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# Credit Scoring Model
- URL: https://fsc.stevens.edu/credit-scoring-model/
- Published: 2019-10-28T16:31:42.000Z
- Updated: 2026-05-16T22:41:29.000Z
- Author: Kajol
- Tags: FE 800 Projects, 2015 Fall, Research, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14

### **Abstract**

The major objective of this project is to develop a credit scoring model with reliable accuracy and competitive speed. We build the credit scoring system based on neural network model, and compare it with other methods including traditional linear regression and logistic regression. In addition, we also test the ability to optimize performance of two feature selection models.

![](https://fsc.stevens.edu/content/images/2019/10/asa.JPG)

### **Researchers**:

****Research Group (2015 Fall)*:**  
 Yang Liu, Master in Financial Engineering, Graduated in Jan 2016  
 Yang Li, Master in Financial Engineering, Graduated in Jan 2016  
 Haoshuo Zhang, Master in Financial Engineering, Graduated in Jan 2016  

****Advisor*:**  
[Dr. Rupak Chatterjee](https://web.stevens.edu/hfslwiki/index.php?title=Rupak%5FChatterjee&ref=fsc.stevens.edu)  
[Dr. Ionut Florescu](https://web.stevens.edu/hfslwiki/index.php?title=Ionut%5FFlorescu&ref=fsc.stevens.edu)

### **Research Topics:**

Neural Network, Feature Selection, Logistic Regression, Credit Scoring System

### **Main Results:**

To improve the accuracy, we apply the feature selection technique, which improves our accuracy by 10%. Our model allows us to forecast a rough credit rating situation (within one rating level error) with a confidence level of 95%, and an accurate credit rating with a confidence level around 65% to 70%.

![](https://fsc.stevens.edu/content/images/2019/10/dsdkkd.JPG)

### **Conclusions**

At the end of the project, we are able to predict the credit rating of any firm with public financial data using the neural network model we built. A high confidence level is achieved when we only want a rough estimation of the credit rating situation. To sum up, our model works.