Data Mining in Banking Industry

To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the

2. What considerations should a corporate executive in industries such as banking, health care, or higher education investigate when deciding on implementing a data-mining program?

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

Data mining is a class of database applications that looks for hidden patterns in a group of data that can be

Data collected by a business includes internal data, such as financial or operational information, as well as external data, such as customer or website usage information. Properly analyzing and acting on this vast amount of data can transform the way companies do business and can become their biggest competitive advantage. Leaders of the organization no longer have to rely on their “gut instincts” to make key decisions, instead they will make decisions off historic data and will be able to more easily measure and track the effectiveness of those decisions.

"A bank wants to know more about its customers, in particular the types of people who might want a loan from the bank. This could be extremely useful information, because the bank could make big savings from using a tightly targeted marketing campaign, compared to one that tries to communicate with the general public...Using test data, algorithms are used to detect the characteristics of customers who took out a particular type of loan. These customers are differentiated from those who used other kinds of financial services, including other types of loan. The algorithm will be able to develop 'rules' by which it can identify customers who are likely to be good prospects for the loan. These rules are then applied to the remainder of the database. The final computing task may be to sort the whole databank into clusters; groups of customers who share similar characteristics. It is at this stage that interesting and perhaps surprising patterns might be detected. These patterns then have to be interpreted by the data miners and banking staff." [4]

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

My job in the personal loan center was to assist in maintaining the database and generating evaluations. One project which I was involved in was using personal credit scoring model to make a risk forecast and detect bank fraud based on loan documentation in the database. During the project, I realized that rather than just about complicated methods, statistics was about simplifying and making sense. Sufficient data and in-depth analysis can not only help companies make valuable decisions, but also enable us to understand real life in a more direct and precise way. As the trend of information explosion is inevitable, I am sure that the power of data will become increasingly overwhelming, and the role of data analysis will be indispensable. Therefore, along with my goal to pursue graduate studies in statistics, my desire to pursue a career in the field of data analysis was

Exploiting the customer information hidden in large database can help identify valuable customers and predict future behavior, enable the company to become proactive with their campaign, implement knowledge-driven decisions and make it possible for the organization to limit the defect rate.

Since higher education has blurred the lines with traditional businesses, it is important to have the tools to assist them with valuable data and information, in making decisions. Using of data and having the right data mining tools can insure the institute’s success, in many forms, such as, identifying market trends, precision marketing, new products, performance management, grants and funding management, student life cycle management and procurement to mention a few. To get a better grasp on these benefits it’s important to understand data warehouse, data mining and the associated benefits.

The company deals in fast moving consumer products and requires an analytics system that helps it serve the needs of its stakeholders more effectively. It has three major rivals in the industry and seeks to streamline its supply chain and inbound inventory processes to improve its cash flows. This implies that the ability of its employees to gather and act on different sources of data will allow them a chance to perform their responsibilities in a timely fashion. Thus, by becoming more conversant with various trends in the market, the organization will be in a position to predict future performance in its key business units. Moreover, modern firms