Data Mining In Banking Sector Finance Essay - Free Essay Example

Sample details Pages: 9 Words: 2746 Downloads: 4 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? Data mining defines the nontrivial extraction of hidden, previously unknown, and theoretically useful information from data. It is the facts of extracting valuable information from large databases. Banks have several and vast databases. Don’t waste time! Our writers will create an original "Data Mining In Banking Sector Finance Essay" essay for you Create order The important business information can be extracted from these data stores. The main application areas of data mining in banking are Customer Relationship Management, Marketing, Risk Management, Data cleansing, Fraud Detection, Software Support, and Present Industry Status. Data mining tools are used by leading banks for customer segmentation and profitability, forecasting payment default, credit scoring and approval, detecting fraudulent transactions, etc. The study of private and public banks has been done to appraise the feasibility of implementation of techniques of data mining. The fundamental aim of this paper is to check the feasibility of implementation of the techniques in banking sector in India. It highlights the perspective applications of data mining to increase the performance of some of the core business processes in banking sector. INTRODUCTION: Data Mining is the process of extracting knowledge hidden from large volumes of raw data. The knowledge must be new, not clear, and one must be able to use it. Data mining has been defined as the nontrivial extraction of implicit, previously unknown, and possibly useful information from data. It is the knowledge of extracting useful information from large databases. Data mining is one of the tasks in the process of knowledge discovery from the database. Data mining applications has two primary components namely Data manager and Data mining tools/algorithms. Data mining techniques can be classified as artificial neural networks, genetic algorithms, nearest neighbour method, decision trees and rule induction. Data Mining In Banking Sector: The computerization of financial operations connectivity through World Wide Web and the support of computerized softwares has completely changed the basic concept of business and the way the business operations are being carried out. The banking sector is not exclusion to it. It has also observed a tremendous change in the way the banking operations are carried out. Since 1990s the entire model of banking has been moved to online transactions, centralized databases, online transactions and ATMs all over the world, which has accomplish banking system technically robust and more customer oriented. In the present day environment, the large amount of electronic data is being preserved by banks around the globe. The enormous size of these data bases makes it impossible for the organizations to analyse these data bases and to retrieve useful information as per the need of the decision makers. [4] Banks have to adjust to the changing requirements of the societies, where people not only repute a bank account as a right rather than a privilege, but are also appraised of the fact that their business is valuable to the bank, and if the bank does not aspect after them, they can take their business elsewhere. Technology in banking is not just the computerization of process, but it is much more than this. The amount of data collected by banks has grown rapidly in recent years. Current statistical data analysis techniques find it difficult to manage with the large volumes of data now available. This volatile growth has leads to the need for new data analysis techniques and tools in order to find the information unknown in this data. Banking is an area where massive amounts of data are collected. This data can be produced from bank account transactions, loan repayments, loan applications, credit card repayments, etc. It is expected that valuable information on the financial profile of customers is hidden within these enormous operational databases and this information can be used to improve the performance of the bank. [7] The banks in India and abroad have started using the techniques of data mining. Chase Manhattan Bank in New York, Fleet Bank Boston, ICICI, IDBI, Citi bank, HDFC and PNB in India are using data mining to analyse customer profiles to use them for their benefits. The banking industry across the world has undergone tremendous changes in the way the business is conducted. With the recent implementation greater acceptance and usage of electronic banking, the capturing of transactional data has become easier and simultaneously, the volume of such data has grown considerably. It is beyond human capability to analyses this huge amount of raw data and to effectively transform the data into useful knowledge for the organization [2]. The banking industry is widely recognizing the importance of the information it has about its customers. Undoubtedly, it has among the richest and largest pool of customer information, covering customer demographics, transactional data, credit cards usage pattern, and so on. As banking is in the service industry, the task of maintaining a strong and effective CRM is a critical issue. To do this, banks need to invest their resources to better understand their existing and prospective customers. By using suitable data mining tools, banks can subsequently offer ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€¦Ã‚ ¾tailor-madeÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€¦Ã‚ ¸ products and services to those customers [2]. There are numerous areas in which data mining can be used in the banking industry, which include customer segmentation and profitability, credit scoring and approval, predicting payment default, marketing, detecting fraudulent transactions, cash management and forecasting operations, optimizing stock portfolios, and ranking investments. In addition, banks may use data mining to identify their most profitable credit card customers or high-risk loan applicants. To help bank to retain credit card customers, data mining is used. By analysing the past data, data mining can help banks to predict customers that likely to change their credit card affiliation so they can plan and launch different special offers to retain those customers. Credit card spending by customer groups can be identified by using data mining. Following are some examples of how the banking industry has been effectively utilizing data mining in these areas. HISTORY OF DATA MINING IN BANKING: Keeping the requirement of use of information technology in the banking sector, the Reserve Bank of India constituted a committee on technology up gradation in the banking sector, the committee emphasized the usage of management information systems by the banks and recommended that by the use of data mining techniques data available at several computer systems can be accessed and by a combination of techniques like classification, clustering, segmentation, sequencing, association rules, decision tree various ALM reports such as Statement of Structural Liquidity, Statement of Interest Rate Sensitivity etc. or accounting information like Balance Sheet and Profit Loss Account can be generated instantaneously for any desired period/date. Trends can be examined and predicted with the availability of historical data and the data warehouse assures that everyone is using the same data at the same level of extraction, which removes conflicting analytical results and arguments over the source and quality of data used for analysis. In Indian Express Newspapers highlights the Citibank, HDFC Bank and ICICI Bank have taken the lead in using data mining along with leading mobile telephony service providers. The data mining techniques can be of enormous help to the banks and financial institutions in this arena for better targeting and acquiring new customers, fraud detection in real time, accommodate segment based products for better targeting the customers, search of the customers obtaining patterns over time for better retention and relationship, detection of developing trends to take proactive approach in a highly competitive market adding a lot more value to existing products and services and launching of new product and service bundles. The leading banks are using data mining tools for credit scoring and approval, customer segmentation and profitability, detecting fraudulent transactions, predicting payment default, marketing, etc. [7] APPLICATION OF DATA MINING IN BANKING SECTOR: As banking competition becomes more and more global and powerful, banks have to fight more creatively and proactively to gain or even maintain market shares. Banks which still trust on reactive customer service techniques and conventional mass marketing are doomed to failure or degenerate. The banks of the future will use one asset, information and not financial resources, as their control for survival and excellence. Most of this knowledge are currently in the banking system and generated by daily transactions and operations. This valuable information need not be collected by intrusive customer surveys or expensive market research programs. Marketing: One of the most widely used areas of data mining for the banking industry is marketing. The banks marketing department can use data mining to analyse customer databases and develop statistically complete profiles of individual customer preferences for products and services. By offering only those products and services that customers really want, banks can save substantial money on promotions and offerings that would otherwise be unprofitable. Bank marketers, therefore, need to focus on their customers by learning more about them. Bank of America, for instance, uses database marketing to improve customer service and increase profits. By consolidating five years of customer history records, the bank was able to market and sell targeted services to customers. Uses of Data mining in the area of Marketing: Customer Acquisition Marketers use data mining methods to discover attributes that can predict customer responses to offers and communications programs. Then the attributes of customers that are found to be most likely to respond are matched to corresponding attributes appended to rented lists of noncustomers. The objective is to select only noncustomer households most likely to respond to a new offer. Customer Retention Data mining helps to identify customers who contribute to the companys bottom line but who may be likely to leave and go to a competitor. The company can than target these customers for special offers and other inducements. Customer Abandonment Customers who cost more than they contribute should be encouraged to take their business elsewhere a customer has a negative impact on the companys bottom line. Market basket analysis Retailers and direct marketers can spot product affinities and develop focused promotion strategies by identifying the associations between product purchases in point-of-sale transactions. Risk Management: Risk management covers not only risks involving insurance, but also business risks from competitive threat, poor product quality, and customer attrition. Customer attrition, the loss of customers, is used in finance, retail, and telecommunications industries to help predict the possible losses of customers. Losing customers to competitors is a major concern for industries today, with the increasing amount of competition businesses are facing. Therefore, methods must be found to determine the number of customers who are likely to be lost to competitors so that a business can be used is to build a model of customers who are likely to leave and go to a competitive company. An analysis of customers who have recently left can often show non-nutritive patterns, such as after a customer has a change of address or a recent protracted exchange with one of the agents of the company. Data mining is broadly used for risk management in the banking industry. Bank executives necessity to know whether the customers they are dealing with are reliable or not. Providing new customers credit cards, prolonging existing customers lines of credit, and approving loans can be unsafe decisions for banks if they do not know anything about their customers. Data mining can be used to reduce the risk of banks that issue credit cards by determining those customers who are likely to default on their accounts. Credit and market risk present the central challenge, one can observe a major change in the area of how to measure and deal with them, based on the advent of advanced database and data mining technology. Today, integrated measurement of different kinds of risk (i.e., 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, making this field highly important topic of research. Financial Market Risk Credit Risk Fraud Detection: Another popular area where data mining can be used in the banking industry is in fraud detection. Being able to detect fraudulent actions is an increasing concern for many businesses; and with the help of data mining more fraudulent actions are being detected and reported. Two different approaches have been developed by financial institutions to detect fraud patterns. In the first approach, a bank taps the data warehouse of a third party and use data mining programs to identify fraud patterns. The bank can then cross-reference those patterns with its own database for signs of internal trouble. In the second approach, fraud pattern identification is based strictly on the banks own internal information. Most of the banks are using a hybridÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€¦Ã‚ ¸ approach [2]. One system that has been successful in detecting fraud is Falcons, fraud assessment systemÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€¦Ã‚ ¸. It is used by nine of the top ten credit card issuing banks. The data mining techniques will help the organization to focus on the ways and means of analysing the customer data in order to identify the patterns that can lead to frauds [10]. The bank mines customer demographics and account data along different product lines to determine which customers may be likely to invest in a mutual fund, and this information is used to target those customers. Bank of Americas West Coast customer service call centre has its representatives ready with customer profiles gathered from data mining to pitch new products and services that are the most relevant to each individual caller. [1] Portfolio Management: Portfolio management refers to the selection of securities and their continuous shifting in the portfolio to optimize returns to suit the objectives of an investor. Portfolio management package is one of the merchant banking activities recognized by Securities and Exchange Board of India (SEBI). The service can be extracted either by merchant bankers or portfolio managers or discretionary portfolio manager. There are three major activities involved in an efficient portfolio management which are as follows: Identification of assets or securities, allocation of investment and also defining the classes of assets for the purpose of investment, They have to decide the major weights, percentage of different assets in the portfolio by taking in to consideration the related risk factors, To end, they select the security within the asset classes as identify. Risk measurement approaches on an aggregated portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an induction of the expected return or price of a financial instrument. Both make it possible to manage firm wide portfolio actively in a risk/return efficient manner. The application of modern risk theory is therefore within portfolio theory, an important part of portfolio management. With the data mining and optimization techniques investors are able to allocate capital across trading activities to maximise profit or minimise risk. This feature supports the ability to generate trade recommendations and portfolio structuring from user supplied profit and risk requirement. With data mining techniques it is possible to provide extensive scenario analysis capabilities concerning expected asset pricesor returns and the risk involved. With this functionality, what if simulations of varying market conditions e.g. interest rate and exchange rate changes) cab be run to assess impact on the value and/or risk associated with portfolio, business unit counterparty, or trading desk. Various scenario results can be regarded by considering actual market conditions. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub portfolio can be benchmarked against common international benchmarks. Customer Relationship Management: In the era of cut throat competition the customer is considered as the king and its the customer only who is ruling the whole show. The concept of selling a product to the customer is outdated and obsolete, now the objective is to reach to the heart of the customer and hence to develop a sense of belongingness for the organization. The huge data bases of various organizations are storing billions of data items about the customers. Data mining can be useful in all the three phases of a customer relationship cycle: Customer Acquisition, Increasing value of the customer and Customer retention [5]. Data mining technique can be used to create customer profiling to group the like-minded customers in to one group and hence they can be dealt accordingly [8]. The information collected can be used for different purposes like making new marketing initiatives, market segmentation, risk analysis and revising company customer policies according to the need of the customers [9]. The profiling is usually done on the basis of demographic characteristics, life style and previous transactional behaviour of a particular customer. Customer profiling is to characterize features of special customer groups [10].

King Lear’s Sins Pale in Comparison to those Committed...

King Lear’s Sins Pale in Comparison to those Committed Against Him King Lear commits several acts that are nearly unforgivable. Not only does he exile a trusted, loyal servant, he also banishes his own daughter. Cordelia, unable and unwilling to submit herself to the ridiculous game of her father, is sent off to France with his curses. His subsequent action - the division of the land between his two ungrateful daughters - is the final act, the final sin, and one that plunges the land into turmoil. However, his actions do not excuse the responses they bring from his kin and kinsmen. The sins against him - the actions of his two daughters and the evilness of Edmund - are far greater than those he committed†¦show more content†¦Fury envelops him: Here I disclaim all my paternal care, Propinquity and property of blood, And as a stranger to my heart and me Hold thee from this for ever. (I.i.113-115) he exclaims, disowning his loving daughter. Unable to balance between his need for respect and his great love for his daughter, he succumbs to the madness that has threatened to overwhelm him. Unfortunately, Kent then steps in, far too early after Lears proclamation. Still riding his wave of anger, King Lear can hardly accept this second violation of his power. Fuelled by the madness, he recklessly banishes the noble from his court. These actions were terrible, and a sin against both; however, in both cases, he had a reason, one that perhaps does not excuse his act, but nevertheless explains it. His final sin, the division of the land between Goneril and Regan is, upon further examination, hardly a sin at all. It is probable that King Lear had planned ahead to this day for years, and the decision to spread his land between his daughters is not, as some would suggest, folly at all (Kermode 1251). In fact, it may well have been brilliance. Lear intended to give Cordelia one third of his kingdom, the central, more opulent third of his kingdom, and effectively use her to separate the two other daughter. This would allow

Einstein Essay free essay sample

The reason she would have most likely be asking this question is probably because the world at the time is going through tough times such as The Depression and it also being post World War 1 era. Einstein’s response to Phyllis’s question was ineffective because it lacks ethos, pathos, and it did not give a straight forward answer to the question. The lack of ethos made the argument less effective because even though Albert Einstein is one of the most famous scientists of the twentieth century, he did not show any credibility that he had towards the argument. Yes he might have won a Nobel Prize for Physics, but that does not mean he knows anything about religion, also being the only scientist responding to Phyllis’s question, Einstein does not necessarily have the correct answer because he does not give the point-of-view of the other scientists. Yes, Einstein was most likely one of the only known scientist at the time, and for that reason he would be the â€Å"go-to† guy, for this question, but that does not give him much credibility for this question. We will write a custom essay sample on Einstein Essay or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The lack of pathos made the argument less effective because he did not show any emotion towards the reader, Phyllis Wright. Einstein should have showed more compassion towards Phyllis because she was merely a 6th grader, not one of his colleagues. Yes, Einstein had many facts on why scientists may or may not pray, but by the way he worded the response, it seemed that he did not take into consideration that Phyllis was just a young girl. He also says that â€Å"a research scientist will hardly be inclined to believe that events could be influenced by a prayer,† thus making it seem that there was no reason for Phyllis to even ask the question, thus making her feel like she just wasted his time asking him the question. His answer too was not straightforward, due to how he would say one thing and then went on saying something else that would go against his first argument by doing this; he is making it difficult for Phyllis to comprehend what he is trying to say. If he were to use more pathos the argument would be more effective because it allows the reader to feel what the writer is feeling. Einstein did not lack logos, but he did fail to give a straight forward answer which in turn made the made the argument ineffective. He uses the â€Å"Red Herring Fallacy† in order to divert attention from the original question. For example he stated †the belief in the existence of basic all-embracing laws in Nature also rests on a sort of faith† which has nothing to do with the question on whether scientist pray or not. Using this fallacy makes Phyllis’s attention to her question divert from whether or not scientist pray to, whether or not there is a god. Einstein’s use of logos was used greatly throughout his argument, but he must remember that he is talking to a young girl and should not be talking to her as if it was one of his colleagues, so there is a chance that she did not comprehend the argument thoroughly. Because Einstein’s argument lacked ethos and pathos, as well as not giving a straight forward answer, it was ineffective. The fact that he lacked ethos, made this essay ineffective because being a scientist does not give him any credibility towards religion. The lack of pathos, made this argument ineffective because he needs to make the reader feel the way he felt while writing this response. The way he worded the response made the argument unclear of what he wanted to say, which made the reader unsure of what his stand on the question was. Ethos, pathos, and logos are all necessary when attempting to make an effective argument, but Einstein seemed to have left some of these key factors out of his argument.

The Greek Myth of Arachne free essay sample

All of us are created with a special purpose In this world. We are not created just to Increase the population, but we are created to know our special purpose and to help make this world a better place. All of us are gifted with deferent latent skills, Its lust up to us on how we are going to develop these skills and make use of It for our own good and for the good of others as well. In the story Earache she was also gifted with a certain skill. That Is her talent to eave fine soft cloth with the use of a high-standing loom. Earache was well known all over Greece because of her magnificent works of art. And because of that, people tend to visit Earaches cottage Just to witness her weave fine soft cloths. But because of her fame, she got blinded and sees herself being superior to others, to the point that she even challenged the goddess Athena. We will write a custom essay sample on The Greek Myth of Arachne or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page And because of her insolence. She was punished and cursed by Athena and turned her into a spider.Moral of the story: Be humble enough to accept the fact that youre not the only one whiff good at a certain skill. If you have an outstanding talent among others, share it and dont brag about it. Boastfulness will bring you nothing but chaos. It is better to count your friends than to count your enemies. Living in fame and luxury but hated by many is what you called baloney. But being loved by everyone because of your humility is what you called treasure, that you can be proud of for the rest your life.