Effective Automatic Feature Engineering on Financial Statements for Bankruptcy Prediction

Feature engineering on financial records for
bankruptcy prediction has traditionally relied significantly on
domain knowledge and typically results in a range of financial
ratios but with limited complexity and feature utilization due
to manual design. It is often a time-consuming and error-prone
procedure, confined to the domain experts’ experience, without
taking into account the characteristics of different data sets.
In this paper, we propose an automated feature engineering
approach to generate effective, explainable, and extensible model
training features. The experiments have been conducted using
a publicly available record of financial statements submitted
to the Luxembourg Business Registers. This approach aims to
improve bankruptcy prediction for professionals who may not
possess the necessary engineering expertise or efficient data.
The experimental results suggest that the proposed approach
can provide valuable features for model training and in most
of the cases, the model’s outcomes outperforms predominantly
as compared to the traditional approaches and the well-known
approaches the models, thus can provide valuable features for
model training.