Business Intelligence with Data Mining
Business Intelligence with Data Mining
Abstract Banking and finance institutions are growing very fast in this globalization era. Mergers, acquisitions, globalization have made these institutions bigger. No doubt, the data also grow real huge and more varied. Big data storage such as data warehouse and data marts are provided to give a solution on big data storage. On the other sides, those data are needed to be analyzed. Business intelligence finally comes in as a solution in analyzing those huge data. Business intelligence especially with data mining can create a solution in further decision making. With various tools and techniques, data mining has been proven in many aspects of business. Hidden informations that stored inside either data warehouse or data marts can be gained easily. In example, those hidden informations are market and economy trens, competitor trends, competitive price, good products and services and also can provide better customer relationship management. There is still one benefit in business intelligence with data mining that this paper will focus on, i.e. risk management and frauds and losses prevention. One of product from banking and finance institutions is credit loans. It is really a high risk business, but with business intelligence with data mining especially classification and clustering techniques, it can be maintained and implemented safely and of course with low risks, minimized frauds and losses and increased profits and revenues.
Keywords : Banking and Finance, Business Intelligence, Data Mining, Risk Management, Credit Loans
Introduction Banking and Finance institutions are growing rapidly nowadays. For one institution, there are more than one offices or branches in one country or even in different country. Globalization, mergers, acquisitions, competitions, market changes are some of the reasons behind why are they growing fast. As those banking and finance institutions grow, so do the data. In this case,
Abstract Banking and finance institutions are growing very fast in this globalization era. Mergers, acquisitions, globalization have made these institutions bigger. No doubt, the data also grow real huge and more varied. Big data storage such as data warehouse and data marts are provided to give a solution on big data storage. On the other sides, those data are needed to be analyzed. Business intelligence finally comes in as a solution in analyzing those huge data. Business intelligence especially with data mining can create a solution in further decision making. With various tools and techniques, data mining has been proven in many aspects of business. Hidden informations that stored inside either data warehouse or data marts can be gained easily. In example, those hidden informations are market and economy trens, competitor trends, competitive price, good products and services and also can provide better customer relationship management. There is still one benefit in business intelligence with data mining that this paper will focus on, i.e. risk management and frauds and losses prevention. One of product from banking and finance institutions is credit loans. It is really a high risk business, but with business intelligence with data mining especially classification and clustering techniques, it can be maintained and implemented safely and of course with low risks, minimized frauds and losses and increased profits and revenues.
Keywords : Banking and Finance, Business Intelligence, Data Mining, Risk Management, Credit Loans
Introduction Banking and Finance institutions are growing rapidly nowadays. For one institution, there are more than one offices or branches in one country or even in different country. Globalization, mergers, acquisitions, competitions, market changes are some of the reasons behind why are they growing fast. As those banking and finance institutions grow, so do the data. In this case,
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). 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.