A Survey: Credit Sentiment Score Prediction
A. N. M. Sajedul Alam, Junaid Bin Kibria, Arnob Kumar Dey, Zawad Alam, Shifat Zaman, Motahar Mahtab, Mohammed Julfikar Ali Mahbub, Annajiat Alim Rasel
arXiv:2209.15293v1h-index: 10
Originality Synthesis-oriented
AI Analysis
This is an incremental survey for banks and NGOs seeking to automate and enhance credit rating forecasts.
The paper surveys existing sentiment analysis techniques applied to predict creditworthiness, aiming to improve loan approval processes that are currently manual and error-prone.
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.