Adopting cloud, big data and AI in supply chain-based ABS
Supply chain-based asset-backed securities (ABS), which the upstream and downstream transactions of the core companies underlie, and which takes the creditworthiness of the core companies as guarantee, are created to secure financing by selling future cash inflows.
This sector is surging year by year, accounting for 25.38% of company-based ABS products in 2019. Legal services to this sector will be prospectively enormous. It should be noted, however, that the underlying assets are complicated, and the number of players is multiple, so conventional patterns are not enough to solve the pain points.
Since China is now rolling out its initiative to “nurture new economy with cloud, big data and intelligence reformation”, the authors would like to explore how to improve the way we work by adopting big data, artificial intelligence (AI) and other technologies.
Most of the legal services rendered during the issuance of products involve due diligence on underlying assets. According to the Rules on Due Diligence for Company Accounts Receivable Securitization provided by the Asset Management Association of China, the due diligence conclusions made by lawyers shall be sufficient so that one can be sure that the underlying transaction is authentic, the transaction consideration is fair, and the transaction contract is real and lawful, with explicit division of rights.
In light of this, the following pain points are frequently encountered during legal services:
Difficulties with the verification of relevant players. To ensure the authenticity of the transactions in which the underlying assets are involved, lawyers need to verify whether the parties to the underlying contracts are still operating, and whether those parties fit the inclusion criteria of the issuance. Although relevant information is available in the state public enterprise credit system, it is extremely time-consuming to check the parties or the corresponding codes against the system when there are hundreds involved.
Difficulties with the verification of association. As the above-mentioned due diligence rules require that the transaction consideration is fair, it is necessary to check whether there is any association or relation between the parties to the underlying contracts, and between the underlying creditor and the core company. This entails penetration through the shareholding structures of the parties involved in the assets to be pooled together, to judge whether there is related-party transaction. Some law firms have begun to use Qcc.com and similar platforms, but still, manually entering the company names, one by one, to access the structure diagrams is time consuming.
Difficulties with the verification of information consistency. When doing due diligence, it is required to check the return receipts of the transfer notices, confirmation letters of the buyers, payment confirmations of the core companies, and credit notes of the factors, to make sure that this information is consistent with the asset lists of the factors or scheme managers. Again, manually checking the lists against the asset documents is a formidable task, neither efficient nor error-proof.
On 4 April 2020, the National Development and Reform Commission, jointly with Cyberspace Administration, issued the Initiative to Nurture New Economy with Cloud, Big Data and Intelligence Reformation, with an intention to make cloud services more accessible, to help businesses adopt big data in a deeper and integrated way, and to heighten support for the intelligence reformation of businesses, with particular emphasis on AI plus real economy. This initiative is also inspiring how we solve the pain points as lawyers.
Cloud services. The data involved in the underlying assets, or needed to be further verified, may be put in a cloud database. For example, it is necessary to check where there is an association relationship between the underlying creditor and debtor, also between the subsidiaries and related parties of the core company and the creditor. A cloud database storing the data of the subsidiaries and related parties of the core companies would be helpful in subsequent verification.
Big data. Buy and connect to the public platforms to access the raw data or data sorted out already, through big data analytics, in the platforms for the items that can be checked via public resources. For example, lawyers may purchase the ownership structure diagrams from the platforms for business information inquiries to check the association between the transaction parties involved in the underlying assets. Text recognition services available on the intelligence big data platforms can also be adopted to check the information consistency between the asset documents and the lists, by converting the PDFs or pictures of asset documents to text data.
Intelligence. Develop intelligent tools for information check based on the patterns and logic summarized, and the data sources acquired from the platforms. Take the logic of a consistency check as an example. Obtain the texts of the asset documents through text recognition platforms. As every asset may be coded, and the asset code appears on each asset document, when comparing the information the computer may locate the asset lists and the asset documents through asset code, and judge whether the information on the asset documents and the lists bearing the same code is consistent.
The authors believe that digital and intelligent means will be more extensively applied in legal service scenarios, beyond due diligence on supply chain-based ABS. Therefore, the whole legal service sector should closely follow the initiative and conduct digital and intelligence transformation as soon as possible.