English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The economic and banking importance of the small and medium enterprise (SME)sector is well recognized in contemporary society. Business credit loans arevery important for the operation of SMEs, and the revenue is a key indicator ofcredit limit management. Therefore, it is very beneficial to construct areliable revenue forecasting model. If the uncertainty of an enterprise'srevenue forecasting can be estimated, a more proper credit limit can begranted. Natural gradient boosting approach, which estimates the uncertainty ofprediction by a multi-parameter boosting algorithm based on the naturalgradient. However, its original implementation is not easy to scale into bigdata scenarios, and computationally expensive compared to state-of-the-arttree-based models (such as XGBoost). In this paper, we propose a ScalableNatural Gradient Boosting Machines that is simple to implement, readilyparallelizable, interpretable and yields high-quality predictive uncertaintyestimates. According to the characteristics of revenue distribution, we derivean uncertainty quantification function. We demonstrate that our method candistinguish between samples that are accurate and inaccurate on revenueforecasting of SMEs. What's more, interpretability can be naturally obtainedfrom the model, satisfying the financial needs.

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