2026-05-05T04:31:34.398Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11660 2025-10-27T16:06:47Z 1

MSQ Methodology in AI-driven Analysis of Assets and Liabilities: A Case Study of the ValidoAI System 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11660 https://ceur-ws.org/Vol-4077/paper13.pdf S. Milojković P. Dakić T. Heričko M. Aleksić J. Lang Small and medium-sized enterprises (SMEs) in Serbia often lack access to tools that enable them to interpret basic financial statements independently, making it difficult to assess liquidity and debt levels. This paper presents the ValidoAI system, which applies a Minimum Sufficient Quantity (MSQ) approach to automate balance sheet analysis using real-world SME data. The core component, the AI Ledger module, transforms unstructured accounting records into structured representations, visualizes asset and liability structures, and generates narrative explanations of financial positions. Emphasis is placed on the relationship between short-term liabilities, equity, and current assets, enabling the system to classify financial stability and highlight potential risks. The methodology integrates a Python-based ETL pipeline, automated data anonymization, and GPT-driven narrative generation, ensuring both transparency and accessibility for non-expert users. To evaluate the system, we have created an example testing dataset was by extracting anonymized balance sheet and transaction data from SME accounting software in both PDF and Excel formats. The data was standardized, categorized, and structured to reflect typical SME financial records, enabling robust testing of the automated analysis pipeline. This study is focusing on the potential of AI-driven, explainable systems that can support financial decision-making in the SME sector, even in the absence of formal accounting expertise. Results demonstrate that a small set of well-selected indicators can reliably identify liquidity risks and unbalanced capital structures, providing actionable recommendations without manual intervention. conferenceObject https://ceur-ws.org/Vol-4077/paper13.pdf https://ceur-ws.org/Vol-4077/paper13.pdf