JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 12, ISSUE 2, FEBRUARY 2012 32
BusinessIntelligence: Data mining and Decision making for Banking Industry 1
. S.Kannan, 2.K.Pappathi. and 3.M.Karnan
Abstract—Business intelligence is a broad category of applications and technology for gathering,providing access and analyzing data for the purpose of helping enterprise users enable the better optimal business decisions.Business Intelligence(BI) is about getting the right information,to the right decision makers,at the right time.Decicion Support System(DSS) is an interactive computer-based application that combines data and mathematical models to help decision makers solve complex problems faced in managing the public and private enterprises and organizations.The aim of this paper is to discuss the need of business insiders in the process of knowledge discovery and distribution,to make BI more relevant to business of the bank.We have also discussed about the BI,DSS applications in banking industry and provide a framework to integrate BI and DSS in banking industry. Index Terms—Business Intelligence, Customer-centric solutions, Data Mining, Knowledge Management and Banking.
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1 INTRODUCTION Bank worldwide use data warehousing BI solutions for performance measurement,profitability analysis,and risk management,historical analysis,managing compliance requirements,executive dashloards,regulatory reporting and customer relationship management.Once the business transactional data(deposits and loans etc) from all the branches of banks had been accumulated in the transactional processing system(i.e. a common database in a central server located in the data center),giving a consolidated view of the bank’s operations,the need of bank managers at various positions to know the financial ststus(financial ststements,cash flows,and summary reports etc.)of the bank in order to acquire new customers and retain existing customers,was perceived.Therefore,bank needed to develop a platform that would integrate data from various sources within the bank into an easy-to-use,easy-tolocate data delivery service.As a result BI came into existence.BI system brought the perception of knowledge discovery which bankers have quickly adopted for active support to decision making processes al all managerial levels.BI tools founded on information technologies sch as on-line analytical processing and data mining make possible intelligent business decision making in complex banking environment.In this paper we have discussed about BI,DSS,BI&KM applications for banks,literature review on BI and KM integration,need to integrate BI & KM and a framework for banks to integrate BI and KM.
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• 1. S.Kannan is with the MSU University, Tirunelvely, Tamilnadu, India, Research Scholar. • 2. K.Pappathi is with the Information Technology Department, Kings College of Engineering, Thanjavur, Tamilnadu, India, Assistant Professor, • 3. M.Karnan is with the Department of Computer Science and EngineeringTamilnadu Engineering College, Tamilnadu, India, Professor.
2 LITERATURE REVIEW 2.1 Review Stage Since the 1970s and 1980s,business have used business information and structured business analysis to tackle complex business decisions.Examples include revenue optimization models in asset-intensive usiness such as the airline industry,the hotel industry,and the logistics industry,as well as logistics network optimization techniques used in industries that face complex distribution challenges.DSSs range from sophisticated,customized analytical tools running on maintframe computers to spreadsheet-based products running on personal computers.DSSs vary enormously in price and sophistication and are application-specific.Accordingly,they have not systematically adderssed integration and delivey of business information and analyses to support the range of BI opportunities available to companies today[13-18]. 2.2 Demerits In the context of discussing information challenges for the 21st century, peter Drucker observed that”…information technology so far has been a producer of data rather than a producer of information” (Drucker, 2001) [8].This view comports with capital investment trends of past 15 years. of information. • Increasing data and content volumes. • Increasingly complex regulatory and general reporting requirements.The nimber of regulations required to keep the increasingly complex global economy transparent will continue to skyrocket. • Growing and increasingly complex business operations. • Compeitive differentiation—effectiveness vs efficiency.Effectiveness is the next major competitive differentiator for business.
3. METHEDOLOGY The procedure or procedures used to make a system or design as effective or fuctional as possible ,especially the mathematical techniques involve.The approaches to optimizing systems are
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varied and depend on the type of system involved,but the goal of all optimization procedures is to obtain the best results possible;subject to restrictions or constraints that are imposed[7-1113].The first step in modern optimization is to obtain a mathematical description of the process or the system to be optimized.A mathematical model of the process or the system is then formed on the basis of this description.Depending on the application,the model complexity can range from very simple to extremely complex.System models used in optimization are classified in various ways,such as linear versus nonlinear,static versus dynamic[21-25].Certain models lend themselves to rapid and well developed solution algorithms,whereas other models may not.When choosing between quality valid models,therefore,those that are cast in standard optimization forms are to be preferred.That is: • Genetic Algorithm. • Ant colony optimization. • Particle swarm optimization.
3.1 Genetic Algorithm (GA) A heuristic search technique[15-20] used in computing and artificial intelligence to find optimized solutions to search problems using techniques inspired by evolutionarybioloy:mutation,selection,reproduction[inheritance]and recombination.This search technique used in computing ti find exact or approximate solutions to optimization and search problems. Algorithm 1: Genetic Algorithm 1. [Start] Generate random population of n chromosomes (suitable solutions for the problem) 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population. 3. [New population] Create a new population by repeating following steps until the new population is complete. a) [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be seleted). b) [Crossover]With a crossover probability cross over the parents to form a new offspring (children).if no crossover was performed, offspring is an exact copy of parents. c) [Mutation]With a mutation probability muate new offspring at each locus (position in chromosome). d) [Accepting] place newoffspring in a new population. 4. [Repalce] Use new generated population for a further run of algorithm. 5. [Test] If the end condition is satisfied.stop, and return the best solution in current population. 6. [Loop] Go to step 2. Each iteration of this process is called a generation.A GA is typically iterated for anywhere fro 50 to 500 or more generations.The entire set of generation is call a run.At the end of run there are oten one or more highly fit chromosimee in the population.Since randomness plays a large role in each run,two runs with different random-number seeds will generally produce different detailed behaviors.GA researchers often report statistics(such as the best fitness found in a run and the generation at which the individual with the best fitness was discovered)averaged over many different runs of GA on the same problem.
3.2 Ant Colony Optimization (ACO) This algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graph.The original idea ha since diversified to solve a wider class of numerical problems,and as aresult,several problems have emerged, drawing on various aspects of behavior of ants.A short path,by comparition,gets marched over faster,and thus the pheromone density remains high. Ant Colony Optimization(ACO) studies artificial systems that take inspiration from the brhavior of real ant colonies and which are used to solve discrete optimization problems.In 1999 ,the Ant Colony Optimizationmetaheuristics was defined by Marco Dorigo,Di Caro and Gambardella.The ants move from vertex to vertex along the edges of the construction graph exploiting information provided by the pheromonr values and in this way incrementally building a solution.additionally,the ants deposit a certail amount of pheromone on the components,that is,either on the vertices or on the edges that they traverse.The amount of pheromone deposited may depend on the quality of the solution found.Subsequent ants utilize the pheromone information as a guide towards more promising regions of the search space.Thw ACO metheuristic is: Set parameters, initialize pheromone trail SCHEDULE_ACTIVITIES. ConstructAntSolution. DaemonActions {optional}. UpdatePheromones. END_SCHEDULE_ACTIVITIES. 3.3 Particle Swarm Optimization (PSO) Particle swarm optimization is a computational methos that optimizes a problem by iterative trying to improve a candidate solution with regard to a given measure of quality.Such methods are commonly known as metaheuristic as they make few or no assumptions about the problem being optimized and can search very large space of candidate solutions.However, metaheuristics such as PSO do not quarantee an optimal solution is ever found. The conventioanal PSo is used to discover the optimal solution in a static environment.Conventinal PSO has poor tracking characteristics when the optimal silution is moving.When the environment is dynamic,the task of the optimization is not only to acquire the extreme but also to track the extreme’s trajectory as closely as possible.A human social adaptive based PSO is used to discover and track the optimal solution in a dynamic environment.Each particle evaluates the knowledge it will be gradually forget by the particle and new knowledge will be learned. PSO os a robust stochastic optimization technique based on the environment and intelligence of swarms.PSO applies the concept of social interaction to problem solving solving.It was developed in 1995 by James Kennedy (Social-psychologist) and Russell Eberhart(electrical engineer).It uses a number of agents(particles) that constitute a swarm moving around in the search space looking for the best solution.In PSO,there have been two basic topologies: Ring Topology (neighborhood of 3) Star Topology (globol neighnorhood) 3.4 The Anatomy of the Particle A particle (individual) is composed of: Three vectors, • The x-vectors records the current position(location) of
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the particle in the search space, The p-vector records the location of the best solution found so far by the particle,and • The v-vector contains a gradient (direction) for which particle will travel in if undistributed. Two fitness values, • The x-fitness records the fitness of the x-vector,and • The p-fitness records the fitness of the p-vector. Each particle is treated as a point in a N-dimensional space which adjusts its”flying” according to its own flying experience as well as the flying experience of other particles.Each particle keeps track of its coordinates in the solution space which are associated with the best solution(fitness) that has achieved so far by that particle.This value is called personal best,phest.Another best value that is tracked by the PSO is the best value obtained so far by any particle in the neighborhood of that particle.This value is called gbest.The basic concept os PSO lies in accelerating each particle toward its phest and the gbest locations,with a random weighted acceleration at each time. •
A.Business Intelligence in Banking Industry A BI system has evident importance as a communication and information diffusion channel, preferably one that is open, trustworthy, transparent and permanent. In supporting the monitoring and evaluation of business results while maintaining information integrity (Petrini and Pozzebon 2009). Zeng et al. (2009) define BI as “The process of collection, treatment and diffusion of information that has an objective, the reduction of uncertainty in the making of all strategic decisions.” Experts describe Business intelligence as a “business management term used to describe applications and technologies which are used to gather, provide access to analyze data and information about an enterprise, in order to help them make better informed business decisions (Zeng et al 2009).” Business Intelligence (BI) refers to various software solutions, including technologies such as ETL, Data warehouse, OLAP, Data mining &other reporting applications, share point server and web-enabled interface and methodologies needed to acquire the right information necessary for the business decision-making with the major purpose of enhancing the overall business performance on a marketplace. Data will be extracted using ETL (Extraction transformation and loading), stored in DW and generated reports (will be generated with the help of OLAP, DATA MINING and other reporting tools) can be accessed by the endusers through user interface. The BI tools were able to analyze the data for decision support in a fast and accurate manner. ETL Process: ETL packages extract data from internal and external sources, eliminate data error and redundancies, and provide tailor data for access and analysis and load to DW. An important part of this process is data cleansing where variations in data schemas and data values from disparate transactional systems are resolved. OLAP (online analytical process): Depending on the organizations requirement one or more data cubes will be created. Each OLAP database contains specific number of cubes and dimensions. OLAP is a multidimensional model can then be created which supports flexible drill down and roll-up analyses (rollup analyses create progressively higher-level subtotals, moving from right to left through the list of grouping columns. Finally, it creates a grand total).
3.5 Business Intelligence Applications in Banking Industry The way organizations approach a business intelligence project and the culture of the organization is as much of a factor of success as the integrated technology used to deliver the solution. BI would centralize the customer’s information of bank, providing valuable insight (e.g. historical analysis, performance analysis, what if analysis, profit analysis, executive dashboards for managing customer relationship) throughout the organization, to improve the efficiency and provide better customer support. This would enable all the employees of the bank, to obtain all the relevant information from a single source, the BI systems, in order to carry out their business. Applications of BI in banks can be summarized as follows ((Bhasin 2006); (Katarina, et al, 2007), (Koh & Chan, 2002), (Decker, 1998)). 3.5.1 Bank Performance (BP) Banks analyze their historical performance over time to be able to plan for the future. The key performance indicators include deposits, credit, profit, income, expenses; number of accounts, branches, employees etc. 3.5.2 Financial Dervatives (FD) Derivatives are financial instruments whose value is based upon other, simpler, instruments or market variables. These instruments or variables usually include prices of commodities, stocks, or bonds, or any other type of asset which is normally traded. Derivatives, however, can also be dependent on other variables, including weather or market indices – anything quantifiable (Hull 1). In fixed-income trading, derivatives such as options and forward contracts are often used to hedge risk in bond position (Hull 6). 3.5.3 Risk of Financial Derivatives (RFD) Risk in financial derivatives is measured in dimensions denoted by Greek letters – the most common being delta (Δ), gamma (Γ), and Vega (ν). Vega is not technically a Greek letter, but it is commonly used with the symbol for nu. There exist other Greek risk characteristics, however these are outside of the scope of this project and will not be discussed here. Delta (Δ) risk describes the way an option’s price will change as the underlying asset or instrument’s price changes, providing a characteristic of the relationship between price of the asset and its derivative. Mathematically, delta for a stock’s call option can be defined as: (1) Where c is the price of a call option on the asset and S is the stock price (Hull 360). Since delta is defined as a first-order mathematical derivative, it is considered a “first order” measurement of risk. Vega is a measure of portfolio volatility – that is, the rate at which portfolio value will change given the rate of change in volatility of the underlying assets. This rate is defined mathematically as: (2) Where V is the value of the portfolio and is the volatility of the asset.
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3.5.4 Data Visualization (DV) Visualization is the process of presenting data in a useful way to interested parties. When considered in a methodical way, visualization efforts take place in seven steps, according to Ben Fry. These are acquiring data, parsing and providing structure, filtering to narrow the scope of the data, and mining in order to find patterns or hidden information, representation in the form of a chart or graph, refinement of the representation method, and enabling the user to interact with the data. Acquisition and parsing of data will be addressed in other parts of this literature review, however filtering, mining, and visual representation are all key elements of a visualization strategy when applied to the types of data involved in this project. Fry further demonstrates that the seven steps of data visualization are not necessarily sequential, but instead a suggestion, dependent on the needs of the project. A proper visualization project uses the steps as a framework and then reorganizes the working steps to make the most sense for users and the data involved. Emphasis is placed on evaluating the needs of the entire project instead of basing decision making in a piecemeal form, developing each component one at a time. Particularly important in considering the project is the matter of when functions are actually needed - the temporal nature of data analysis. 3.5.5 Marketing One of the most widely used areas of data mining for the banking industry is in marketing. The bank’s marketing department can use data mining to analyze customer databases and develop statistically sound profiles of individual customer preferences for products and services. By offering only those products and services the customer really wants, the bank saves money on promotions and offerings that would otherwise be unprofitable. 3.5.6 Risk Management (RM) In a promptly changing and uncertain financial world, banking institutions need to rely more on fact-based actionable information, gleaned from ever-increasing data assets, to reduce risk wherever possible. BI is widely used for risk management in the banking industry. Bank executives need to estimate the reliability of their customers. Lack of knowledge regarding future customers may prove to be a great risk while offering new customers credit cards, extending existing customers lines of credit, and approving loans. Construct Credit scoring models to assess the credit risk of the loan applicants and construct fraud detection models to give signals of possible fraud transactions. Card theft analysis showed that number of transactions increases rapidly after the theft. By comparing expected average number or value of daily transactions, the authorization system can issue an early warning. 3.5.7 Fraud Detection (FD) 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 stopped. According to Decker, 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 (potentially containing transaction information from many companies) and uses 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 bank’s own
internal information (Decker, 1998).
3.5.8 Customer Acquistion and Retention BI helps in acquiring and retaining customers in the banking industry. BI techniques can be used to study customers’ past purchasing histories and know what kind of promotions and incentives to target customers. Also, if a branch has seen a number of people leave and go to competitors, BI can be used to study their past purchasing histories, and use this information to keep other customers from doing likewise. The findings can be used to prepare e-mail catalogues, target advertisements and promotion campaigns.
4. INTEGRATION OF BI AND DSS Studies show that IT executives believe business managers do not understand what data they need and business managers reflect their belief that IT executives lack the business acumen to make meaningful data available. There is no easy solution to this problem; the beginning of the solution is for business managers and IT managers to pledge to work together on this question(Davenport and Harris, 2007).One of the factors for BI success is team work and business-IT alignment, and is necessary to support adoption and use of BI. Second involves implementation success outcomes when democratization or universal user adoption of BI has been achieved. In past studies measuring BI success, only a small portion of users had access to BI capabilities, successful outcomes can be realized while extending BI benefits to all users (Joseph, 2011). BI/MIS converts data into information, and then into knowledge that finally meets the needs of users of the system.
4.1 Integrate BI and KM in Banking Industry R. I. Scott et al (2001) have shown the extensive role that domain knowledge plays in every step of the Business Intelligence Implementation process. In this case, the information was supplied by banking domain experts. The aim to integrate, ETL,OLAP, data mining, data warehousing, business modeling and high performance computing technologies to enable banks to increase profitability by the improved use of the vast amounts of customer-related data they hold. The interpretation of BI results is another step in the BI implementation process that relies heavily on domain knowledge. Often, this interpretation is based on the intuition of a domain expert and is therefore difficult to model (Scott et al, 2001). KM plays a role on all four processes of Nonaka KM model whereas BI plays a role on combinations, i.e., BI will transform the dirty data into explicit/codified knowledge. But the experience of the end-users on BI usage can be called as tacit and KM will help end-users to share their experience by converting that into codified /explicit knowledge.BI deals with a subpart of the KM, without BI it is not possible to get that knowledge, so Integration of BI and KM will provide a more value. 4.2 Experiment and Result A key step in the formulation of any optimization problem is the assignment of performance measures that are to be optimized. The success of any optimization result is critically dependent on the selection of meaningful performance measures. In many cases, the actual computational solution approach is secondary. Ways in which multiple performance measures can be
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incorporated in the optimization process are varied. The BIG BANK is currently flourished bank in the financial sector. BIG BANK have 10 million account holders. They are going to introduce the Credit Card in the market. In the current financial market, Lots of Competition for the Credit Card Sectors. If we find the Valued Customers, It’ll be a big successful and back bone for the bank. Since The bank is flourished bank, They don’t want to take the risk in the Financial market.Since, they have decision to sell the Credit card from the Valued and Trust worthy Internal Customer only. Plenty of account holders are in the Banks Data warehouse. We need to identify valued customer from the Data warehouse. In the Banking &finance sectors, the Customer profitability analysis. Determinate the overall profitability of individual customer, current and long term, provide the basis for highprofit sales and relationship banking, maximize sales to highvalue customers, reduce costs to low-value customers, provide the means to maximize profitability of new products and services. Establish patterns of credit problem progression by customers class and type, warn customers to avoid credit problems, to manage credit limits, evaluate of the bank’s credit portfolio, reduce credit losses. Improve customer service and ac-
count selling, facilitate cross selling, improve customer support, and strengthen customer loyalty. In data envelopment analysis the units being compared are called decision-making units (DMUs), [23] since they enjoy a certain decisional autonomy. Assuming that we wish to evaluate the efficiency of n units, let N = {1, 2 . . . n} denote the set of units being compared. If the units produce a single output using a single input only, the efficiency of the j th decisionmaking unit DMUj , j _ N, is defined as θj = yj / xj (3) in which yj is the output value produced by DMUj and xj the input value used. If the units produce multiple outputs using various input factors, the efficiency of DMUj is defined as the ratio between a weighted sum of the outputs and a weighted sum of the inputs. Denote by H = {1, 2 . . . s} the set of production factors and by K = {1, 2, . . .,m} the corresponding set of outputs. If xj, i H, denotes the quantity of input i used by DMUj and yj , r K, the quantity of output r obtained. Efforts undertaken to develop BI systems have resulted in many business solutions that allow for effective support of manager’s work.
Instead of nuggets or pockets of information from corporate databases, it provides a true 360º view of attitudes and behaviors, combines structured and unstructured data, meshes solicited and unsolicited feedback, and keeps a real-time pulse on business (Kadayam, 2002). Banks will be able to manage explicit information and thereby transform the information to knowledge which in turn can help bank in making better decisions and lead them to be in a better position in contemporary business competitive environment. This integration will not only facilitate the capturing and coding of knowledge but also enhances the retrieval and sharing of knowledge across the bank to gain strategic advantage and sustain in competitive market.
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5. CONCLUSION Banks can use technology to improve their performance and they can get the sustainable competitive advantage. Business intelligence needs to provide us with feedback information that can be used to evaluate a decision. It can provide that foundational and feedback information. Key Performance Indicators (KPIs) are highly summarized measures designed to quickly relay the status of that measure. According to our study, we can conclude that proper integration of BI & KM can help bank to get wide benefits. It includes historical context, not just a shallow examination of what is apparent and easily accessible.
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S.Kannan is an Assistant professor of Information Technology at Kings College of Engineering at Thanjavur. He holds MS in Information Technology from Bharathidhasan University at Tiruchirappalli and ME in Software Engineering at Periyar Maniammai College of Technology for women, AnnaUniversity, Chennai and currently doing PhD in Software Engineering from Manonmaniam Sundaranar University at Tirunelveli. He special interests include Business Intelligence Systems, data warehouse, Decision Support Systems, knowledge management, Data Mining, Wireless Sensor Network. K.Pappathi is an Assistant professor of Information Technology Department in Kings College of Engineering,Thanjavur,Tamilnadu, India.She have completed B.Tech (Information Technology) in Anjalai Ammal Mahalingam Engineering College, Kovilvenni, Thanjavur, from Anna University, Chennai, achieved during 2001-2005 and M.E (Computer Science and Engineering) in J.J. College of Engineering and Technology,Tiruchirappalli from Anna University, Tiruchirappalli, achieved during 2008-2010. She area of interest is Multimedia communications, Computer Networks, Wireless Sensor Networks and Data Mining and Warehousing. Dr.M.Karnan is a Professor of Computer Science and Engineering at Tamilnadu Engineering College in Coimbatore. He holds ME in Computer Science and Engineering from Coimbatore Institute of Technology in Coimbatore and PhD in Computer Science from Gandhi gram Rural University in Dindugal.He special interests include Business Intelligence Systems, data warehouse, Decision Support Systems, knowledge management, Data Mining, Soft Computing.
