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The Use of AI in Conveyancing

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We are usually disappointed when reading any articles about the role of technology in conveyancing.  This is mostly due to a chronic lack of ambition, imagination and thought.

However, when it comes to artificial intelligence (AI) or machine learning in conveyancing, we are never let down.  Because frankly, no-one ever writes about it.

What is AI and is it relevant

Most technology discussions about the role of technology in conveyancing espouse the benefits of chatbots, portals and self-service quoting systems.  Hardly topics that are, in the words of Michael Caine, going to blow the doors off.  Whilst AI coves a huge area, fundamentally, it’s the ability of technology to make decisions based on the analysis of historic data and a continuous learning process from each new scenario.

In other words, looking at what has gone before and applying those facts to situations that we see today.  Which sounds awfully like conveyancing.

How do we implement technology solutions

Many think technology simply automates elements of the process, which an expert lawyer could always do better.  This is fanciful thinking at best.  AI using machine learning techniques in the corporate world today are already more effective than lawyers.  So when it is applied to conveyancing, the results are going to be spectacular.

Despite traditionalists holding back the process by their lack of imagination, there’s a huge amount of data in the form of title information, searches and mortgage lender handbooks that can be analysed today.  For example, every new property coming up for sale could be compared against all other properties that have ever been registered to look for similarities and characteristics.

Then comes the tricky bit – how is a machine going to learn which questions to ask when buying a property.  This is challenging because lawyers raise enquiries in a chaotic and unstructured way.  Technologists will need to capture these effectively to be able to apply them to new properties.  They will need the assistance of conveyancers who must capture this information in a way that can be re-used by machine learning software.

Opportunity or threat

Historically, new technology, typically with automation, led to reskilling of existing workers and new opportunities.  Today, for those in low-skill work, the new knowledge revolution is more challenging.  However, in the legal industry, enhancing current skills to move to a more client-centric role is much more achievable. Therefore the risks of unemployment is far lower.  The role of the conveyancer in the future will change, which, given the lack of expertise in the industry, is a good thing.

Lawyers today spend too much time manually reading title documents, inspecting search results and manually raising enquiries and producing reports.  However, technology for this is already available today. For example, extracting title information directly from Land Registry and having software interpret the data and present it in an easy-to-read format.  Geospatial technology can be used to compare title maps with mapping information to check areas such as access and extent.

Lawyers should be spending their time dealing with clients, interpreting their concerns and guiding them through the often tortuous process.  This will be the main change for lawyers. They will have the ability to deal with more cases, as much of the manual work will be done by machines.

Obviously, there will always be an element of a transaction, whether it is a complex restriction, covenant or a tricky lease where lawyers will need to step in.  However, for the majority of the analytical work, technology should provide a solution.

Conclusion

Conveyancers need to move away from their paper files, search results and title documents and start embracing the changes that are coming.  Hours wasted collating data from various sources could be more efficiently achieved by technology.  Their time could be far better spent talking to clients, lawyers and agents to reduce the current confusion and delays.

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