452 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 25 Novita Ikasari Curtin University, Australia & University of Indonesia, Indonesia Fedja Hadzic Curtin University, Australia Tharam S. Dillon Curtin University, Australia Incorporating Qualitative Information for Credit Risk Assessment through Frequent Subtree Mining for XML ABSTRACT Credit risk assessment has been one of the most appealing topics in banking and fnance studies, at- tracting both scholars’ and practitioners’ attention for some time. Following the success of the Grameen Bank, works on credit risk, in particular for Small Medium Enterprises (SMEs), have become essential. The distinctive character of SMEs requires a method that takes into account quantitative and qualitative information for loan granting decision purposes. In this chapter, we frst provide a survey of existing credit risk assessment methods, which shows a current gap in the existing research in regards to taking qualitative information into account during the data mining process. To address this shortcoming, we propose a framework that utilizes an XML-based template to capture both qualitative and quantitative information in this domain. By representing this information in a domain-oriented way, the potential knowledge that can be discovered for evidence-based decision support will be maximized. An XML document can be efectively represented as a rooted ordered labelled tree and a number of tree mining methods exist that enable the efcient discovery of associations among tree-structured data objects, tak- ing both the content and structure into account. The guidelines for correct and efective application of such methods are provided in order to gain detailed insight into the information governing the decision making process. We have obtained a number of textual reports from the banks regarding the information DOI: 10.4018/978-1-4666-3886-0.ch025