- author = "Mengnan. Song and Jiasong. Wang and Tongtong. Zhang and Guoguang. Zhang and Ruijun. Zhang and Suisui. Su",
- title = "Effective Automated Feature Derivation via Reinforcement Learning for Microcredit Default Prediction",
- booktitle = "2020 International Joint Conference on Neural Networks (IJCNN)",
- year = "2020",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Credit default, Feature derivation, Feature interpretation, Reinforcement learning",
- ISSN = "2161-4407",
-
DOI = "
doi:10.1109/IJCNN48605.2020.9207410",
- size = "8 pages",
-
abstract = "Microcredit is a new financial instrument serving the
segment of population that typically lack collateral
and are highly likely to be rejected by traditional
financial institutions, by lending very small loans.
For platforms that participate in such consumer finance
activities, the key challenge lies in risk management
and the popular credit scoring method predicting
whether a borrower would default or not takes an
important role in this field. However, the fact is that
we are often facing mass of raw data and the
traditional credit scoring is heavily depending on
feature engineering involving domain expert knowledge,
intuition and trial and error, which is often time
consuming.
It is very challenging to derive effective features from raw data as the searching space can be very large with noninformative features. In this paper, we propose a new performance-driven framework automated generating discriminating features from raw data via reinforcement learning to help improve the default prediction of the downstream classifier which may be a logistic regression or boosting tree. Specially, we first define a formal paradigm for the automated feature derivation framework which unifies the feature structure, its interpretation and the calculation logic together. For the particularity of the financial industry, the interpretation of the feature is often of high interest. Then we reformulate the feature generation problem as reinforcement learning by constructing a transformation link and regarding it as a sequential decision process. In addition, we carry out an effective practice on default prediction in consumer finance. Finally, experimental results on the data of user behavior log from 360 Financial show the significant improvement of the proposed method over our years of domain expert knowledge and the Genetic Programming. Moreover, this FDRL framework can be easily adapted to other applications due to its versatility.",
-
notes = "Also known as \cite{9207410}
360 Financial, Beijing, China",
