Abstract
Credit scores hold significant importance for the people to avail credit from the banking and fin-tech companies. With the increasing trends of using the contemporary model of credit history evaluation, there is a need for a more comprehensive solution that can take in to account the peer trust factors too. In this manuscript, the emphasis is on understanding the existing set of machines learning-based credit scoring systems and to propose a “Peer Level Credit Rating” system that can support in a more comprehensive solution. The proposed framework upon testing in conjunction with some of the existing credit evaluation solutions, the accuracy and the structure of the credit scoring system can be more strengthened. The key benefits of the proposed framework are responsibility and accountability tagged to the trust score from the endorsers and to develop a sustainable scoring pattern for managing the credit scores of individuals.
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Rudra Kumar, M., Gunjan, V.K. (2022). Peer Level Credit Rating: An Extended Plugin for Credit Scoring Framework. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-16-7985-8_128
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DOI: https://doi.org/10.1007/978-981-16-7985-8_128
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