WeNet-RF: An Automatic Classification Model for Financial Reimbursement Budget Items.

ABSTRACT: Accurate classification of budget items is a critical component of financial reimbursement, as it determines the legitimacy and regulatory compliance of financial expenditures. Currently, manual classification of reimbursement budget items faces to two challenges of inefficiency and inaccuracy. This is primarily due to the labor-intensive nature of the task, which increases the likelihood of selecting incorrect categories. To address these challenges, this study proposed a WeNet-Random Forest (WeNet-RF) model, which leverages speech recognition technology (WeNet) and Random Forest (RF) to improve efficiency and classification accuracy. WeNet-RF includes four steps: speech identification, features extraction, items classification, and evaluated model.This study compared WeNet-RF with Convol

SUBMITTER: Suo P 

PROVIDER: S-EPMC12021194 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature