Business Intelligence with Data Mining

agate line newsworthiness operation with makeing digging Abstract Banking and pay worlds ar ripening very fast in this globalisation era. Mergers, acquisitions, globalization draw make these creative activitys braggart(a)ger. No doubt, the info excessively grow re on the wholey wide and more than varied. Big information storage much(prenominal) as info wargonho map and information marts argon provided to chip in a solution on big entropy storage. On the early(a) sides, those selective information be needful to be submitd. railway line lore fin eachy comes in as a solution in analyzing those huge information. Business intelligence especially with entropy digging chamberpot bring on a solution in further finality fashioning.With various dents and proficiencys, selective information tap has been proven in umpteen aspects of melodic line. Hidden informations that stored at bottom both info wargonho implement or information marts imp ressionlife be pull ined easily. In example, those apart(p) informations argon grocery and delivery trens, competitor trends, competitive price, estimable harvestings and serve and to a fault kindle provide fall in node alliance trouble. on that point is becalm unmatchable benefit in trading intelligence with entropy dig that this root for induce boil down on, i. e. as sound out gravel intercoursement and frauds and departurees pr stilltion. ace of crop from banking and pay institutions is quote loans.It is rattling a high schooler(prenominal) risk stock, exclusively with worry intelligence with entropy mining especially mixed bag and clustering techniques, it squirt be retained and implemented safely and of course with depleted risks, minimized frauds and losses and additiond pelf and grosss. Keywords Banking and pay, Business cognizance, entropy excavation, guess copement, ascribe Loans Introduction Banking and finance instituti ons ar exploitation rapidly nowa days. For one institution, thither ar more than one offices or branches in one country or even in several(predicate) country.Globalization, mergers, acquisitions, contests, grocery changes are some of the reasons behind why are they growing fast. As those banking and pay institutions grow, so do the data. In this case, banking and pay institutions in all probability have much more data than other institutions. Every single node or people has one or more accounts in one institution or more. The challenge is how to deem those data easily, how to make good death among those data, how to relieve oneself good product for nodes and how to retain good guests that laughingstock bring much more wampums and incr assuagement revenues.For those that sewer non maintain data and make a closing for further movement without analyze the data onwards get out mystify it hard to be success or even lose in competition with other banking and pa y institutions. rough of disc anywhere success factors in banking and finance institutions, such as 1. Customer satisfaction level-headed guest anxiety and good product are the tell to satisfy client. If the institution could manage the guest well and draw out good product that discharge crap benefit to both sides then it leave behind guarantee customer bequeath be very satisfied. 2. Customer loyalty There is no guarantee that satisfied customers go away be loyal.Strategies and tactics are needed to retain those customers. 3. Increased profit & revenue Similar with production line organisation institutions, gaining profit and amplification revenue are the just about valuable thing. 4. Minimal risk With many a(prenominal) customers, banking and finance institutions need to analyze the risks that probably could happen. non all of customers are good customer. tosh or loss might happen. 5. training for new foodstuffs to increase customer Markets are changing r apidly. Winning the competition style winning the customer. Offered products are the key here such as higher interest, kick admin cost etc. 6. Efficiency of operationsSince banking and finance institutions have several branches and many customers, the challenge is to make operations in routine transactions become efficient. Problems in Banking and Finance Institutions Similar with other institutions in melodic phrase, banking and finance institutions also have some of problems in their lineage. under are some of those problems 1. disjunct data instance entropy are separated through branches all over the place. The banking and finance institution go out distinguish it hard to collect and analyze the data. This will also carry on in finis making beca work ending should be made aft(prenominal) analyzing all of the data. . High risk Banking and finance institutions have many customers and not all of those customers are good customer. withdraw to get word out whether th e customer is realible or not. 3. How to detect fraud and prevent loss Frauds and losses might happen in banking and finance institutions. Fraud in extension loans will ca intent loss to the institution. 4. How to hit good customer relationship To debate in the foodstuff and winning customer, banking and finance institutions need to create good customer relationship to satisfy customers and make them loyal. 5. How to create good productProduct is one the aspect that customers imagine. create a good product and can compete with others product will impingement in customer winning. 6. How to arise the mysterious information inner those data to ease the finale making Huge data are needed to be examine and there are some out of sight informations in those data that could affect the decision maker in making the decision. If the decision made is authoritative one, it could lead to emerging success. Business Intelligence Business Intelligence can be defined as an ability of an enterprisingness to comprehend and use information in order to increase the performance.Business intelligence has several activities, procedures and applications. Some of those that generally utilise are data Wareho development, info Marts, OLAP Tools, tools for Extract Transform and Load (ETL), development Portals, info Mining, Business Modelling, etc (Katarina Curko, 2007). Business Intelligence can also defined as the touch of playing high-quality and meaningful information about the subject matter being researched that will help the individual(s) analyzing the information, draw conclusions or make assumptions (Muhammad Nadeem, 2004). In this radical, we shall focus more in data mining.selective information mining industrial plant with data store and data marts for data storage and conjure transform and commove (ETL) tools. Some of advantages by using business intelligence with data mining 1. raise profit and revenue for first step With business intelligence, the enterprise can gain the data access easily and structured inside data warehouse & data marts. So the enterprise can service customers give way and quicker which will encounter in profit and revenue increment. 2. Decision making With data mining in business intelligence, the enterprise can gain the hidden informations in those huge data and can make quick and easy decisions. . stretch out the market segment With the ease of decision making, the enterprise can make decision in markets such as price, discount, etc which will impact in winning the market competition. Data Mining Data mining refers to computer-aided pattern denudation of previously inscrutable interrelationships and recurrences across seemingly un have-to doe withd attributes in order to predict actions, behaviours and outcomes. Data mining, in fact, helps to identify patterns and relationships in the data (Bhasin, 2006). Some of goal examples in using Data mining 1.Forecasting market price With data mining, e nterprise can predict the market price and see on the topper price to compete the price in market. 2. Cross-selling and up-selling abbreviation Data mining can be used to analyze market ground on products. It means enterprise can make cross-selling or up-selling to their products to hone or increase the sales. 3. Profiling customers Data mining can be used to segment customers depends on the category. For example we categorise customers by their profit or revenue. 4. Manage customer retentionNot only enterprises data, data mining can be used to manage customer data which will impact in better customer relationship trouble. pic Figure 1. Overview of Business Intelligence with Data Mining Source of data that we shall process come from various sources such as customer data, market data, transaction data, product data, service data etc. As mentioned above, those huge and heterogeneous data will be stored in data warehouse and data marts. Before entering either data warehouse and d ata marts, those data will be extracted, cleaned up and sometimes transformed into different types of data.Then it will turn on the results into data warehouse and data marts. In this data warehouse and data marts, the data will be stored. Once the substance abuser want to analyze the data using data mining, the system will gather the data stored in data warehouse and data marts. With some of slicing and dicing techniques, data mining process the required data and resulting in enterprise reports. With these reports, charge of enterprise then decides what to do next. Data Mining Techniques According to (Larissa T. Moss, 2003), data mining itself has many models and various methods in analyzing data.When to use one of these models or methods depend on the requirements. Below are some of those models or methods Associations denudation Is used to identify the behaviour of specific events or processes. Associations discovery links occurrences within a single event. Example of use in discovering when a person buys a toothbrush then may also buy a toothpaste or a person buys a cigarette may also buy the lighter. Sequential radiation diagram Discovery Is similar to associations discovery carry out that a sequential pattern discovery links events over time and determines how items relate to each ther over time. Example of use in predicting a person who buys a couple sets of computer may also buy a switch or router within cardinal months. categorisation Is the close to common data mining technique. Classification looks at the behaviour and attributes of predetermined groups. Data mining tool can crystalize to new data by examining the live data that has been classified before. Example of use in classifying characteristics of customers. Clustering Is used to find different groupings within the data.Clustering is similar to motley except that no groups have even so been defined at the outset of footrace the data mining tool. Clustering divides items in to groups establish on the similarities the data mining tool finds. Clustering is used for problems such as detecting manufacturing defects or finding resemblance groups for quote cards. Forecasting Is used to promise market or forecasting products in manufacturing enterprise. Comes in two types regression analysis (predict future based on exclusively past trends) and time sequence discovery (predict future based on time-dependent data values).Business Intelligence in Banking and Finance Banking and finance in this paper, is the institution that require to alter in globalization, flexible in market, keep growing, create origins to gain more customers that will increase profit and revenue. The challenging indecisions is how to make those requirements. Those institutions also do risk attention to handle frauds and losses. With high profit and revenue, it will be useless if the institution can not handle practicable risks, in this case frauds and losses are the closely poss ible risks. They need customers but after customers increase so do the risks.So the possible way is to manage those risks. The same question as above, how to make the risk management easily and cover up all the risks. With business intelligence, all of those things can be achieved. Banking and finance institutions can depend on business intelligence in many aspects. Efficiency of analyzing the data, detection of frauds and losses, risk management, customer management and product management are some of these aspects. Striving for success, banking and finance institutions eer trying to create new innovation either in products or services.Mergers and acquisitions have inevitable made those institutions have really huge and heterogeneous data. Impossible to maintain those data without new technologies (Katarina Curko, 2007). Using Data Mining as Solution in consultation Loans for Banking and Finance As mentioned above, this paper will focus more on data mining in business intelligence . After discussing the benefit of business intelligence in banking and finance institutions, at last we go to the last authorised question, how to extract the hidden informations from those huge and heterogeneous data.In this section, we shall focus more on how to predict frauds, losses and risks that might happen in cite loans. Being able to predict risks, frauds and losses are the main concern these days in banking and finance institutions. Credit loans now have been growing rapidly. Almost every single shop or business center allows payment with consultation entry card, but we shall focus more on computer address loans such as loan for business, fomite etc. Credit loans have been the most interesting product for banking and finance institutions. many customers are looking for source handiness to help them financially.With the credit interests, the banking and finance institutions gain profits. Quite interesting business when they can offer credit and gain the profit from the credit interests, but the most all important(predicate) question is how to guarantee that the customer is a good one or at least make sure the customer will pay back including the credit interests so those institutions will not get frauds and losses. We can say to prevent frauds and losses is a kind of risk management. Risk management really is a crucial step to do especially in banking and finance institutions.Risk management in banking and finance institutions itself covers many aspects such as liquidity risk, operational risk and concentration risk. Today, integrated measurement of different kinds of risk (market and credit risk) is moving into focus. These all are based on models representing single financial instruments or risk factors, their behaviour, and their interaction with overall market (Dass, 2006). We shall focus more on credit risk. Credit risk assessment is key component in the process of commercial altering (Dass, 2006). The institution has money to loan bu t to decide which customer or borrower is not an easy matter.We shall learn more about the customer or borrower, find their background, their market transaction, their contemporary income, and in more extreme way is learning their current life. To make those tasks possible, we can use compartmentalization or clustering in data mining technique. These data mining tools can provide a grouping of customer or borrower. Lets say there are three groups of customer or borrower that we want to manage. First, high cute customers, middle value customers and low valued customers. Before put customers into those groupings, there are many things to consider and analyze.Different institutions use different kinds of classification and analysis. But in general, things to consider and analyze are customer background, customer history transaction, customer credit history, customer account at another banking or finance institution, customer income. Those are from credit customer or borrower persp ective. They also consider and analyze market and economy trends to target and manage the possible profit gained before make a decision to bestow or give the credit. pic Figure 2. Overview of Data Mining physical process (Classification & Clustering) in Credit LoansWith these data mining tools, the psychoanalyst from those institutions can easily decide to approve the credit or not. Logically, analyst or management inside institutions will decide to lend or approve the credit quest by customers in high valued customer then it goes down until low valued customer. But not all decisions are correct, many aspects can suit of clothes wrong decision such as incomplete data or unconsistent data of customers, market & economy trends changing, or other aspects. But these tools surely help a lot to do risk management in credit loans which will impact in minimized rauds and losses and increased profits and revenues. Conclusion Banking and finance institutions have so many products and ser vices offered to customers. One of those are credit loans. Credits that offered to customers or borrowers are not directly pass if one of the customer or borrower makes a request of credit. Many aspects to consider and analyze. With business intelligence especially with data mining including data warehouse and data marts, those important aspects are collected, stored and analyzed. Specifically we use a couple of data mining technique i. e. classification and clustering.The purpose is to group the customer or borrower into groups that are easily to read and analyzed by institution analyst or management to ultimately decide to approve the requested credit or not. In this paper we suggest three groupings of customers or borrowers such as high valued customer, middle valued customer and low valued customer. Analyst or management also analyze the market and economy trends beside customer aspects. In the end, these business intelligence and data mining tools are used to ease in decision making to make the best decision for whole enterprise. References Journals 1 Dass, R. (2006).Data Mining In Banking And Finance A Note For Bankers. Indian represent of Management, Ahmedabad . 2 Katarina Curko, M. P. (2007). Business Intelligence and Business Process Management in Banking Operations. Information engine room Interfaces . 3 Muhammad Nadeem, S. A. (2004). Application of Business Intelligence In Banks (Pakistan). CoRR . Textbooks 1 Bhasin, M. L. (2006). The Chartered Accountant, Banking and Finance, Data Mining A Competitive Tool in the Banking. Oman. 2 Larissa T. Moss, S. A. (2003). Business Intelligence Roadmap The Complete Project Lifecycle for Decision-Support Applications. Addison Wesley.