PREDICTIVE ANALYTICS FOR CREDIT RISK MANAGEMENT AT IDFC FIRST BANK
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901121556021248 |
|---|---|
| author | Journal of Management Excellence |
| author_facet | Journal of Management Excellence |
| contents | <p><span>The purpose of this research is to look into how IDFC FIRST Bank uses predictive analytics to improve credit risk management. This study looks into how data-driven models might be able to improve the accuracy of loan approval decisions and predict when borrowers will not pay back their loans. By looking at past customer records, financial trends, and spending habits, the study shows how machine learning can be used to improve risk assessment. It shows how important it is to combine structured data, like account amounts, with unstructured data, like transaction notes, in order to make credit checks more accurate. The study also looks into how well these models work, how accurate their predictions are, and how well they can lower non-performing assets (NPAs). It's clear that automating credit checks not only makes operations more efficient but also speeds up the process. The results show that predictive analytics helps the bank make smarter decisions about loans and take security steps to avoid possible problems. </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19396943 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PREDICTIVE ANALYTICS FOR CREDIT RISK MANAGEMENT AT IDFC FIRST BANK Journal of Management Excellence Predictive Analytics Credit Risk Management Machine Learning Credit Scoring Loan Default Prediction Non-Performing Assets (NPAs) <p><span>The purpose of this research is to look into how IDFC FIRST Bank uses predictive analytics to improve credit risk management. This study looks into how data-driven models might be able to improve the accuracy of loan approval decisions and predict when borrowers will not pay back their loans. By looking at past customer records, financial trends, and spending habits, the study shows how machine learning can be used to improve risk assessment. It shows how important it is to combine structured data, like account amounts, with unstructured data, like transaction notes, in order to make credit checks more accurate. The study also looks into how well these models work, how accurate their predictions are, and how well they can lower non-performing assets (NPAs). It's clear that automating credit checks not only makes operations more efficient but also speeds up the process. The results show that predictive analytics helps the bank make smarter decisions about loans and take security steps to avoid possible problems. </span></p> |
| title | PREDICTIVE ANALYTICS FOR CREDIT RISK MANAGEMENT AT IDFC FIRST BANK |
| topic | Predictive Analytics Credit Risk Management Machine Learning Credit Scoring Loan Default Prediction Non-Performing Assets (NPAs) |
| url | https://doi.org/10.5281/zenodo.19396943 |