Credit Usage Prediction in the Banking Sector Using Big Data Processing and Analysis Techniques: A Case Study

EasyChair Preprint 4458

Abstract

Within the scope of this research, a methodology is proposed for estimating the loan needs of customers in the banking field using machine learning models on big data processing and analysis platforms. Prototype application of the proposed methodology has been designed, developed and applied on the credit usage behavior data of banking customers. Performance tests were carried out on the prototype application developed for the estimation success of the method, its scalability, and the working time metrics required for training in creating the models. The results obtained reveal the usability of the proposed method in the banking sector.

Keyphrases: Bankacılık Sektörü, büyük veri analizi, kredi kullanım tahmini, kredi satın alma eğiliminin modellenmesi

@booklet{EasyChair:4458,
  author    = {Ümit Tigrak and Nail Taşgetiren and Erdal Bozan and Güven Gül and Emir Demirci and Hakan Sarıbıyık and Mehmet S. Aktaş},
  title     = {Credit Usage Prediction in the Banking Sector Using Big Data Processing and Analysis Techniques: A Case Study},
  howpublished = {EasyChair Preprint 4458},
  year      = {EasyChair, 2020}}