Centralized Financial Networking System for Banking Applications through Data Mining Techniques

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1 Centralized Financial Networking System for Banking Applications through Data Mining Techniques P.V.D Prasad, Senior Consultant, J M R InfoTech, Beta Building, Sigma Soft Tech Park, Whitefield, Bangalore Abstract -Financial sector is the core sector to decide the country s GDP. The substantial and continuous development of any country is based on the financial health of the nation. Last few decades witnessed the proliferation of financial reforms, liberalization and globalization of Indian economy coupled with rapid revolution in information technology (IT).The paper presents the benefits of applying data mining (DM) and data warehousing (DW) techniques in customer relationship management (CRM) of the financial sectors like banking. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. Key words - centralized system, financial Networking, Data Mining, Operational Data. I.INTRODUCTION Technological innovations have enabled the banking industry to open up efficient delivery channels to the public. IT has helped the banking industry to deal with the challenges the new economy poses. Nowadays, Banks have realized that customer relationships are a very important factor for their success in the market. Advent of information technology and cyber devices heralded a new world and bought tremendous change in all the sectors of the economy. For this banks are exploring new financial products and service options that would help them grow without losing existing customers. Financial services are the economic services provided by the finance industry, which encompasses a broad range of organizations that manage money, including unions, banks, credit card companies, insurance companies, accountancy companies, consumer finance companies, stock brokerages, investment funds and some government sponsored enterprises. II.THE PRIMARY OPERATIONS OF BANKS INCLUDE Keeping money safe while also allowing withdrawals when needed. Issuance of chequebooks so that bills can be paid and other kinds of payments can be delivered by post.provide personal loans, commercial loans, and mortgage loans.issuance of credit cards and processing of credit card transactions and billing.issuance of debit cards for use as a substitute for cheques.allow financial transactions at branches or by using Automatic Teller Machines (ATMs).Provide wire transfers of funds and Electronic fund transfers between banks through internet. Provide Charge card advances of the Bank's own money for customers wishing to settle credit advances monthly. Provide a check guaranteed by the Bank itself and prepaid by the customer.accepting the deposits from customer and provide the credit facilities to them. Sell Investment products like Mutual funds etc. III.TECHNOLOGY APPLICATION IN BANKS The various technology applications in banking: a) Data Warehousing b) Data Mining c) Electronic Data Interchange d) Corporate Web Sites e) Management Information System ISSN: Page 220

2 IV.DATA WAEHOUSING AND DATAMINING 4.1. Data warehousing-in computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a system used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. 4.2 Data mining-data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary sub-field of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, disinterestedness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. This illustrates five things: a. Data Sources (operational systems and flat files) b. Staging Area (where data sources go before the warehouse) c. Warehouse (metadata, summary data, and raw data) d. Data Marts (purchasing, sales, and inventory) e. Users (analysis, reporting, and mining) V.ARCHITECTURE OF DATAMINING Data mining is described as a process of discover or extracting interesting knowledge from large amounts of data stored in multiple data sources such as file systems, databases, data warehouses etc. This knowledge contributes a lot of benefits to business strategies, scientific, medical research, governments and individual. 4.3 ARCHITECCTURE OF DATA WAREHOUSE Banks require a variety of solutions ranging from a fully centralized global data warehouse (DW), to not integrated data marts (DM) for the emerging needs of the departments. Both of these extreme solutions have significant disadvantages along with their benefits, so it is worth considering the indirect variant presented here based on a global data source ODS (Operational Data Store), a global DW and DMs. Steps involved in architecture are a. Client gives the data b. Data assembling is done c. Data verification d. Task assignment by team leader to employees e. Finally data is dispatched ISSN: Page 221

3 To best apply these advanced techniques, they must be b. Hedge fund management - Hedge funds often fully integrated with a data warehouse as well as flexible employ the services of "prime brokerage" divisions interactive business analysis tools. Many data at major investment banks to execute their trades. Mining tools currently operate outside of the warehouse, c. Custody services - the safe-keeping and processing requiring extra steps for extracting, importing, and of the world's securities trades and servicing the analyzing the data. associated portfolios. VI.INVESTMENT BANKING SERVICES a. Capital markets services - underwriting debt and equity, assist company deals, and restructure debt into structured finance products. b. Private banking - Private Banks provide banking services exclusively to high-net-worth individuals. c. Brokerage services - facilitating the buying and selling of financial securities between a buyer and a seller. IX.FINANCIAL SERVICES The following services are obtained by the user in the financial system. a. Bank cards. b. Credit card machine services and networks. c. Inter-mediation or advisory services. d. Private equity. e. Venture capital. f. Angel investmentt. g. Conglomerates. h. Financial market utilities. i. Debt resolution. X.THE NEED FOR CENTRALIZED INFRASTRUCTURE a. Decentralization is the early days of banking technology. This meant that each branch had its own server, banking applications, database, and other such assorted hardware/software. b. Decentralized networks had their own set of problems in terms of the cost and management fronts. VII.FOREIGN EXCHANGE SERVICES Foreign exchange services include: a. Currency exchange - where clients can purchase and sell foreign currency banknotes. b. Wire transfer - where clients can send funds to international banks abroad. c. Remittance - where client that are migrant workers send money back to their home country. VIII.INVESTMENT SERVICES a. Asset management - the term usually given to describe companies which run collective investment funds. c. There came the need for a centralized database. The database had to be updated instantaneously ISSN: Page 222

4 irrespective of the branch or channel the customer e. When one or two private sector banks showed that it used. can be done efficiently, other banks began to show d. Things changed when banks realized the cost an interest they also began consolidating their benefits of swapping the decentralized model to databases into a single large database. centralized data center architecture. Centralization using a data center has helped a lot in improving and simplifying the network from the operations, user, and administration perspectives. From a cost perspective, centralization has been very effective. f. It is not just the data center which contributed to centralization. The network has also evolved into a unified IP network. XI.MINING FOR INTELLIGENCE 1. Another important issue banks face is in proper analysis of financial data to identify business potential. 2. CRM backing with your data warehouse solution, streamlines the channels, but also tells you where to move. It tells you which customer to focus on. 3, A data warehouse can help the bank get a single view of its data across disparate systems. 4. Data warehousing solves these by integrating all the data into a common warehouse (usually an RDBMS). 5. Data mining can help you recognize patterns in the data you have. Data mining will be the cornerstone of the competitive if not the survival strategy for the next millennium in banking. Banks which ignore it are giving away their future to competitors which today are busy mining. a. Managing customers is one of the main issues that banks face in today's hyper competitive environment. b. Before banks go for a CRM solution, they need to ask themselves one question: How well do they know their customer? c. For that matter how many customers have moved in the past? Or how existing customers use various services that the bank provides. d. In banking, being the first to market alone is not enough since products can be copied very fast. ISSN: Page 223

5 e. This excerpt from Data Mining: Know It All includes examples that show how data mining algorithms and data sets work f. Retiring applications. ons_trends.htm g. Reducing data footprint and storage costs. [14]. h. Enhancing operational efficiency. fference_between_machine_learning_and_data_minin i. Implementing a structured data management policy. g2 19/1/1/7 XIII.CONCLUSION Data warehouses are costly and complex undertakings with the primary purpose of supporting the management. Development should be determined by the rules of efficient business, not the ambitions of technology personnel. Essential to success is the formulation of business goals and information needs, the quality of the input data and the proper use of the potential of modern technology. As banking competition becomes more and more global and intense, banks have to fight more creatively and proactively to gain or even maintain market shares. REFERENCES 1. ml 2. ml pro.techtarget.com/search_advantage_broad 9. d_data_manager_webcast Warehouse-From-Architecture-to-Implementation/ page 11. nopr.niscair.res.in/bitstream/ /11552/1/a LIS%2058(1)% pdf [16]. ments/apcity/unpan pdf 16/1/1/1 [15]. [17]. [18]. -MINING.aspx [19]. ata-mining-database.html [20]. [21]. [22]. ISSN: Page 224