JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT

2782-4586 2949-1851

109056

10.26118/2782-4586.2025.54.79.033

Научные статьи

SCIENTIFIC ARTICLES

Научные статьи

Measurable impacts of LLM-powered document intelligence on M&A due diligence

Измеримые эффекты использования документальной аналитики на основе LLM в процессе M&A due diligence

Избасар

Асем Бахытжановна

Izbassar

Assem Bakhytzhankyzy

Казахстанский институт менеджмента, экономики и прогнозирования Алматы Казахстан Kazakhstan Institute of Management, Economics and Forecasting Almaty Kazakhstan

03 12 2025

9 447 451 01 12 2025

https://zhpi.ru/en/nauka/article/109056/view

В обзоре количественно оценивается влияние аналитики документов на основе больших языковых моделей (LLM), включая retrieval augmented generation (RAG) и агентный искусственный интеллект, на процесс due diligence со стороны покупателя при сделках слияний и поглощений среднего рынка. Проведена быстрая оценка доказательств по регуляторным и профессиональным вопросам, отраслевым кейсам, рецензируемым исследованиям и вендорским отчетам. Анализ сосредоточен на измеримых показателях, таких как время проверки документов, качество выявления рисков и механизмах участия человека. Сопоставленные данные показывают сокращение времени проверки на 70-75% по сравнению с ручным анализом. Оптимизированные RAG-конфигурации достигают точности анализа договоров до 95%, что сопоставимо или превосходит предыдущие решения машинного обучения. Банки, фонды прямых инвестиций и юридические фирмы отмечают ускорение сделочных циклов и повышение эффективности выявления рисков при сохранении качества. Участие человека остается ключевым элементом, обеспечивающим надежность и управляемость технологии.

This review quantifies how large language model (LLM) powered document intelligence, including retrieval augmented generation (RAG) and agentic AI, is reshaping buy side M&A due diligence in mid market deals. We conduct a rapid evidence assessment across regulatory and professional surveys, industry case studies, peer reviewed studies, and vendor whitepapers. We focus on measurable indicators: time to review, issue detection quality, and human in the loop controls. Triangulated findings show 70–75% reductions in document review time versus manual baselines. Optimized RAG configurations reach contract analysis accuracy up to 95%, matching or surpassing earlier machine learning tools. Banks, private equity firms, and law firms report faster deal cycles, stronger risk flagging, and maintained or improved quality with appropriate oversight. Humans in the loop remain essential to manage hallucinations and privacy constraints without erasing efficiency gains. The study consolidates metrics from real deployments and highlights governance practices required for adoption in the mid market.

due diligence в сфере M&A большие языковые модели генеративный искусственный интеллект анализ документов retrieval-augmented generation эффективность legaltech сделки среднего рынка

M&A due diligence large language models generative AI document review retrieval-augmented generation efficiency legaltech mid-market transactions

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