A Survey: Credit Sentiment Score Prediction

TL;DR

This paper reviews current sentiment analysis techniques used in creditworthiness prediction, highlighting how machine learning enhances credit rating forecasts and addressing the limitations of manual approval processes.

Contribution

It provides a comprehensive survey of sentiment analysis methods applied to credit scoring, emphasizing recent advancements and their potential to improve credit decision automation.

Findings

Sentiment analysis techniques can improve creditworthiness prediction accuracy.

Machine learning methods are increasingly used in credit scoring.

Sentiment analysis helps automate and streamline credit approval processes.

Abstract

Manual approvals are still used by banks and other NGOs to approve loans. It takes time and is prone to mistakes because it is controlled by a bank employee. Several fields of machine learning mining technologies have been utilized to enhance various areas of credit rating forecast. A major goal of this research is to look at current sentiment analysis techniques that are being used to generate creditworthiness.