Discussion on AI enabling legal due diligence before investment of non-performing assets

abstract

The non-performing assets market is faced with the problem of information asymmetry and risk concealment for a long time, and the traditional legal due diligence model is inefficient and costly. With the development of artificial intelligence (AI) technology, its application in the legal due diligence before the investment of non-performing assets has gradually attracted attention. This article explores the potential of AI to solve the core challenges of NPL legal due diligence, analyzes its challenges and boundaries, and looks at future trends. Research shows that AI technology can significantly improve due diligence efficiency and risk identification accuracy, but human intelligence is still irreplaceable in legal interpretation, non-public information acquisition and business logic judgment. In the future, the development of AI technology and its integration with technologies such as the meta-universe will push non-performing asset due diligence to a higher dimension.

Introduction

The core difficulty of NPL investment and disposal lies in information asymmetry and risk concealment. Traditional legal due diligence, which relies on teams of lawyers to review contracts page by page, manually compare title documents, and manually retrieve related lawsuits, can take weeks or even months and can be costly. This inefficient investigation model is difficult to meet the requirements of efficiency and precision in modern financial markets.

In recent years, the rapid development of artificial intelligence (AI) technology has brought new opportunities to the non-performing assets market. Using techniques such as natural language processing (NLP), knowledge graph and machine learning, AI can upgrade the due diligence process from "blanket screening" to "precision guidance", significantly improving the efficiency of due diligence and risk identification accuracy, and uncovering associated risks that are difficult for humans to detect. This paper aims to explore the enabling role of AI in pre-NPL legal due diligence, analyze the challenges and boundaries it faces, and look forward to the future development trend.

AI enables four core breakthroughs in the legal due diligence of non-performing assets

(1) Automatic processing of batch unstructured data

In the traditional model, lawyers need to manually go through thousands of pages of loan contracts, mortgage contracts, judgments, industrial and commercial archives and other documents, and extract key information, such as the sequence of collateral, seizure status, and so on. This process is not only time-consuming and laborious, but also prone to information omission or error due to human negligence.

Through the document parsing engine, the AI solution supports the OCR recognition of PDF, scanned copies, and pictures, and automatically extracts fields such as "collateral list" and "rights restriction situation". At the same time, the smart label system can give early warning to high-risk labels such as "second mortgage" and "waiting for seizure", effectively reducing risks.

(2) Penetrating identification of implicit associated risks

In the non-performing assets market, it is not uncommon for enterprises to transfer assets by means of cross-shareholdings, holding agreements and related transactions. Under the traditional due diligence model, manual due diligence is difficult to trace the flow of funds, which makes it difficult to identify the hidden associated risks.

AI can effectively solve this problem through equity penetration models and fund flow graphs. A corporate family tree based on industrial and commercial data can identify shell companies and special purpose vehicles (SPVS) associated with actual controllers, quickly analyze multi-layer nested limited liability companies and partnerships, and trace clues that core assets may have been transferred. At the same time, through bank statements and invoice data to restore the "asset stripping - transfer - hiding" path, AI can provide more comprehensive and in-depth risk identification support for due diligence.

(3) Dynamic quantitative assessment of legal compliance risks

In traditional due diligence, lawyers often rely on personal experience to judge the risk level, which lacks data support, resulting in strong subjectivity of risk assessment. AI technology can provide dynamic quantitative assessment of legal compliance risks through judgment rule mining and risk scoring models.

Through AI technology, it can also analyze the disposition tendency of similar assets across the country, and generate an asset risk index (such as 0-100 points, the higher the score, the higher the risk) by combining the data of the debtors involved in litigation records, administrative penalties, and lists of broken faith. This quantitative evaluation method not only improves the accuracy of risk identification, but also provides investors with more scientific decision-making basis.

(4) Intelligent discovery of value depression

In the non-performing assets market, high quality assets are often covered up due to incomplete procedures and defects in property rights. Under the traditional due diligence model, the value of these assets is difficult to fully tap.

Through big data retrieval and defect repair cost models, AI can initially calculate the time and cost of reissuing property certificates and removing seizures. At the same time, combined with data such as urban planning and judicial auction premium rates, AI can predict the appreciation space of assets and find potential value depressions for investors. For example, a piece of land on the outskirts of a city that was idle due to incomplete procedures was found to have great appreciation potential under the analysis of AI due diligence tools, and was eventually acquired by investors at a lower price and successfully revitalized.

Challenges and boundaries of AI-enabled due diligence

(1) Flexibility of legal interpretation

While AI has significant advantages in data processing and risk identification, there are still limitations when it comes to legal interpretation. Legal concepts such as "principle of good faith" and "obviously unfair" have strong abstraction and flexibility, and it is difficult to judge accurately through quantitative models. Human lawyers, with their rich experience and comprehensive understanding of industry practices and judges' discretionary tendencies, are better able to deal with these complex issues.

(2) Supplement of non-public information

AI technology relies on data input and cannot effectively identify and process non-public information such as hidden drawer agreements and verbal commitments. This information is of great value in due diligence and needs to be supplemented by debtor interviews, site visits, etc. Therefore, AI due diligence reports need to be combined with field investigations by human lawyers to ensure the comprehensiveness and accuracy of the information.

(3) The balance between business logic and legal risk

The balance between commercial logic and legal risk is crucial in non-performing asset investment. For example, if the mortgage rate of an asset exceeds the standard but the location is scarce, AI may suggest giving up investment, but human decision-makers are likely to choose premium acquisition and reorganization based on strategic layout. AI in the non-performing asset investment business only needs to provide a list of risks, while human lawyers can weigh the risk-return ratio and make more reasonable decisions.

Future Outlook: The "Upgrading Revolution" of AI Due Diligence

(1) Improvement of data processing and analysis capabilities

AI technology will continue to optimize the processing capacity of unstructured data, through natural language processing (NLP) and machine learning, can more efficiently extract critical information from massive documents, such as mortgage status, debtor credit history, and quickly identify potential risks. In addition, AI will automatically create summary reports, flag unusual terms, and generate insights into transaction risk through enhanced data analytics.

(2) Meta-cosmic investigation

With the development of virtual reality (VR) and augmented reality (AR) technology, meta-cosmic inspection will become an important means of due diligence of non-performing assets in the future. Through VR/AR technology, investors can remotely check the physical state of assets, such as the wear and tear of plant equipment, land development, etc. This technology not only improves the efficiency of due diligence, but also reduces the cost and risk of field investigations.

(3) Ecological restructuring promoted by data sharing

In the future, if data interoperability between AI and the judicial system becomes possible, the court enforcement system can be integrated with data from due diligence tools, which can update asset disposal data and status in real time. Such data sharing will promote the restructuring of the NPL due diligence ecosystem and improve the transparency and efficiency of the market.

(4) Cross-field application and internationalization support

From the current point of view, AI can already support cross-domain due diligence, such as in cross-border transactions, AI can perform comparative analysis of contracts under different legal frameworks to ensure compliance with the standards of the country/region. In addition, AI will also support a multilingual environment to help international investors better understand and value non-performing assets.

Conclusion

AI technology is transforming non-performing asset due diligence from a "labor intensive" operation to a "cognitive intensive" battlefield. AI can not only significantly improve the efficiency of due diligence and risk identification accuracy, but also provide investors with more scientific decision support. However, AI technology still has limitations, and human intelligence plays an irreplaceable role in legal interpretation, non-public information acquisition and business logic judgment. The future due diligence of non-performing assets will be the deep integration of AI and human wisdom, and the "super legal person" who makes good use of AI to amplify professional leverage will become the winner of the market. With a risk scanner in their left hand and a value detector in their right, they help clients dig through the rubble of distressed assets.