Abstract
Home Loan plays a pivotal role in today’s age
when one steps into purchasing their home. It has been
witnessed that in many cases users are unable to pay the after
taking the loan and thus the loan is slipped to NPA (NonPerforming Asset) from Standard Asset for the bank or any
lending institution. The revenue generation is ceased. As the
housing loan is taken against property the lenders have right to
sell the property and close the dues, but the process is lengthy
as judicial procedures are involved. In most cases, the property
value is much less than the calculated loan amount (Principal +
Interest).
In this study we examined the several ML methods to
identify the loan default before disbursing the loan to the
applicant. This matter has been studied widely and used the
predictive analytics to find out the relationship between
attributes and the target variable. Predictive Analytics enables
us to feed optimal set of features to the ML models. The study
started with 122 attributes and ended up with around 30% of
features as the ideal subset for housing loan default prediction.
Then, five ML models were fit into the dataset and the
champion model came up with roc score 0.94, Recall 0.90 and
Precision 0.94. LIME and SHAP were applied on the champion
model along with the dataset for global and local
interpretability. The experimental procedure concluded that
ML models along with predictive analytics can arrest the loan
disbursal to the ineligible applicants and will also provide the
insight of such prediction with the help of model
interpretability.
when one steps into purchasing their home. It has been
witnessed that in many cases users are unable to pay the after
taking the loan and thus the loan is slipped to NPA (NonPerforming Asset) from Standard Asset for the bank or any
lending institution. The revenue generation is ceased. As the
housing loan is taken against property the lenders have right to
sell the property and close the dues, but the process is lengthy
as judicial procedures are involved. In most cases, the property
value is much less than the calculated loan amount (Principal +
Interest).
In this study we examined the several ML methods to
identify the loan default before disbursing the loan to the
applicant. This matter has been studied widely and used the
predictive analytics to find out the relationship between
attributes and the target variable. Predictive Analytics enables
us to feed optimal set of features to the ML models. The study
started with 122 attributes and ended up with around 30% of
features as the ideal subset for housing loan default prediction.
Then, five ML models were fit into the dataset and the
champion model came up with roc score 0.94, Recall 0.90 and
Precision 0.94. LIME and SHAP were applied on the champion
model along with the dataset for global and local
interpretability. The experimental procedure concluded that
ML models along with predictive analytics can arrest the loan
disbursal to the ineligible applicants and will also provide the
insight of such prediction with the help of model
interpretability.
| Original language | English |
|---|---|
| Title of host publication | 2023 15th International Conference on Developments in eSystems Engineering (DeSE) |
| Editors | Dhiya Al-Jumelly, Header Abed Dhahad, Manoj Jayabalan, Jade Hind, Jamila Mustafina, Sulaf Assi, Abir Hussain, Hissam Tawfik |
| Publisher | IEEE |
| Pages | 492-497 |
| ISBN (Electronic) | 9798350335149 |
| ISBN (Print) | 9798350335156 |
| DOIs | |
| Publication status | Published - 17 Apr 2023 |
| Externally published | Yes |
| Event | 2023 15th International Conference on Developments in eSystems Engineering - Baghdad, Iraq Duration: 9 Jan 2023 → 12 Jan 2023 Conference number: 15 |
Conference
| Conference | 2023 15th International Conference on Developments in eSystems Engineering |
|---|---|
| Abbreviated title | DESE |
| Country/Territory | Iraq |
| City | Baghdad |
| Period | 9/01/23 → 12/01/23 |