A Novel AI-Based Approach for Automated Moderation of Client-Supporting Files in Regulatory Claims
Elzana Dupljak Afan Hasan Bekim Fetaji
AbstractClaim moderation processes in industries like airline compensation, insurance and regulatory compliance are becoming increasingly complex. Manual moderation methods are time consuming, error prone and can’t handle the variety of file types clients submit – PDFs, images, audio files etc. To address this challenge this paper proposes an AI based moderation system that can automatically process and classify supporting documents in any format and validate them against the claims being made. The motivation for this work is the need for faster, more reliable and scalable moderation systems that can extract relevant data from diverse files and ensure regulatory compliance. The main gap this paper addresses is the inefficiency and inaccuracy of current moderation processes that can’t scale with large volumes of claims and diverse document formats. Existing solutions either rely on rigid rule based approaches that lack flexibility or basic machine learning models that don’t generalize well across different file types. Identifying this gap the proposed AI moderation system integrates advanced machine learning techniques for file classification, metadata extraction and inappropriate content detection to provide a more robust solution than previous approaches. The novelty of this work is the ability to handle multiple file types – text documents, images, multimedia files etc. with focus on extracting key data required for claim validation. Unlike previous studies that focus on a narrow range of document types or use simple classification methods this system uses deep learning models to classify files, detect inappropriate content and validate metadata and provides real time feedback to users. The contributions of this paper are: (1) a comprehensive multi-format AI moderation system for document processing in regulatory claims (2) novel methods for automated metadata extraction and inappropriate content detection (3) fills the gaps by addressing the limitations of existing solutions in terms of scalability, flexibility and accuracy. The ability to process complex claims with diverse file formats differentiates this system from current models and provides significant improvement in moderation speed, accuracy and overall effectiveness