Najčešće su u ovaj proces, pored eksperta iz područja za čĳe potrebe se dejta majning sprovodi, uključeni i analitičari, i stručnjaci iz oblasti informacionih tehnologĳa, jer samo timskim angažovanjem i intersektorskom saradnjom postoji mogućnost da se na optimalan način otkrĳu, tumače i interpretiraju otkrivene zakonitosti (G. J. Mya�, 2007. str. 10)

Razlike između OLAP analize, statistike i dejta majninga

Proces dejta majninga se razlikuje kako od OLAP-a (Online Analytical Processing), tako i od klasičnih statističkih metoda (C. Vercellis, 2009. str. 81). Osnovna razlika prikazana je u Tabeli l. Razlika počiva na aktivnoj orĳentacĳi

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Data mining process is a widespread analysis conducted by companies in the developed economies, in order

to try to enhance their own business. The process is implemented regardless of the type of business a company conducts, hence it may be said that its application is almost universal. The only precondition is for a company to have available the databases on its clients and that these databases record the clients’ business activities.

At the moment, domestic commercial banks mostly have stable market shares. The possibilities for development of new banking products and services are narrowed due to the market saturation, slump in purchase power, along with the rapid growth of business in the last several years. In such circumstances, it would be risky to pass business decisions based only on the conducted market research. In this respect, data mining process considerably reduces the risk. This process is based on detailed data processing, and each commercial bank already has loads of data on its clients at its disposal.

During its first contact with the client, whether in relation to account opening, loan application or payment card request, the bank obtains a relatively large amount of personal, demographic and socio-economic data from its client. Throughout their business transactions with the bank, the clients additionally enlarge the database of ‘their’ data since all transactions conducted by the concerned clients get ‘recorded’. These types of data remain recorded in the banks’ transaction databases. Each piece of such data on its own presents neither a new piece of information, nor adds a new value, and the commercial banks operating in Serbia usually do not use the available data sufficiently enough, in order to generate information by means of selection, analysis, induction and generalization. In economic terms, all these recorded pieces of data are just raw materials, i.e. intermediate goods, or, at best, semi-finished products, until the banks try to use them to obtain information, i.e. finished products. And having information at your disposal is exactly what ma�ers in the business decision-making process.

Data mining definition

Data mining is a process which seeks information potentially useful for business enhancement in a heap of data recorded in the companies’ databases. Namely, what is sought in the large amounts of data are the hidden information ‘worth their weight in gold’ (J. Han, M. Kamber, 2006, p.5).

Some authors, prone to viewing most modern business processes and activities from the management perspective, classify data mining in business intelligence area. Others, however, given its proximity to marketing research and the possibility of using the results obtained in this process for marketing purposes, believe that it is appropriate to classify it in the field of marketing. Some other authors, still, consider it to be an independent method related, first and foremost, to IT sector and the possibility of developing mathematical models for the purpose of computer database analysis.

Regardless of the differing opinions, all authors agree on the usefulness of data mining. The major benefit of this process being implemented in banking is the possibility to increase profit and market share in the circumstances of harsh market competition, in particular, by means of using data mining for risk assessment, marketing and enhancement of client relations. The strength of data mining also comes from the fact that this process is independent from its field of implementation, since the focus is on data, not on the field in which the process is conducted.

The objective of data mining is to detect the factors and their characteristics regarding the set goal, i.e. to find the solution to a certain business challenge. In addition to experts in the field for whose purposes data mining is being conducted, this process usually involves the analysts and IT experts, because team work and inter-sector cooperation is the only way to detect, examine and interpret the observed regularities in the most optimum way (G. J. Mya�, 2007, p.10).

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zasnovanoj na induktivnim modelima učenja koja je zastupljena u dejta majningu. Za razliku od dejta majninga OLAP i statistički modeli su pasivne prirode, i ne baziraju se na interaktivnosti.

Naime, statističke analize počivaju na formulisanju hipoteza koje se potvrđuju ili odbacuju na bazi podataka iz uzorka. OLAP predstavlja konceptualni i intuitivni model zasnovan na multidimenzĳalnoj analizi podataka i podrazumeva gledanje podataka kroz veći broj filtara, odnosno dimenzĳa. Za razliku od statističkih modela i OLAP-a, dejta majning omogućava konstruisanje modela za predviđanje pojava. Primera radi, OLAP pruža mogućnost za utvrđivanje načina distribucĳe dohotka podnosilaca zahteva za stambeni kredit, dok statističke metode mogu analizirati varĳacĳe dohotka podnosilaca zahteva za stambeni kredit. Za razliku od njih dejta majning omogućava da se utvrde karakteristike podnosilaca zahteva za stambeni kredit i predvide budući korisnici ove usluge.

Data mining process

Ne postoji ustaljena šema po kojoj se odvĳa dejta majning proces, s obzirom na široki spektar tehnika koje se u njemu mogu primeniti. Takođe,

priroda poslovne odluke ponekad zahteva da se neki koraci prošire, uđe u detaljnĳu analizu podataka, ili da se neki koraci preskoče, jer su suvišni, ali i da se napravi “korak unazad”, odnosno vrati u prethodnu fazu procesa radi

provere valjanosti postupka. Međutim, to ne znači da nĳe moguće dati okvir za sprovođenje ovog procesa, već samo da je on prilagodiv potrebi rešenja poslovnog problema. Osnovu okvira za sprovođenje dejta majninga, kao što je u Šemi 1 naznačeno čine definisanje poslovnog problema, priprema podataka, kreiranje modela i

njegova primena (C. Baragion i saradnici, 2001. str. 29 i 30).

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Differences between OLAP analysis, statistics and data mining

Data mining process differs both from OLAP (Online Analytical Processing) and the classical statistical methods (C. Vercellis, 2009, p.81). The main difference is shown in Table 1. This difference lies in the active orientation based on inductive learning models, which is present in data mining. As opposed to data mining, OLAP and statistical models are passive in nature, not being based on interactivity.

Namely, statistical analyses are based on formulating hypotheses that are either confirmed or rejected on the basis of sample data. OLAP is a conceptual and intuitive model based on multi-dimensional data analysis, which implies examining the data through a large number of filters, i.e. dimensions. As opposed to statistical models and OLAP, data mining enables the construction of a model for scenario prediction. For instance, OLAP provides an opportunity to define the method for distribution of housing loan applicants’ income, whereas statistical methods may analyze the income variations of housing loan applicants. In contrast, data mining defines the characteristics of housing loan applicants and predicts the future users of this service.

Data mining process

There is no standardized scheme for conducting the data mining process, given the wide range of techniques that

it may include. Also, the nature of a business decision sometimes requires the expansion of certain steps, a more in-depth data analysis, or skipping of certain, redundant steps. On the other hand, it may also require going a ‘step back’, i.e. returning to the previous stage of the process in order to check the validity of the procedure. However, this does not mean that it is impossible to provide a framework for the conduction of this process, just that this framework is adjustable to the needs of the solution to the business challenge. The basis of

data mining process framework, just as the Scheme 1 indicates, includes the definition of the business challenge, preparation of data, creation of the model and its implementation (C. Baragion et al., 2001, p.29 and 30).

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Prva faza dejta majning procesa podrazumeva definisanje poslovnog problema koji se želi rešiti. Svrha dejta majninga je da ponudi solucĳu za rešenje određenog poslovnog problema na taj način što će donosiocima odluka pružiti dodatne informacĳe i znanje. U ovoj fazi određuju se osobe koje će uzeti učešće u procesu. U timu se moraju nalaziti osobe sa dobrim analitičkim sposobnostima, stručnajci za informativne tehnologĳe specĳalizovani za rukovanje bazama podataka i rukovodioci onih službi ili odeljenja u banci iz čĳeg delokruga poslovanja je problem koji se želi rešiti.

Ako je poslovni cilj povećanje broja korisnika usluge elektronskog bankarstva među korisnicima platnih kartica, u timu se pored rukovodilaca odeljenja za platne kartice i službe elektronskog bankarstva moraju naći i bankarski stručnjaci za informacione tehnologĳe, osobe zadužene za unapređenje prodaje, marketing i analitiku. Zadatak dejta majninga u ovom slučaju biće kreiranje modela kojim se može predvideti koji će vlasnici platnih kartica koristili usluge elektronskog bankarstva i zašto. Po okončanju procesa dejta majninga banka može svoje promotivne napore usmeriti samo na grupu kroz proces dejta majninga selektovanih klĳenata, čime će smaniti troškove i povećati uspešnost promocĳe.

U drugoj fazi se vrši priprema podataka, odnosno određuju vrste potrebnih podataka prema izvorima, obavlja njihova selekcĳa i vrednovanje. Podaci mogu da potiču iz različitih izvora. Neki se mogu nalaziti u dosĳeima klĳenata, dok drugi mogu poticati iz transakcionih baza banke. Takođe, neki podaci mogu poticati iz internih, a neki iz eksternih baza podataka. Mogu se koristiti podaci dobĳeni nekim prethodno sprovedenim marketing istraživanjem, ali i podaci iz analiza Udruženja banaka Srbĳe, Kreditnog biroa, Narodne banke Srbĳe, Ministarstva finansĳa, kao i podaci Republičkog zavoda za statistiku. Zato je u ovoj fazi cilj da se podaci objedine, sakupe na jednom mestu. Tim koji je formiran treba da odluči koji izvori podataka će biti najadekvatnĳi, i na koji način će doći do ukrštanja podataka.

Sledeći korak bio bi određivanje podataka koji su potrebni za konstrukcĳu modela, odnosno selekcĳa podataka. Transakcione

baze podataka koje poseduju banke pored šifre klĳenta i broja računa obično sadrže i nekoliko varĳabli - vrste, iznos i datume transakcĳa, dok dosĳei klĳenata tipično sadrže šifru klĳenta, broj računa, ime i prezime, adresu, telefon, demografske podatke, proizvode i usluge koji su do tada korišćeni. Baze klĳenata da bi bile upotrebljive i za marketinške aktivnosti zasnovane na metodama i principima direktnog marketinga trebale bi da sadrže i sve do tada upućene ponude klĳentima i njihov odgovor na njih, učestalost korišćenja pojedinih usluga i proizvoda, vrednost ili iznos prometa klĳenta u poslovanju sa bankom. U ovom koraku tim formiran za sprovođenje dejta majning procesa donosi odluku koje varĳable treba zadržati, a koje odbaciti.

U fazi transformacĳe podataka varĳable iz dostupnih baza podataka se transformišu u oblik pogodan za dejta majning. Podaci moraju biti u tabelarnom obliku pri čemu se u kolone svrstavaju varĳable, odnosno karakteristike, a u redovima se beleže zapažanja. Svaki red mora opisivati podatak značajan za banku. Često se podaci iz transakcĳske baze podataka moraju sumirati da bi bili korisni, pri čemu se dosta koristi ukupan i prosečan mesečni iznos transakcĳa po svim računima klĳenta. Na osnovu dostupnih varĳabli iz baza podataka računaju se atributi koji su značajni za rešenje problema.

U transakcĳskim bazama podataka i bazama klĳenata nalaze se velike količine podataka. Za izradu modela nĳe potrebno toliko podataka, pa se zato koristi primena metode uzorka kako bi se odabrala manja količina podataka potrebnih za model. Često se postavlja pitanje koliko je podataka dovoljno. Nema univerzalnog odgovora na ovo pitanje, to mora proceniti tim formiran za sprovođenje procesa dejta majninga. Primera radi za izradu stabla odlučivanja dovoljno je imati dve do tri hiljade podataka. Podaci za uzorak mogu se izabrati slučajnim izborom. Ako banka raspolaže bazom od 50.000 kilenata od kojih je samo njih 2.000 počelo da koristi neki novi proizvod ili uslugu, na bazi karakteristiika tih klĳenata napraviće se model. Za izradu modela nisu potrebni podaci od svih 50.000 klĳenata banke, već će biti dovoljno da se odabere njih 5.000. Ako se želi pouzdanĳe otkrivanje karakteristika

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The first stage of the data mining process involves the definition of the business challenge that needs to be addressed. The purpose of data mining is to offer a solution to a certain business challenge by providing additional information and knowledge to the decision-makers. In this stage the persons to take part in the process are appointed. The team must include the persons with excellent analytical skills, IT experts specialized for database administration, and heads of those bank sectors or departments in which the challenge to be addressed occurred.

If the business target is to increase the number of users of E-banking service among the payment card users, the team, in addition to the heads of payment cards department and E-banking department, has to involve banking IT experts, officers in charge of sales promotion, marketing and analytics. The task of data mining in this case is to create a model that could predict which payment card holders used the E-banking services and why. Upon completing the data mining process, a bank may target its promotional activities only at the group of clients selected by means of data mining, whereby it will reduce the costs and increase the promotion successfulness.

In the second stage the data are prepared, i.e. the types of necessary data determined by source, a�er which they undergo selection and evaluation. The data may originate from different sources. Some of the data may be found in the clients’ files, whereas some others may come from the bank’s transaction databases. Also, some data may originate from internal, and again some other from external databases. The banks may use the data obtained in a previously conducted market research, but also the data from the analysis of the Association of Serbian Banks, Credit Bureau, National Bank of Serbia, Ministry of Finance and the Statistical Office of the Republic of Serbia. Therefore, the objective in this stage is to collect the data, i.e. gather them at one place. The team that is formed should decide which sources of data will be the most adequate, and what will be the method of combining these data.

The next step would be to select the data needed for model construction, i.e. data selection. Transaction databases owned by banks, in addition to the client’s code and

account number, usually contain several variables - types, amount and dates of transactions, whereas the clients’ files typically contain the client’s code, account number, first name, last name, address, telephone number, demographical data, and products and services used so far. In order to be useful for marketing activities based on the methods and principles of direct marketing, clients’ databases should also contain all offers extended to clients and their response to them, the frequency of using certain services and products, the value or amount of turnover of the client in their transactions with the bank. As part of this step, the team formed to conduct data mining process decides which variables should be kept, and which are to be discarded.

In the data transformation stage, the variables from accessible databases are transformed into the form suitable for data mining. The data must be in table form with variables, i.e. characteristics being ordered in columns, and observations in rows. Each row must describe the data significant for the bank. The data from the transaction database o�en need to be aggregated in order to be useful, and the overall and average monthly amount of transactions per all clients’ accounts is frequently used. Based on the available variables from the databases, the a�ributes relevant for addressing the challenge are calculated.

Transaction databases and clients’ databases contain large amounts of data. Model building process does not require that much data, hence we use a sampling method in order to select a smaller amount of data needed for the model. The question of how much data is enough is frequently asked. There is no universal reply to this question; this has to be assessed by a team formed for the implementation of data mining process. For the sake of illustration, in order to create a decision tree, it would suffice to have two to three thousand pieces of data. The sample data can be selected randomly. If a bank has a database of 50,000 clients, out of whom only 2,000 started using a new product or a service, the model will be built on the basis of these clients’ characteristics. Model building process does not require the data about all 50,000 bank clients - it will suffice to select 5,000 of them. If we wish to be more reliable in

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klienata koji su počeli da koriste novi proizvod u okviru uzorka od 5.000 može se naći svih 2.000 klĳenata koji koriste novi proizvod, a preostalih 3.000 se mogu odabrati slučajnim izborom. Pošto je izabran uzorak za izradu modela, potrebno ga je podeliti na dva dela. Deo podataka će se koristiti za izradu modela, a drugi deo podataka će biti upotrebljen za testiranje valjanosti modela.

Zatim se pristupa vrednovanju podataka. U ovom koraku potrebno je analizirati postojanje netipičnih vrednosti i tzv. “prljavih” podataka. Netipične vrednosti javljaju se u svakoj bazi podataka, a radi se na primer o klĳentima sa izrazito visokim ili izrazito niskim primanjima. Potrebno je odlučiti šta učiniti sa netipičnim vrednostima. Moguće je napraviti posebnu analizu u vezi s tim podacima, izbaciti podatke o klĳentima koji imaju netipične vrednosti, izbaciti iz analize varĳablu koja ima mnogo netipičnih vrednosti ili netipične vrednosti zameniti s nekom drugom vrednosti - minimumom, maksimumom ili prosekom. Ili, vrednosti varĳable mogu da se podele u klase po osnovu niskih, srednje visokih i visokih primanja.

“Prljavi” podaci odnose se na nepostojeće vrednosti, nejasne definicĳe podataka i netačne vrednosti. Nepostojeće vrednosti su česte, a obično se radi o situacĳi da o klĳentu nedostaju neki demografski podaci. Potrebno je utvrditi da li je moguće te podatke izračunati na osnovu nekih drugih varĳabli. Primera radi, ako nemamo podatke o godinama klĳenta, te podatke možemo dobiti na osnovu jedinstvenog matičnog broja građana. Ako se radi o podacima koje je nemoguće utvrditi, postupak sa nepostojećim vrednostima isti je kao i postupak za netipične vrednosti. Nejasne definicĳe podataka česte su kod „prelaska” podataka iz jedne baze podataka u drugu, i one su najčešće posledica pogrešnog unosa podataka u računar. Zato se u otklanjanju nejasnih definicĳa treba koncentrisati na valjanost procesa kontrole sažimanja baza podataka.

Treća faza je faza modeliranja, a sastoji se u odabiru metode dejta majninga, izradi i vrednovanju modela. Na početku procesa modeliranja često se izrađuje analiza profila klĳenata, pri čemu se analiziraju odabrane karakteristike klĳenata kao što su: pol, godine starosti, zanimanje ili primanja.

U procesu dejta majninga koriste se različite metode i njihov izbor treba da bude prilagodiv kako samim podacima kojima se raspolaže, tako i rešenju poslovnog problema. Metode dejta majninga mogu da se podele u tri kategorĳe: otkrivanje, klasifikacĳa i predviđanje (Berry, 2000. at all). Metode otkrivanja odnose se na postupke kojima se traže pravilnosti u podacima bez prethodnog znanja o njihovom obliku. Ima dosta metoda kojima se mnogu otkriti pravilnosti u podacima, a većina njih je zasnovana na asocĳacĳi i komparacĳi. Metode za klasifikacĳu varĳabli koriste se za predviđanje čitave kategorĳe - npr. hoće li klĳent vratiti kredit ili ne. Za klasifikacĳu se često koristi stablo odlučivanja, logit regresĳa i neuronske mreže. Metode za predviđanje vrednosti varĳabli koriste se za predviđanje numeričkih vrednosti - npr. iznosa novca koji će klĳent godišnje potrošiti na osnovu njegovih godina starosti, zanimanja ili prethodnog trošenja. Tako se prema Slici 1 na osnovu godina starosti klĳenta može predvideti nivo njegove potrošnje.

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detecting the characteristics of the clients who started to use the new product, we may locate all 2,000 clients using the new product within the 5,000 clients’ sample, and the remaining 3,000 may be selected randomly. A�er the selection of the model building sample, it needs to be divided into two parts. One part of the data will be used for model building, whereas the other part will be used for the purpose of model validation.

Then, the evaluation of data commences. Within this stage, we need to analyze the existence of atypical values and the so-called ‘dirty’ data. The atypical values occur in each database, and those are, for instance, the clients with extremely high or extremely low income. We must decide what to do with the atypical values. Potential options are: to conduct a separate analysis in respect of these data, discard the data on those clients having atypical values, discard the variable that yields many atypical values or replace atypical values with some other value - minimum, maximum or average. Or, the variable values may be divided into classes according to low, medium-high and high incomes.

‘Dirty’ data refer to the non-existent values, ambiguous definitions of data and incorrect values. Non-existent values are frequent, and it is usually certain demographic data about the client that are missing. It is necessary to determine whether it is possible to calculate these data based on some other variables. For instance, if we lack the data about the client’s age, we may obtain them based on the unique personal identification number of that client. If the data are impossible to determine, the procedure concerning non-existent values is the same as the procedure concerning atypical values. Ambiguous data definitions are frequent in data ‘transfers’ from one database to another, and are usually the result of an erroneous data entry into the computer. Therefore, in order to eliminate ambiguous definitions, one should focus on the validity of the database reduction control process.

The third stage is the modeling stage, consisting of data mining method selection, model building and validation. At the beginning of the modeling process, we frequently conduct the clients’ profile analysis, whereby the selected clients’ characteristics are analyzed, such as sex, age, profession or income.

Data mining process involves various methods, and their selection should be adjustable both to the available data and to the business challenge solution. Data mining methods may be divided into three categories: detection, classification and prediction (Berry et al., 2000). Detection methods refer to the procedures that are used to detect irregularities in the data without any previous knowledge about their form. There are numerous methods that may detect irregularities in the data, most of them being based on association and comparison. Methods for the classification of variables are used to predict the entire categories - e.g. whether a client will repay the loan or not. The frequently used methods for the purpose of classification are decision tree, logistic (logit) regression and neural networks. Methods for the prediction of variables are used to predict numerical values - e.g. the amount of money a client will spend annually based on their age, profession or previous spending history. Thus, in Figure 1, based on the client’s age we may predict the amount of their spending.

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Potrošnja klĳenta po pravilu raste od sticanja punoletstva, da bi između četrdesete i pedesete godine života dostigla svoj maksimum. Nakon ovog razdoblja, većina klĳenata smanjuje svoju potrošnju. Za predviđanje se pored numeričkih vrednosti takođe mogu koristiti i neuronske mreže, ali i linearna regresĳa i metode vremenskih serĳa. Retko je da se koristi samo jedna metoda, već obično treba proveravati delotvornost nekoliko metoda. Tek nakon njihovog upoređivanja na konkretnom uzorku podataka bira se metoda dejta majninga. Primena svih ovih metoda bila bi teško moguća bez so�vera. Na tržištu postoje brojni besplatni i komercĳalni so�veri. Obe grupe so�vera mogu sadržati više metoda ili biti specĳalizovane samo za jednu (detaljan spisak so�vera dostupan je na web adresi www.kdnuggets.com/so�ware, 15.03.2010).

Nakon konstruisanja i primene metoda vrednuju se dobĳeni rezultati. Pošto su svi podaci prethodno podeljeni u dve grupe - podatke za izradu modela i podatke za testiranje, podaci za testiranje modela koriste se za vrednovanje metode, i na taj način proverava se efikasnost modela na podacima koji nisu korišćeni za njegovu izradu.

Završna faza dejta majning procesa sastoji se od interpretacĳe i korišćenja rezultata. U ovoj fazi ključna je uloga bankarskog stručnjaka za oblast u kojoj se poslovni problem rešava, koji na osnovu stručnih bankarskih znanja treba da interpretira rezultate. Korišćenje rezultata zavisi od njihovog predstavljanja i integracĳe u svakodnevno poslovanje. Što su rezultati bolje predstavljeni, to će se više koristiti. Dobro je takođe ako se modeli dejta majninga implementiraju u informatički sistem banke. Primera radi, model za predviđanje odlaska klĳenata konkurencĳi trebao bi da se integriše u bazu podataka klĳenata u obliku varĳable koja sadrži verovatnost odlaska klĳenta. Isto tako, model za prodaju dodatnih proizvoda trebao bi se integrisati u bazu klĳenata tako što bi se prikazalo koje proizvode i usluge bi klĳent verovatno mogao da koristi. Time će banka unaprediti mogućnost predviđanja svog odnosa sa klĳentom u budućnosti.

Oblasti primene dejta majninga u bankarstvu

Proces dejta majninga se u bankarskom poslovanju može koristiti za rešavanje problema u sferama marketinga, prodaje i rizika (M. Awad, L. Khan, B. Thuraisingham, L. Wang, 2009. str. 227 i C. Vercellis, 2009. str. 319).

Polazište svake marketinške aktivnosti trebalo bi da bude razumevanje potreba klĳenata. Dejta majning u tom smislu može da pomogne profilisanju i segmentiranju klĳenata u banci i građenju dobrih odnosa sa njima. Velike količine podataka o klĳentima bankama omogućuju da formiraju segmente kojima se mogu posebno prilagoditi pojedine usluge. Banke koje posluju u Srbĳi već odavno koriste tradicionalne metode segmentacĳe, međutim, takva tradicionalna segmentacĳa često može ”zamagliti” stvarno stanje. Korišćenjem dejta majninga mogu se pronaći segmenti koji su do tada možda bili zanemareni i otkriti informacĳe za neke nove segmentacĳe. Na taj način, prema novim modelima segmentiranja klĳenata mogu se svakom segmentu ponuditi specĳalno prilagođeni proizvodi čime se povećava profitabilnost poslovanja.

Dejta majning može biti osnova i za repozicioniranje pojedinih proizvoda i usluga i definisanje pravaca za razvoj novih proizvoda i usluga. Na osnovu rezultata dejta majninga može se otkriti kako određeni tržišni segment doživljava neki proizvod banke u odnosu na druge ili koliko su ime i karakteristike proizvoda prepoznatljivi. Na osnovu toga mogu se dobiti informacĳe o ceni, korisnosti ili nekom drugom svojstvu proizvoda ili usluge, što predstavlja osnovu za diferenciranje od konkurencĳe. Istovremeno, karakteristike proizvoda i usluga koje klĳenti preferiraju, odnosno one vrednosti proizvoda koje klĳenti smatraju najvažnĳim, mogu biti dobro polazište za razvoj strategĳa za proizvode koje tek treba kreirati i ponuditi tržištu, i poboljšanje postojećih proizvoda i usluga.

Takođe, dejta majnig primenjen u svrhu marketinga može pomoći zadržavanju postojećih klĳenata. U uslovima zasićenosti tržišta jedine mogućnosti rasta svode se na privlačenje klĳenata konkurencĳe ili prodaju drugih proizvoda i usluga postojećim

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As a rule, clients’ consumption increases a�er they become of age, only to reach its maximum between their forties and fi�ies. Once they pass this age, most clients reduce their consumption. In addition to numerical values, for the purpose of prediction one may also use neural networks, as well as linear regression and time-series methods. It rarely happens that just one method is used, but, instead, the efficiency of several methods needs to be checked. Only a�er these methods are compared on a specific data sample is the data mining method selected. The implementation of all these methods would hardly be possible without so�wares. The market offer involves numerous free-of-charge and commercial so�wares. Both groups of so�wares may contain several methods or be specialized for a single one (a detailed list of sofwares is available at the following web address: www.kdnuggets.com/so�ware, 15.03.2010).

A�er the construction and implementation of the method, the achieved results are validated. Since all the data are previously divided into two groups - model building data and model testing data, the model testing data are used for method validation. This way, the efficiency of the model is tested on the basis of data not used for its building.

The final stage of data mining process consists of interpretation and results usage. In this stage the key role is played by the banking expert in the field in which the business challenge that needs to be addressed occurred, who needs to interpret the results based on his/her expert banking knowledge. The usage of results depends on their presentation and integration into day-to-day operations. The be�er the results get presented, the more they will be used. It is also recommendable to implement data mining models into the IT system of the bank. For instance, the model predicting the clients’ departure to a competitor should be integrated into the clients’ database in the form of a variable containing the probability of the client’s departure. Likewise, the model for sales of additional products should be integrated into the clients’ database by showing which products and services a client could potentially use. Thus the bank will enhance the possibility of predicting its relationship

with the client in the future.

Fields of implementation of data mining in banking

Data mining process may be used in the banking business for the purpose of addressing the challenges in the field of marketing, sales and risk (M. Awad, L. Khan, B. Thuraisingham, L. Wang, 2009, p. 227, and C. Vercellis, 2009, p. 319).

The starting point of each marketing activity should be the understanding of the clients’ needs. In this respect, data mining may be helpful in terms of profiling and segmenting the clients in a bank, as well as establishing sound relations with these clients. Large amounts of data on their clients enable banks to form segments to which certain services may be specifically adjusted. The banks operating in Serbia have been using the traditional segmentation methods for quite a while, but, such traditional segmentation may o�en “veil” the real state of affairs. Using data mining may help detect the segments neglected so far and discover the information regarding some new segmentation types. Thus, according to the new clients’ segmentation models, each segment may be offered the specially adjusted products, whereby business profitability gets increased.

Data mining may also serve as a basis for repositioning of certain products and services, and for defining the directions for development of new products and services. Based on data mining results, it may be discovered how a certain market segment perceives a new product of the bank in comparison with other products, or how recognizable the name and characteristics of the product are. Based on this, one may acquire information about the price, usefulness or some other feature of a product or service, which is the basis for differentiation from the competition. At the same time, the characteristics of the products and services preferred by the clients, i.e. the values of the products that the clients deem most important, may be a good starting point for developing strategies for the products that are yet to be designed and offered in the market, as well as for improving the already existing products and services.

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klĳentima. Klĳenti najčešće prelaze u drugu banku zbog pogodnosti koje im se nude. Banke u Srbĳi poslednjih par godina utrkivale su se snižavanjem nominalnih kamata na revolving kartice. Niske kamate činiće svoje neko vreme i privlačiti klĳente, a banke će se nadati da će klĳenti nastaviti da koristite revolving karticu i nakon isteka perioda trenutnih pogodnosti. Međutim, ukoliko im se ne ponude nove pogodnosti ili povisi kamatna stopa veliki broj njih će prestati da ih koristi. Dejta majningom mogu se konstruisati modeli kojima će se predvideti verovatnost zadržavanja klĳenata.

U oblasti prodaje dejta majning se može koristiti za određivanje tzv. životne vrednosti klĳenata. Životna vrednost klĳenta je očekivana vrednost zarade od pojedinog klĳenta u određenom vremenskom razdoblju. Primera radi, banka može da razvĳe usluge namenjene studentima, i gotovo sigurno je da na ovakvim uslugama ne može očekivati veliki profit. Međutim, ako se stvori dobar odnos s klĳentom, u budućnosti se može ostvariti velika korist. Studentu koji je diplomirao i zaposlio se trebaće, između ostalog, tekući račun, kreditna kartica, kredit za auto. Zbog visokog obrazovanja očekuje se da će takva osoba imati i natprosečna primanja, pa će sebi moći da priušti te proizvode. Dejta majningom mogu se konstruisati modeli kojima će se predvideti životna vrednost klĳenta kako bi bankarski službenici mogli da posvete više pažnje klĳentima koji nisu trenutno profitabilni, ali bi to mogli postati u budućnosti.

Kroz dejta majning proces mogu se kreirati i modeli prodaje dodatnih proizvoda postojećim klĳentima, jer oni mogu da odrede verovatnost da će klĳent banke kupiti dodatni proizvod. Cilj ovakve analize nĳe samo povećati broj klĳenata koji će odgovoriti na novu ponudu, čime se smanjuju troškovi informisanja klĳenata. Ponudom odabranih proizvoda samo određenoj grupi klĳenata, takođe se povećava kvalitet odnosa s njima. Na taj način raste profitabilnost poslovanja, jer je trošak prodaje drugih proizvoda postojećim klĳentima mnogo niži od privlačenja novih kupaca uz istovremeno povećanje lojalnosti postojećih klĳenata.

Banke često koriste direktan marketing putem pošte i na taj način direktno na adrese

klĳenata šalju svoje ponude. Na primer, vlasnicima štednih računa u evrima može se slati ponuda da počnu da koriste platnu karticu kojom u zemljama evro zone mogu kupovati robu ili plaćati usluge uz povoljnost izbegavanja troška konverzĳe valuta. Kampanja može uključiti sve vlasnike štednih računa u evrima, i stopa reakcĳe klĳenata će se verovatno biti ispod dva odsto, što neće biti rezultat za pohvalu. Dejta majning može pomoći u smanjivanju uzaludnog napora i troška predviđanjem reakcĳe potrošača. Pošto se na taj način obavi selekcĳa klĳenata, banka će ovu ponudu poslati samo onim segmentima klĳenata za koje postoji velika verovatnoća da će kupiti proizvod, značajno uštedeti sredstva i postići visok stepen odziva u kampanji.

Modeli aktivacĳe klĳenata koje dejta majning može da razvĳe predviđaju verovatnost da će klĳent koga je banka pridobila, postati profitabilan. Na primer, klĳent često otvori račun u banci koji mu služiti samo za jednokratnu upotrebu. Banke koje posluju u Srbĳi beleže ovakve primere tokom konvezĳe valuta zemalja članica Evropske monetarne unĳe u evro, i tokom kampanje podele besplatnih akcĳa pojedinih javnih preduzeća građanima. Nakon obavljenog posla klĳent će završiti svoje poslovanje sa bankom. Takvim klĳentima se mogu ponuditi dodatne pogodnosti da bi se oni podstakli na aktiviranje.

Bankama je od suštinske važnosti i da ne odobre kredit osobi koja neće moći da ga vrati. Tada se koriste modeli dejta majninga za predviđanje rizika. Ovi modeli mogu se koristiti kako za kredite koji imaju neki oblik obezbeđenja kao što su žirant ili hipoteka, tako i za kredite koji se odobravaju putem revolving kreditne kartice ili minusa koji se odobrava po tekućem računu. Cilj primene dejta majninga u ovoj oblasti je da na osnovu prethodnog ponašanja klĳenta brzo otkrĳe rizik. Zato se kao izvori podataka za dejta majnig mogu koristiti informacĳe o transakcĳama klĳenta sa bankom u prošlosti, njegova trenutna zaduženost, broj korišćenih kredita i ažurnost njihovog otplaćivanja. Svi ti podaci se mogu dobiti iz transakcionih baza. Ali kako bi se povećala sigurnost, tim podacima moraju se dodati i ažurnost plaćanja računa za komunalne usluge, ukoliko klĳent te transakcĳe obavlja putem

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Also, data mining implemented for the purpose of marketing may help retain the existing clients. In times of market saturation, the only possibilities of growth are reduced to a�racting the competitors’ clients or selling other products and services to the existing clients. The clients most o�en go to another bank because of the benefits it offers. In the past several years the banks in Serbia have been racing to reduce the nominal interest rate on revolving cards. Low interest rates will serve the purpose for a while by a�racting clients, and the banks will be hoping that the clients will continue to use their revolving card a�er the expiry of their present benefits. However, if they are not offered new benefits or if the interest rate goes up, a large number of them will stop using them. Data mining may be used to design models for predicting the probability of client retention.

In terms of sales, data mining may be used to determine the so-called life-time value of a client. Life-time value of a client is the expected value of profit from a single client in a specified time period. For instance, a bank may develop student-oriented services, and it is almost certain that large profit cannot be expected from such services. However, if a sound relationship is developed with a client, large profit may be yielded in the future. A student who graduated and got employed will need, among other things, a current account, credit card, car loan. Thanks to his/her high education, such a client is expected to have above-average earnings, hence will be in the position to afford such products. Data mining may be used to construct the models for predicting the life-time value of a client, so that the bank officers could devote more a�ention to the clients who are not profitable at the moment, but may become so in the future.

Through data mining process one may also design the models for selling additional products to the existing clients, because they can determine the probability of the bank’s client purchasing the concerned additional product. The objective of such analysis is not just to increase the number of clients that will respond to the new offer, whereby the costs of clients’ notification are reduced. By offering selected products only to a certain group of clients, the bank also increases the quality of its

relationship with these clients. Thus, business profitability gets increased, since the cost of sales of other products to the existing clients is much lower than the cost of a�racting new clients, accompanied by the increased loyalty of the existing ones.

Banks o�en use direct marketing through the post-office, thus directly sending their offers to the clients’ addresses. For instance, the holders of Euro savings accounts may be sent an offer to start using a payment card for buying goods or paying for services in the Eurozone countries with the advantage of avoiding the currency conversion cost. Such a campaign may include all holders of Euro savings accounts, and the clients’ response rate will probably amount to less than two percent, which is not a commendable result. Data mining may help in reducing the fruitless efforts and costs by predicting consumer response. A�er conducting the selection of clients in this way, the bank will send its offer only to those segments of clients for which there is a high probability of purchasing the product, whereby it will save a considerable amount of funds and achieve a high level of responses within its campaign.

Clients’ activation models that data mining can develop predict the probability of a client, whom the bank have a�racted, becoming profitable. For example, clients frequently open bank accounts that will serve a one-time purpose. The banks operating in Serbia record such examples during the conversion of currencies of the European Monetary Union member countries into Euro, and during the campaigns by certain public companies distributing free shares to the citizens. A�er they close their business, the clients will end their operations with the bank. Such clients may be offered additional benefits so as to be encouraged to activate.

It is of crucial importance for a bank not to extend a credit to the person not being able to repay it. This is where data mining models are used for risk prediction. These models may be used both for credits with some form of security, such as a guarantor or a mortgage, and for credits extended by means of a revolving credit card or current account overdra�. The objective of data mining implementation in this field is to

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elektronskog bankarstva ili ih obavlja nalozima za plaćanje iz same banke. Izvori za dejta majnig proces u ovom slučaju obogaćuju se i podacima o socioekonomskoj situacĳi klĳenta koji se mogu pronaći u njegovom dosĳeu, ali ne treba ispustiti iz vida ni podatke o poslovanju firme u kojoj je klĳent zaposlen. Osim onih koji se mogu dobiti zvaničnim putem, valjalo bi uključiti i one koje se mogu dobiti nezvaničnim metodama prikupljanja podataka.

Plan sprovođenja procesa dejta majninga i organizacĳa tima

Kako bi se pojasnila korisnost dejta majninga, uputno je analizirati i konkretan plan aktivnosti i model organizovanja tima koji će sprovoditi dejta majning proces na rešavanju određenog problema sa kojim se suočava neka banka. U Tabeli 2 naznačene su glavne aktivnosti u ovom procesu (G. J. Mya�, 2007. str. 16).

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detect risks swi�ly, based on the past behaviour of the client. This is why the sources of data used in data mining may be the information on the client’s past transactions with the bank, his/her current indebtedness, number of used credits and the timeliness of his/her repayment. All these data may be acquired from the transactions’ databases. However, in order to increase security, these data need to be added the timeliness in paying utility bills, in case the client conducts such transactions by means of E-banking or standing orders of the bank itself. In this case the sources for data mining process are enriched by adding the data on socio-economic situation of the client, which may be found in his/her files, but one should also bear in mind the data about the business

of the client’s employer company. In addition to the data that may be obtained by using official methods, the bank should also include the data that may be acquired by using unofficial data collection methods.

Data mining process implementation plan and team organization

In order to clarify the usefulness of data mining, it might be commendable to analyze a concrete plan of activities and a model of organization of the team that is to conduct the data mining process in relation to the solving of a certain problem faced by a bank. Table 2 indicates the main activities in this process (G. J. Mya�, 2007, p.16).

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U projektnom timu treba odabrati osobu koja će biti vođa projekta (project leader). Za tu pozicĳu treba izabrati osobu koja raspolaže dobrim sposobnostima za poslovnu analizu. Timu je potreban i stručnjak za informacione tehnologĳe i stručnjak za primenu dejta majning procesa. U tim moraju biti uključeni rukovodilac odeljenja marketinga, ali i osoba zadužena za koordinacĳu u sektoru prodaje.

Polazne informacĳe za rešenje problema nalaze se u podacima koji su timu dostupni u dve baze: transakcionoj bazi i bazi klĳenata banke. Obe baze administrira odeljenje banke zaduženo za informacione tehnologĳe.

Realizacĳa projekta počinje inicĳalnim sastankom članova tima na kome se definišu faze projekta: pripremna faza, faza implementacĳe dejta majninga i faza primene konstruisanog modela. Tokom faze pripreme određuju se prioritetni podaci koji su potrebni iz obe prethodno navedene baze podataka i biraju metode dejta majninga koje će biti testirane. Odeljenja marketinga i prodaje imaće zadatak da podele klĳente banke u dve grupe. Na osnovu klĳenata banke svrstanih u prvu grupu treba graditi model za predviđanje, a na osnovu klĳenata u drugoj grupi napraviće se skala za rangiranje klĳenata u skladu sa njihovim preferencĳama. Situacĳu na trzistu nezavisnu od zadataka odeljenja za marketing i prodaju treba da napravi i vođa projektnog tima. Radi objektivnosti i pronalaženja što kvalitetnĳih podataka važno je da svaki od poslova bude urađen nezavisno. Rezultati do kojih se dođe biće upoređeni na posebnom sastanku, kada će se pristupiti validacĳi podataka za dejta majning model. Konstruisanje modela na osnovu odabranih podataka i njhovih karakteristika i izbor so�vera biće posao za vođu tima i informacione tehnologĳe. Testiranjem modela

putem primene različitih metoda treba doći do zadovoljavajućeg rešenja. Ukoliko testiranja ne daju željene rezultate moraju se redefinisati ciljevi koji su postavljeni pred tim.

Zaključak

Bez kvalitetne analize i planiranja dejta majning procesa neće biti osnova za realnu postavku, pa se tako može dovesti u pitanje uspeh svih poslovnih nastojanja (J. Han, M. Kamber, 2006. str. 675).

Ključni razlog zbog koga sve više firmi teži da koristi poslovne inteligencĳe leži u motivu uspešnĳeg poslovanja. Odavno se uvidelo da uspešnog poslovanja nema bez sakupljanja i evidencĳe podataka o tržištu, konkurencĳi, okruženju i o samim potrošačima. Međutim, svi ti podaci koji se iz dana u dan sve više gomilaju neće predstavljati informacĳe ukoliko ih poslovni sistemi ne obrađuju i analiziraju.

U hĳerarhĳi poslovne inteligencĳe, struktuiranoj kao na Slici 2, podaci se nalaze na početnoj, odnosno prvoj lestvici. Dejta majning je jedan od procesa koji omogućava da se prikupljeni i evidentirani podaci ukrste sa iskustvom i prevedu u informacĳe, a upravo

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Within the project team one person is to be appointed the project leader. The person chosen for this position should be skillful in business analysis. The team also needs an IT expert and an expert for data mining process implementation. The team should also involve the marketing department head, but also the person in charge of sales department coordination.

The start-up information needed for the problem solving is to be found in the data available to the team within two databases: transaction database and the bank’s clients’ database. Both databases are administered by the bank’s IT department.

The project implementation commences with the initial meeting of the team members at which the project stages are defined: preparatory stage, data mining implementation stage and the stage in which the constructed model is to be implemented. In the preparatory stage the required priority data are selected from the both, above-mentioned databases, and data mining methods to be tested are chosen. Marketing and sales departments will have the assignment to divide the bank’s clients into two groups. Based on the bank’s clients classified in the first group the prediction model is to be built, whereas based on the second group clients a rating scale is to be designed so that the clients could be ranked according to their preferences. The project team leader should also design a market situation independent from the marketing and sales departments’ tasks. With a view to achieving objectivity and finding the highest-quality data, it is important for each operation to be conducted independently. The obtained results will be compared at a special meeting, when data validation for the data mining model will be launched. The construction of the model on the basis

of selected data and their characteristics, along with the so�ware selection, will be conducted by the team leader and IT department. Testing of the model by means of various methods should lead to the satisfactory solution. If the tests do not provide desired results, the objectives set to the team need to be redefined.

Conclusion

Without a high-quality analysis and planning of data mining process, there will not be enough grounds for a realistic scenario, hence the success of the business endeavours may be jeopardized (J. Han, M. Kamber, 2006, p.675).

The key reason for an increasing number of companies wishing to use business intelligence systems lies in their aspiration to conduct successful business. Long time ago people realized that there is no successful business without collecting and recording data about the market, competition, environment and the consumers themselves. However, all these data piling up day by day will not present information unless business systems process and analyze them.

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na osnovu informacĳa gradi se novo znanje i konstruišu nove ekspertize. Najpotrebnĳe ekspertize u savremenom poslovanju su one koje su vezane za ljude, strukturu firme i odnose. Svaka od tih ekspertiza predstavlja posebnu vrstu kapitala, i na osnovu njih firma gradi konkurentsku prednost. A nju postižu samo mudre firme.

Literatura / References

1. Awad, M, Khan, Latifur, Thuraisingham, Bhavani, i Wang, Lei, Desing and Implementation of Data Mining Tools, Auerbach Publications, Taylor & Francis Group, Boca Raton, USA, 2009

2. Baragoin, C, Andersen, C. M, Bayerl, S, Bent, G, Lee, J, i Schommer, C, Mining Your Own Business in Banking Using DB2 Intelligent Miner for Data, IBM Redbook, San Jose, USA, 2001

3. Berry, M. J. A. i Linoff, G. S, Mastering Data Mining, John Wiley & Sons Inc, New York, USA, 2000

4. Gaber, Mohamed Medhat (Ed.), Scientific Data Mining and Knowledge Discovery - Principles and Foundations, Springer, London, United Kingdom, 2010

5. Giudici, Paolo i Figini, Silvia, Applied Data Mining for Business and Industry, Wiley Publication, West Sussex, United Kingdom, 2009

6. Jiawei Han i Kamber, Micheline, Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers, San Francisco, USA, 2006

7. Kudyba, Stephan, Managing Data Mining, Cyberteach Publishing, Hershey, USA, 2004

8. Liebowitz, Jay, Strategic Intelligence - Business Intelligence, Competitive Intelligence, and Knowledge Management, Auerbach Publications, Boca Raton, USA, 2006

9. Ma�hew, Housden i Bu�erworth-Heinemann Elsevier, Marketing Research and Information, Linacre House, Oxford, United Kingdom, 2005

10. Mya�, Glenn J, Making Sense of Data - A Practical Guide to Exploratory Data Analysis and Data Mining, John Wiley & Sons, Inc, New Jersey, USA, 2007

11. Rud, Olivia Parr, Data Mining Cookbook - Modeling Data for Marketing, Risk, and Customer Relationship Management, John Wiley & Sons, Inc, New York, USA, 2001

12. Vercellis, Carlo, Business Intelligence - Data Mining and Optimization for Decision Making, Wiley Publication, West Sussex, United Kingdom, 2009

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In the business intelligence hierarchy, structured as shown in Figure 2, the data are placed at the initial, i.e. first step of the ladder. Data mining is one of the processes enabling the collected and recorded data to be combined with experience and transformed into information, and it is on the basis of information that we build new knowledge

and construct new professional expertise. The most needed types of expertise in modern business are those related to people, company structure and relations. Each of these represents a separate type of capital, and it is based on them that the company builds its competitive edge. And this is something that only prudent companies may achieve.

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mogućnosti primene dejta majning procesa u bankarstvu