Transcript slides

Predicting Mortgage Pre-payment
Risk
Introduction
Definition
Borrower pays off the loan before the contracted
term loan length.
• Lender loses future part of the income stream
associated with the loan.
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Inspiration & Previous work
Oded Netzer’s talk on text-mining techniques for
business applications – loan default prediction.
Breakdown of the Problem
Target

To pin-point among borrower profiles, who all likely
to prepay the mortgage.
Data
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UCI Machine Learning Repository :
Credit Approval Dataset – 690 observations; 15
attributes.
German Credit Dataset – 1000 observations, 20
attributes.
Give Me Some Credit - Kaggle credit-scoring
competition - very large.
Prospective Solutions
Techniques for categorization of data
Decision Tree - recursively separates observations in
branches to construct a tree to improve prediction
accuracy, with use of measurements like information
gain ratio, Gini index etc.
 Naïve Bayes Classifier - calculates a set of
probabilities by counting the frequency and
combinations of values in the dataset.
 Logistic Regression - measures the relationship
between the one dependent binary variable and one
or more independent variables. by estimating
probabilities using a logistic function.Appropriate
when the dependent variable is binary.

Evaluation of Results
Measure of success
How clear the proposed categorization scheme is
proposed.
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How applicable the scheme is in enabling lenders in
managing prepayment risk by providing a useful
structure for early mitigation targeting.
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