Business Intelligence with Data Mining

bank line light discipline with information Mining con Banking and pay inductions be maturation real(prenominal) fast in this globalization era. Mergers, acquisitions, globalization fool do these institutions bigger. No doubt, the info excessively grow real big and to a greater extent varied. Big selective information store such as information storage w atomic number 18ho usance and info marts argon provided to give a resultant on big entropy storage. On the separate sides, those entropy atomic number 18 needed to be analyse. pedigree apprehension finally comes in as a solution in analyzing those huge information. railway line watchword information specially with entropy dig bath dispatch a solution in further stopping point making.With unlike tools and techniques, selective information minelaying has been proven in many a(prenominal) aspects of bank line. Hidden informations that stored inner(a) either selective information wargo nho delectation or selective information marts toilette be gulled easily. In mannequin, those obscure informations atomic number 18 market and sparing trens, altercater trends, competitive harm, nifty products and advantages and likewise dirty dog provide better node relationship talk termsment. T here(predicate) is still angiotensin-converting enzyme(a) service in occupation intelligence with info digging that this paper lead focus on, i. e. take a chance trouble and frauds and losings pr evetion. One of product from banking and pay institutions is deferred payment bestows.It is really a senior high school essay melodic line, scarce with argument intelligence with entropy exploit especially miscellanea and clunk techniques, it throw out be maintained and utilise safely and of course with first-class honours degree put on the lines, lessen frauds and harmes and developmentd internet and taxations. Keywords Banking and pay, blood perce ption, information Mining, Risk circumspection, book of facts Loans doorway Banking and pay institutions argon growing cursorily nowadays. For mavin institution, in that location atomic number 18 much(prenominal)(prenominal) than one offices or branches in one country or even in different country.Globalization, mergers, acquisitions, competitions, market changes be well-nigh of the reasons behind why are they growing fast. As those banking and pay institutions grow, so do the entropy. In this case, banking and pay institutions probably obligate much much info than otherwise institutions. Every single guest or people has one or more accounts in one institution or more. The challenge is how to maintain those data easily, how to contract non bad(predicate) finis among those data, how to reach good product for nodes and how to fulfil good nodes that can bring much more net bring ins and incr jeopardizeup revenues.For those that can not maintain data and devil a ratiocination for further movement without dissect the data before volition bechance it demanding to be achievement or even lose in competition with other banking and finance institutions. Some of key success factors in banking and finance institutions, such as 1. Customer satisfaction Good client attention and good product are the key to satisfy guest. If the institution could manage the guest well and offer good product that can produce benefit to both sides and so it give justify customer forget be very satisfied. 2. Customer loyalty There is no guarantee that satisfied customers lead be loyal.Strategies and tactics are needed to retain those customers. 3. Incr facilityd profit &type A revenue Similar with business institutions, gaining profit and add revenue are the roughly pregnant thing. 4. Minimal pretend With many customers, banking and finance institutions need to analyze the hazards that probably could happen. non all of customers are good cu stomer. cheat or loss might happen. 5. cockiness for sunrise(prenominal) markets to increase customer Markets are changing rapidly. Winning the competition sum kind the customer. Offered products are the key here such as higher interest, bump admin cost and so forth 6. Efficiency of operationsSince banking and finance institutions construct several branches and many customers, the challenge is to make operations in casual executions become efficient. Problems in Banking and Finance Institutions Similar with other institutions in business, banking and finance institutions also fuck off some of problems in their business. Be broken in are some of those problems 1. unconnected data instance selective information are separated through branches all everywhere the place. The banking and finance institution testament find it hard to collect and analyze the data. This will also electric shock in ratiocination making beca practice session determination should be made after an alyzing all of the data. . High insecurity Banking and finance institutions have many customers and not all of those customers are good customer. want to find out whether the customer is realible or not. 3. How to detect fraud and frustrate loss Frauds and losses might happen in banking and finance institutions. Fraud in identification loans will ca office loss to the institution. 4. How to create good customer relationship To debate in the market 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 consider. score a good product and can compete with others product will tint in customer winning. 6. How to find the transcendental information inside those data to ease the closing making Huge data are needed to be analyze and there are some mystical informations in those data that could affect the decision maker in making the decision. If the decision made is crucial one, it could lead to coming(prenominal) success. Business knowledge Business recognition can be defined as an ability of an enterprise to comprehend and use information in order to increase the performance.Business intelligence has several activities, procedures and applications. Some of those that more often than not use are data Wareho using, Data Marts, OLAP Tools, tools for Extract Transform and Load (ETL), entropy Portals, Data Mining, Business Modelling, etc (Katarina Curko, 2007). Business Intelligence can also defined as the play of gathering high-quality and meaningful information about the subject matter universe researched that will help the individual(s) analyzing the information, draw conclusions or make assumptions (Muhammad Nadeem, 2004). In this paper, we shall focus more in data tap.Data mining intact works with data warehouse and data marts for data storage and extract transform and commit (ETL) tools. Som e of advantages by using business intelligence with data mining 1. mount profit and revenue for enterprise With business intelligence, the enterprise can gain the data access easily and integrated inside data warehouse & angstrom data marts. So the enterprise can service customers better and quicker which will impact in profit and revenue increment. 2. conclusiveness making With data mining in business intelligence, the enterprise can gain the hidden informations in those huge data and can make quick and halcyon decisions. . Expand the market segment With the ease of decision making, the enterprise can make decision in markets such as impairment, discount, etc which will impact in winning the market competition. Data Mining Data mining refers to computer-aided word form uncovering of antecedently unknown interrelationships and recurrences across seemingly unrelated attributes in order to auspicate actions, conducts and outcomes. Data mining, in fact, helps to identify pattern s and relationships in the data (Bhasin, 2006). Some of goal examples in using Data mining 1.Forecasting market price With data mining, enterprise can figure the market price and sink on the best price to compete the price in market. 2. Cross-selling and up-selling analysis Data mining can be employ to analyze market based on products. It subject matter enterprise can make cross-selling or up-selling to their products to optimize or increase the sales. 3. profile customers Data mining can be utilise to segment customers awaits on the category. For example we categorize customers by their profit or revenue. 4. Manage customer retentionNot only enterprises data, data mining can be employ to manage customer data which will impact in better customer relationship precaution. pic habitus 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 da ta etc. As mentioned above, those huge and conglomerate data will be stored in data warehouse and data marts. Before ledger entry either data warehouse and data marts, those data will be extracted, cleaned up and some cartridge clips transformed into different types of data.Then it will load the results into data warehouse and data marts. In this data warehouse and data marts, the data will be stored. at one time the user want to analyze the data using data mining, the system will gather the data stored in data warehouse and data marts. With some of disappearance and dicing techniques, data mining process the involve data and resulting in enterprise reports. With these reports, management 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 regardments. Below are some of those models or methods A ssociations Discovery Is used to identify the behaviour of specific events or processes. Associations discovery think occurrences within a single event. grammatical case of use in discovering when a soulfulness buys a toothbrush then whitethorn also buy a toothpaste or a soul buys a derriere may also buy the lighter. attendant Pattern Discovery Is comparable to associations discovery except that a sequential pattern discovery links events over time and determines how items relate to each ther over time. archetype of use in announceing a person who buys a copulate sets of computer may also buy a jump or router within terce months. sorting Is the most common data mining technique. Classification looks at the behaviour and attributes of predetermined groups. Data mining tool can classify to new data by examining the existing data that has been classified before. good example of use in classifying characteristics of customers. flock Is used to find different classs within the data.Clustering is similar to classification except that no groups have yet been defined at the beginning of running the data mining tool. Clustering divides items into groups based on the similarities the data mining tool finds. Clustering is used for problems such as detecting manufacturing defects or determination affinity groups for book of facts cards. Forecasting Is used to presage market or forecasting products in manufacturing enterprise. Comes in two types regression analysis (predict future based on whole past trends) and time epoch 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 adapt in globalization, flexible in market, keep growing, create innovations to gain more customers that will increase profit and revenue. The intriguing questions is how to achieve those requirements. Those institutions also do risk management to han dle frauds and losses. With high profit and revenue, it will be useless if the institution can not handle attainable risks, in this case frauds and losses are the most possible risks. They need customers but after customers increased so do the risks.So the possible way is to manage those risks. The analogous 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 always trying to create new innovation either in products or services.Mergers and acquisitions have inevitable made those institutions have really huge and heterogeneous data. unimaginable to maintain those data without new technologies (Ka tarina Curko, 2007). use Data Mining as beginning in Credit 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 essential 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 point of reference loans. Being able to predict risks, frauds and losses are the main tutelage these days in banking and finance institutions. Credit loans nowadays have been growing rapidly. around every single shop or business center allows payment with trust card, but we shall focus more on credit loans such as loan for business, vehicle etc. Credit loans have been the most interesting product for banking and finance institutions. Many customers are looking for credit availability to help them monetaryly.With the credit interests, the banking and finance institutions gain benefit. Quite interesting business when they can offer credit and gain the profit from the credit interests, but the most important question is how to guarantee that the customer is a good one or at least make certain(predicate) 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 density 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 boilersuit market (Dass, 2 006). We shall focus more on credit risk. Credit risk sagacity is key component in the process of commercial lending (Dass, 2006). The institution has gold to lend but to decide which customer or borrower is not an easy matter.We shall fit more about the customer or borrower, find their background, their market transaction, their current income, and in more extreme way is reading their current life. To make those tasks possible, we can use classification 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 cherished customers, middle valued 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. just in general, things to consider and analyze are customer background, customer history tra nsaction, customer credit history, customer account at some other banking or finance institution, customer income. Those are from credit customer or borrower perspective. They also consider and analyze market and thriftiness trends to calculate and manage the possible profit gained before make a decision to lend or give the credit. pic Figure 2. Overview of Data Mining Process (Classification & Clustering) in Credit LoansWith these data mining tools, the analyst from those institutions can easily decide to respect the credit or not. Logically, analyst or management inside institutions will decide to lend or esteem the credit beged by customers in high valued customer then it goes down until low valued customer. moreover not all decisions are correct, many aspects can cause wrong decision such as incomplete data or unconsistent data of customers, market & economy trends changing, or other aspects. But these tools surely help a plenteousness to do risk management in credi t loans which will impact in minimized rauds and losses and increased profits and revenues. Conclusion Banking and finance institutions have so many products and services offered to customers. One of those are credit loans. Credits that offered to customers or borrowers are not directly approved 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 ma nagement 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 Institute of Management, Ahmedabad . 2 Katarina Curko, M. P. (2007). Business Intelligence and Business Process Management in Banking Operations. Information Technology Interfaces . 3 Muhammad Nadeem, S. A. (2004). exercise of Business Intelligence In Banks (Pakistan). CoRR . Textbooks 1 Bhasin, M. L. (2006). The contract Accountant, Banking and Finance, Data Mining A warring 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.