DocumentCode :
3129158
Title :
Dynamic Loan Service Monitoring Using Segmented Hidden Markov Models
Author :
Lee, Haengju ; Gnanasambandam, Nathan ; Minhas, Raj ; Zhao, Shi
Author_Institution :
Xerox Res. Center Webster, Webster, NY, USA
fYear :
2011
fDate :
11-11 Dec. 2011
Firstpage :
749
Lastpage :
754
Abstract :
We describe how to apply Hidden Markov Model (HMM) to automate the loan service monitoring process. To predict the probability of defaulting in the near future, we build a statistical model of HMM from borrowers´ historical payment data. The predicted probability is dynamic in a sense that the probability keeps changing as new realized data is added to the current historical data. The time series sequence data is obtained from the composite information of the loan status and days delinquent on each month. In the training stage, various HMMs are trained: one is paid HMM and the others are defaulted HMMs. We show that more accurate monitoring can be achieved by segmenting the defaulted data and training them separately (i.e., segmented HMM method) than by training a single defaulted HMM (i.e., simple HMM method). In the prediction stage, for each active loan, we apply the following two steps: 1) classification of the loan and 2) calculation of the default probability over a prospective time period. Finally, the monitoring system sends a signal if the probability is greater than a pre-specified threshold. We also explore how to select the optimal threshold level using precision and recall analysis.
Keywords :
financial data processing; hidden Markov models; probability; time series; HMM; borrower historical payment data; default probability prediction; dynamic loan service monitoring process; loan classification; optimal threshold level; prediction stage; recall analysis; segmented hidden Markov model; statistical model; time series sequence data; training stage; Data models; Hidden Markov models; History; Monitoring; Probability; Testing; Training; Hidden Markov Model; default monitoring system; loan default prediction; sequences and sequential data analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4673-0005-6
Type :
conf
DOI :
10.1109/ICDMW.2011.71
Filename :
6137455
Link To Document :
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