Intelligent Automation for FDI Facilitation: Optimizing Tariff Exemption Processes with OCR And Large Language Models
Muhammad Sukri Bin Ramli

TL;DR
This paper introduces an AI-powered framework combining OCR and Large Language Models to streamline tariff exemption processes, reducing administrative burden and improving accuracy for FDI facilitation.
Contribution
It presents a novel integrated system leveraging OCR and LLMs to automate and optimize tariff exemption verification, enhancing efficiency and accuracy in FDI processes.
Findings
Improved speed of exemption verification
Reduced administrative workload
Enhanced accuracy in tariff code matching
Abstract
Tariff exemptions are fundamental to attracting Foreign Direct Investment (FDI) into the manufacturing sector, though the associated administrative processes present areas for optimization for both investing entities and the national tax authority. This paper proposes a conceptual framework to empower tax administration by leveraging a synergistic integration of Optical Character Recognition (OCR) and Large Language Model (LLM) technologies. The proposed system is designed to first utilize OCR for intelligent digitization, precisely extracting data from diverse application documents and key regulatory texts such as tariff orders. Subsequently, the LLM would enhance the capabilities of administrative officers by automating the critical and time-intensive task of verifying submitted HS Tariff Codes for machinery, equipment, and raw materials against official exemption lists. By enhancing…
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Taxonomy
TopicsModeling, Simulation, and Optimization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
