Alternative Credit Scoring and Classification Employing Machine Learning Techniques on a Big Data Platform

Hindistan, Yavuz Selim; Aiyakogu, Burhan Aasin; Rezaeinazhad, Arash Mohammadian; Korkmaz, Halil Ergun; Dağ, Hasan

doi:10.1109/ubmk.2019.8907113

Cited by 5 publications

(1 citation statement)

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“…Through the utilization of Logistic Regression, while combining a variety of information sources such as consumer purchasing habits, social media activity, and geography details with conventional credit scoring methodologies yields fresh insights for appropriate and more accurate credit scoring (Hindistan et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Scoring Active Telegram Channels Offering Stock Signals

Sohrabi

Jafarabad

Orfi

2022

IJFIFSA

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The impact of personal judgment on the assessment of an individual's financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant's total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as aThe methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.

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Section: Literature Reviewmentioning

confidence: 99%