عنوان

An Efficient Technique for Mining Bad Credit Accounts from Both OLAP and OLTP

Number

TL49882

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

An Efficient Technique for Mining Bad Credit Accounts from Both OLAP and OLTP

General Material Designation

[Thesis]

First Statement of Responsibility

Sheikh Rabiul Islam

Subsequent Statement of Responsibility

Ghafoor, Sheikh; Eberle, William

Name of Publisher, Distributor, etc.

Tennessee Technological University

Date of Publication, Distribution, etc.

2018

Specific Material Designation and Extent of Item

84

Text of Note

Committee members: Talbert, Doug

Text of Note

Place of publication: United States, Ann Arbor; ISBN=978-0-355-94059-6

Dissertation or thesis details and type of degree

M.S.

Discipline of degree

Computer Science

Body granting the degree

Tennessee Technological University

Text preceding or following the note

2018

Text of Note

Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as 'good' when it is actually bad, could lead to loss of revenue - and marking an account as 'bad' when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification. In addition, our approach is efficient in terms of computation time and resources requirement because no transaction is processed more than once for the risk factor calculation. Another factor that makes our approach efficient is the early detection of bad accounts or fraud attempts as soon as the transaction takes place, which leads to a decrease in lost revenue.

Banking; Computer science

Subject Term

Applied sciences;Social sciences;Bankruptcy;Data warehouse;Default;OLAP;OLTP

Fraser Abdur Rahim, Herbert Muhammad

Ghafoor, Sheikh; Eberle, William

Subdivision

Computer Science

Tennessee Technological University

Call Number

2039605712; 10787567

Electronic name

p

[Thesis]

276903

a

Y