Development of a machine learning-based system for credit risk assessment
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Motivation<br> 1.3 Objectives<br>2. Literature Review<br> 2.1 Credit risk assessment methods<br> 2.2 Machine learning applications in finance<br>3. Data Collection and Preprocessing<br> 3.1 Data sources<br> 3.2 Feature selection and engineering<br>4. Machine Learning Model Development<br> 4.1 Model selection<br> 4.2 Training and validation<br>5. System Implementation<br> 5.1 Software architecture<br> 5.2 User interface design<br></p>
Thesis Abstract
<p> The project aims to develop a machine learning-based system for credit risk assessment in the financial industry. The system will utilize historical financial data, customer information, and macroeconomic indicators to predict the creditworthiness of loan applicants. By leveraging advanced machine learning algorithms, the system will provide accurate risk assessments, thereby enabling financial institutions to make informed lending decisions and mitigate potential credit losses. <br></p>Thesis Overview
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