Normal view MARC view ISBD view

Bio-inspired credit risk analysis: computational intelligence with support vector machines

By: Yu, Lean.
Publisher: Berlin Springer 2008Description: xvi, 244 p.ISBN: 9783540778028.Subject(s): Credit --Management | Risk management | Biologically-inspired computing | Support vector machinesDDC classification: 332.7 Summary: Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties. (Source: www.alibris.com)
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Item location Call number Status Date due Barcode
Books Vikram Sarabhai Library
Slot 734 (0 Floor, West Wing) 332.7 Y8B4 (Browse shelf) Available 169438

Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties. (Source: www.alibris.com)

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha