Legal Automation in Practice

What is structured data? 

At a high level, structured data has a framework that makes it machine readable and machine interpretable. If data has these two attributes, then a computer can process this information in novel ways. Digital data is not necessarily the same as structured data. Consider two digital versions of a budgeting table - the first is a static JPEG image, and the second is an excel spreadsheet. The excel spreadsheet will be the only version that also captures information defining how quantities in the table are calculated. A user could change, add or remove one or more particular values in the table, and immediately see how it affects other quantities in the table as a whole. If sufficient quantities of structured data exist, a computer can be trained to recognise patterns and derive insights that would otherwise take humans years of experience to discover, as seen with Amazon, Ocado and Google. 

Why is automation important for the future of financial transactions?

If data was captured in a structured form, financial or legal service providers could identify patterns in their client’s behaviour and devise or tailor products or services that are more suited to that client. The system could start to predict what comments certain clients are likely to make. Documents could be customised without the client having to remake the same comments added on a similar deal the week before. This would also allow lawyers to price more effectively, as they could see precisely which parts of a transaction, right down to negotiations on particular clauses, were profitable and which were not. 

The ability to identify patterns in client behaviour and make predictions from such patterns will provide service providers with a previously unattainable level of client insight and understanding. Ultimately, it facilitates the provision of the strategic advice that is appreciably more valuable to clients than the commoditised and mechanical execution of a transaction. This intelligence is an exciting revenue growth opportunity. 

How can data be effectively captured for use?

1. Take unstructured documents, as they currently are, and parse them through an engine which reads them and converts them into a structured form. There are various ways of doing this, with OCR and natural language processing technologies being the best- known examples and usually components of an overall solution. This technology, while useful, is not yet advanced enough to extract data from complex legal documents with the required accuracy.

2. Structure the data from the point at which the documents are first drafted. This is achieved by using a mark-up language, which is a type of computer language that uses tags to define elements within a document. The language is designed to be human-readable, with a syntax that makes use of natural language as opposed to typical programming syntax.

It is essential that the market coalesces around one standard so that data across the market can be read, understood and interpreted in the same manner.

What are some examples of legal technology in practice?

1. Origin Markets automated issuance 

The Luxembourg Stock Exchange (LuxSE) and Origin had their first fully digital listing of a debt security, which was issued and listed on LuxSE on 26 February 2021. Through its origination platform, the Origin Marketplace, it connects frequent borrowers and investment banks in the international debt capital markets in a global marketplace for private placements. Through digital deal execution and automated documentation flows, Origin is used in the issuance process for new financial debt instruments. The Origin platform can be used to generate, share and approve fully customisable term sheets and final terms. 

2. FLOW transaction execution

The London Stock Exchange Group uses Issuer Services Flow, which was developed by Nivaura. FLOW is used by issuers, dealers, legal advisers and other stakeholders to originate, negotiate and close deals. 

3. Document automation

Many large law firms are investing in technology platforms that automate document production and negotiation. The platforms allow lawyers to save time on previously laborious tasks, such as drafting very standard templates, or many versions of the same document for a particular transaction. 

4. Workflow management

Workflow tools such as HighQ, Legatics and Litera Transact make the process of managing and executing transactions easier. For example, these platforms save lawyers significant time on things like collecting signature pages and CPs, which on complex cross-border finance deals can be long winded processes