Large-scale Uncertainty Estimation and Its Application in Revenue Forecast of SMEs

TL;DR

This paper introduces a scalable natural gradient boosting method for reliable revenue forecasting of SMEs, providing high-quality uncertainty estimates to improve credit limit decisions.

Contribution

It proposes a scalable, interpretable natural gradient boosting approach that effectively estimates predictive uncertainty in SME revenue forecasting.

Findings

The method accurately distinguishes between reliable and unreliable revenue predictions.

It is scalable and easy to implement in big data scenarios.

The approach enhances interpretability for financial decision-making.

Abstract

The economic and banking importance of the small and medium enterprise (SME) sector is well recognized in contemporary society. Business credit loans are very important for the operation of SMEs, and the revenue is a key indicator of credit limit management. Therefore, it is very beneficial to construct a reliable revenue forecasting model. If the uncertainty of an enterprise's revenue forecasting can be estimated, a more proper credit limit can be granted. Natural gradient boosting approach, which estimates the uncertainty of prediction by a multi-parameter boosting algorithm based on the natural gradient. However, its original implementation is not easy to scale into big data scenarios, and computationally expensive compared to state-of-the-art tree-based models (such as XGBoost). In this paper, we propose a Scalable Natural Gradient Boosting Machines that is simple to implement, readily parallelizable, interpretable and yields high-quality predictive uncertainty estimates. According to the characteristics of revenue distribution, we derive an uncertainty quantification function. We demonstrate that our method can distinguish between samples that are accurate and inaccurate on revenue forecasting of SMEs. What's more, interpretability can be naturally obtained from the model, satisfying the financial needs.