Data Mining Problems

Capital One uses IT through its information-based strategy (IBS) to “record, organize, and analyze data on the characteristics and behaviors of their customers,” as stated by CEO Richard Fairbank. Their philosophy was to exploit information by constructing scientific models that could be used to both assess the creditworthiness of potential cardholders through FICO scoring, and to customize product offerings for existing ones. This was done through data mining, sorting, customizing offers and marketing campaigns, and then analyzing this data to see what campaigns worked – for what reason and what

( Experian Information Solutions, Inc., 2005). On the other hand the information being reviewed can help give the employer a general idea regarding the personality of the applicant for example if they are one who do or do not take care of their personal responsibilities by paying their bills and paying the bills when they are due. Also some of the information on the credit report can prove if the applicant has too many financial obligations, that will possibly disturb their job responsibilities and they do not want to risk hiring an applicant whom might steal from their company. ( Experian Information Solutions, Inc., 2005). Another worry for some businesses is they do not want to hire an applicant who cannot handle their personal obligations. (Rosen, 2000).

STATS 415 Data Mining Project Insights into the prediction of the default payment through the history of payments, the amount of previous payment and the amount of bill payment

A retail store gives each of its customers a maximum amount of credit. A customer’s available credit is determined by subtracting the amount of credit used by the customer from the customer’s maximum amount of credit. As you did in programming challenge 1, perform steps 1 through 6 of the programming process to design an application that determines a customer’s available credit.

These provide key information about the candidate in structured and consistent manner that is needed for a specific position. It speeds up the initial screening process. They can also form the basic starting point of the personnel record and are generally very easy to use and record.

It should make it easy for the customer to move through the store and obtain the desired merchandise with a minimum of confusion, congestion and delay. 2. It should minimize labor needed in arranging merchandise through the store while satisfying the store objectives. 3. It should assure maximum exposure and sales for all merchandise carried by the product arrangement on the shelf. 4. It should provide space and an arrangement of the departments consistent with the desired image goals of the

So it is also attractive for Jules Kroll to take this opportunity to enter the credit rating industry. In addition, the “issuer pays” model, which used by the big three rating agencies, lets companies shop around for the best ratings, putting pressure on the agencies to inflate their grades. As a result, it is very difficult to argue that they can adequately represent the users’ side. Therefore, Jules Kroll wants to use another model that can assign unbiased and reliable ratings.

When Equifax’s unit was spun off as a publicly traded company named ChoicePoint, their initial focus is to expand the company beyond credit-reporting to data brokerage, another intention was to provide the business an escape from the laws that restricted the type and amount of information a credit agency can sell. The company gathered data, assembled it into proprietary databases, and sold products that allowed clients to assess risk and to detect fraud. ChoicePoint acquired various companies that added data and data capabilities to ChoicePoint’s existing database. These capabilities range from data sharing within multiple databases, to creating electronic maps, to biometrics. With an expanded set of data, ChoicePoint became a frontier in data

Lending evaluations by Santander are based on credit background of the person (or company) who wants borrow. Through the development of a credit-approval system, Santander Consumer Finance increased an understanding into these clients on an online database which allowed for the use of real time analysis in determining interest rates for the business interactions.

Question 1: Assume a base cuboid of 10 dimensions contains only three base cells: (1) (a1, b2, c3, d4; ..., d9, d10), (2) (a1, c2, b3, d4, ..., d9, d10), and (3) (b1, c2, b3, d4, ..., d9, d10), where a_i != b_i, b_i != c_i, etc. The measure of the cube is count. 1, How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2, How many nonempty aggregate (i.e., non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3, which yields the result. 3, How many

On the other hand, my interest in the incorporation of artificial intelligence and machine learning by financial institutions stems from my work as a Credit Risk Analyst for BECU. I recently joined the institution, and am working on a year-long project to integrate the Synergy Component System which will enable increased automation, improved attribution management, and reduced decision times for credit card and auto loans. Beyond testing plans and cases, I am most excited about designing decision matrices by analyzing member data such as credit quality, debt-to-income ratio, membership years, and trade lines to reach a credit decision. While I have had already experienced academic research, this will be my first taste of applied industry research.

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

Rule induction is one of the major forms of data mining and is perhaps the most common form of knowledge discovery in unsupervised learning systems. It is also perhaps the form of data mining that most closely resembles the process that most people think about when they think about data mining, namely “mining” for gold through a vast database. The gold in this case would be a rule that is interesting - that tells you something about your database that you didn’t already know and probably weren’t able to explicitly articulate (aside from saying “show me things that are interesting”). The bane of rule induction systems is also its strength - that it retrieves all possible interesting patterns in the database. This is strength in the sense that it leaves no stone unturned but it can also be viewed as a weakness because the user can easily become overwhelmed with such a large number of rules that it is difficult to look through all of them. You almost need a second pass of data mining to go through the list of interesting rules that have been generated by the rule induction system in the first place in order to find the most valuable gold nugget amongst them all. This overabundance of patterns can also be problematic for the simple task of prediction because all possible patterns are culled from the database there may be conflicting predictions made by equally interesting rules. Automating the process of culling the most interesting rules and of combing the recommendations

According to statistic shown above, the employees are more familiar with the products layout, most of them could find the required items in a pretty short time, and they are highly responsible to help the customers. Most customers will ask employees for help when they cannot find the products they intend to buy. From most (63%) of their opinions, the bestselling products should be placed the most obvious position of the shelves. But still, there are a small part (13%) of employees are unfamiliar with the shelve order in the store.

Market Basket Analysis, also known as affinity analysis, is a technique to identify items likely to be purchased together. Market basket analysis is only one of the various possible functions that data mining tools can perform. This type of analysis connects to the findings of the retail sales data to allow the user to draw conclusions as to the relations between the products that are irrelevant at the first sight. Implementations of such decisions can lead to a major rise in sales and profits for the retailers. This can be done, for example by laying four products that are related in the nearly-located aisles, or by putting special offers for, let us say, soap on then packs of diapers. For instance, if you are in an