O P T I M I Z I N G C A P I TA L A L L O C AT I O N F O R

M O RT G A G E M A R K E T L O A N S

N A M A N J A I N

G I T H U B . C O M / N A M A N J / M O R T G A G E - M A R K E T-T R I - A N A LY S I S

LOAN DEFAULT CLASSIFIER

LOCATION RECOMMENDER

FORECASTING BUSINESS

• Built a classifier that predicts whether a customer is going to miss their monthly loan

repayment

• Major Challenge - Classes were very imbalanced, with only 3% people ever defaulting on a payment

• Raw Data:• ~15k data points of 40 dimensions• 20 variables were categorical, 19 numerical and 1 temporal

LOAN DEFAULT CLASSIFIER:

• AdaBoost

• No Oversampling

• Used sample weights

Approach:

Results:• Recall 98%

• Recommend new office locations that maximize growth opportunity

• Parameters Used:• Distance from existing office locations• Profitability of existing location over the past 5 years• GDP growth of potential locations over the past 5 years

LOCATION RECOMMENDER:

Approach:• Scipy Optimize Basin Hopping

• Basemap

The model automatically strikes a balance in the cost function between clustering of office locations vs spreading them out

FORECASTING BUSINESS:• Predict amount of business over the next quarter so that the firm can

better manage its resources

• Challenge - Data had a one-time event in the middle of 2016

Approach:• SARIMAX

NEXT STEPS• Loan Default Classifier :

– Extend model to allow a particular branch to determine the risk of loan default per portfolio

• Location Recommender :– Extend Cost Function to determine the tradeoff between

clustering of office locations vs having a larger spread– Make Cost Function less sensitive to initializations

• Forecasting Business :– Incorporate Exponential Smoothing (ETS) in the forecasting

THANK YOU

QUESTIONS?

Naman.jain020@gmail.comNamanJ/Mortgage-Market-Tri-AnalysisNamanJain1