Alfat, L; Rizkinia, M; Sari, RF; Romano, DM; (2020) Feature Selection of Credit Score Factor Based on Smartphone Usage using MCFS. In: Proceedings of the 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON). IEEE: Bangkok, Thailand.

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Abstract

Credit as a part of our consumptive life has helped a lot of people. As a financial product, it is used widely along with the growth of economic and financial services. Therefore, credit is very risky so that motivating the financial institution to use a system called credit scoring to make a decision about acceptance. However, the conventional credit scores is calculated from the user's financial history in financial institution. This method causes people without any financial history or account, such as students, being unnoticed by the system, then their credit proposal is declined. This phenomenon insists the researchers thinking about a new credit scoring system that facilitates people from different economic background. Knowing that people nowadays will spend any money and time on their smartphone, we make a hypothesis about how smartphone usage behavior can be the answer. Then, the survey is conducted to 90 respondents from low to high economic background to model their credit limit. This paper shows that smartphone usage has some insights that can be computed through Multi-Cluster Feature Selection (MCFS). The selected features are Brand of phone, Frequency of changing the phone, Occupation, Data usage for game, Cost for phone, Data usage for social media, Reason of changing the phone, Money for data, Protect personal interest, Age, Spend last money, and Pay for you.

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