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A novel method for credit scoring based on feature transformation and ensemble model.

ABSTRACT: Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

SUBMITTER: Li H

PROVIDER: S-EPMC8189024 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A novel method for credit scoring based on feature transformation and ensemble model.

Li Hongxiang H Feng Ao A Lin Bin B Su Houcheng H Liu Zixi Z Duan Xuliang X Pu Haibo H Wang Yifei Y

PeerJ. Computer science 20210604

Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance ...[more]

PMID: 34151000

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