Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Vertical Federated Learning Method for Interpretable Scorecard and Its
  Application in Credit Scoring   With the success of big data and artificial intelligence in many fields, the
applications of big data driven models are expected in financial risk
management especially credit scoring and rating. Under the premise of data
privacy protection, we propose a projected gradient-based method in the
vertical federated learning framework for the traditional scorecard, which is
based on logistic regression with bounded constraints, namely FL-LRBC. The
latter enables multiple agencies to jointly train an optimized scorecard model
in a single training session. It leads to the formation of the model with
positive coefficients, while the time-consuming parameter-tuning process can be
avoided. Moreover, the performance in terms of both AUC and the
Kolmogorov-Smirnov (KS) statistics is significantly improved due to data
enrichment using FL-LRBC. At present, FL-LRBC has already been applied to
credit business in a China nation-wide financial holdings group.

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The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc