@inproceedings{5d27e362293b4cbcbcdda1ce4204b2d5,
title = "LAPS: Computing Loan Default Risk from User Activity, Profile, and Recommendations",
abstract = "The credit score is one variable in receiving a loan application from a bank or financial institution that provides credit/loan. Many factors determine whether a borrower gets the loan. One of them is through more valuable collateral than the loan that was proposed. However, this is not possible for borrowers to provide it. Personal data, job information, salary amounts, assets owned, and valuable documents are usually required to determine a credit score. We build a personal lending platform model based on the trustworthiness score called LAPS (Loan Risk score, Activity score, Profile score, and Social Recommendation score) borrower trustworthiness score. The borrowers' trustworthiness is an absolute requirement to ensure they can repay the loans and installments on time. We present the practical ways to select the best features from the Bank Marketing dataset. The feature selection of the dataset applies to blockchain applications. The advantage of LAPS is introducing recommenders' as guarantors to convince the lenders'/investors' and minimizes collateral by implementing a LAPS.",
author = "Wisnu Uriawan and Omar Hasan and Youakim Badr and Lionel Brunie",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Blockchain Computing and Applications, BCCA 2022 ; Conference date: 05-09-2022 Through 07-09-2022",
year = "2022",
doi = "10.1109/BCCA55292.2022.9922034",
language = "English (US)",
series = "2022 4th International Conference on Blockchain Computing and Applications, BCCA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "167--172",
editor = "Mohammad Alsmirat and Moayad Aloqaily and Yaser Jararweh and Izzat Alsmadi",
booktitle = "2022 4th International Conference on Blockchain Computing and Applications, BCCA 2022",
address = "United States",
}