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Exploring Input Enhancements Big Dat...

Nguyen, Tuan Duc.

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Exploring Input Enhancements Big Data Analysts Need to Improve a Credit Qualification Model to Support Large Banks in Their Risk Management Operations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exploring Input Enhancements Big Data Analysts Need to Improve a Credit Qualification Model to Support Large Banks in Their Risk Management Operations./
作者:	Nguyen, Tuan Duc.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	148 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-10, Section: A.
Contained By:	Dissertations Abstracts International81-10A.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27830367
ISBN:	9781658497923

Exploring Input Enhancements Big Data Analysts Need to Improve a Credit Qualification Model to Support Large Banks in Their Risk Management Operations.
Nguyen, Tuan Duc.

Exploring Input Enhancements Big Data Analysts Need to Improve a Credit Qualification Model to Support Large Banks in Their Risk Management Operations. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 148 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: A.

Thesis (D.C.S.)--Colorado Technical University, 2020.

This item must not be sold to any third party vendors.

This study explored the use of an artificial neural network (ANN) called iQual to improve a credit qualification model to support large banks in their risk management operations. The research leveraged the Design Science framework to design and evaluate a web-based Information Technology artifact, named iQual, to predict the default probability for a list of credit borrowers. A focus group of five participants included senior data technical experts and financial institutions' directors, in the Washington DC Metro areas, had been selected prior to watch a live demonstration of the iQual tool in action, and provide the expert feedback on the artifact. The research followed the framework for concept proof, artifact construct, and artifact enhancing of the Artificial Neural Network (ANN) machine learning prototype of the iQual credit qualification application via the Web. The research method included semi-structured interviews, each consisting of 7 open-ended questions, responded by 5 expert reviewers with technical expertise and trade experience from a financial industry. The compiled list from the expert reviewers' feedback, recorded through transcription, was then organized into themes of enhanced features. The enhanced features from the iQual dashboard tool were recognized by the reviewers as follows: a) data load module, b) applicant summary view module, c) set credit product qualification standards, d) predict execution, and e) assess accuracy of prediction. The data analysis of the expert reviewers' transcription of interviews also indicated that additional elements as discussed below need to be addressed or improved for real-life application to the banking industries such as a) quality control, b) better logs, c) different loan options, d) interest rate calculation, and e) management of users.

ISBN: 9781658497923Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Artificial neural network

Exploring Input Enhancements Big Data Analysts Need to Improve a Credit Qualification Model to Support Large Banks in Their Risk Management Operations.
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