Legal Technology

The use of AI in the legal sector is growing rapidly, and with it, the potential to streamline processes, improve accuracy, and reduce costs. AI can help lawyers to quickly identify key issues or areas of risk in a case, such as document review, contract management and compliance tracking.

A particularly interesting case study involving AI in the legal sector is LIBOR transition.

On July 27, 2017, the United Kingdom’s Financial Conduct Authority (FCA) declared that the London Interbank Offered Rate (LIBOR) would cease to be published by the conclusion of 2021. Though not entirely out of use, LIBOR was the primary interest rate for inter-bank lending and a key point of reference for financial instruments.

Based on five currencies – the U.S. dollar, the euro, the British pound, the Japanese yen, and the Swiss franc – LIBOR became a vital component of the global financial system, used by banks and other financial institutions to set interest rates for an array of products and services. LIBOR was embedded into financial contracts worth an estimated $300 trillion globally. Thus, the withdrawal of LIBOR triggered considerable panic across financial institutions required to prepare for the transition to new interest rates.

Faced with the need to analyse thousands of LIBOR-referencing contracts for key terms, including “fallbacks” – clauses which delineate the course of action in the event of LIBOR withdrawal – banks and law firms required AI-powered solutions. Existing software, employed in litigation departments to facilitate discovery (the pre-trial gathering of evidence), would still have required the support of 1,000 lawyers over two years to identify and switch over the contracts.

Due to “fallback” clauses being difficult to locate within contracts, law firms and banks turned to Natural Language Processing (NLP) and machine learning-based solutions. Partnering with innovative legal services companies like Elevate, leading firms developed bespoke AI-powered LIBOR transition tools. Machine learning facilitated rapid document analysis and drove informed decision making by observing patterns across a huge corpus of data.

Such AI-based tools have the power to identify and collate contracts requiring the same transition strategies, even if the technical language used is different. This allows for rapid  examination of contractual agreements by automating a previously labour-intensive task. Furthermore, natural language generation is employed to create documents, reports and chatbots for communication between internal and external stakeholders.

The efficacy of AI in streamlining LIBOR-transition demonstrates value for financial institutions dealing with spiralling numbers of remediation incidents. UK banks have incurred an estimated £48.5 billion in remediation costs since 2012 in cases relating to payment protection insurance (PPI) alone. Just as Natural Language Processing (NLP) and machine learning accelerated the transition from LIBOR, these methods will provide firms with technological solutions to manage these cases and reduce the scope for human error.

Procurement and Generative AI

The AI transformation of the legal sector is well underway. While the procurement sector has been slower to embrace new technology, this is starting to change as AI-based solutions increasingly gather momentum. While content management systems are used to streamline the storage and transfer of company knowledge, recent developments in generative AI allow companies to leverage the full power of internal documentation – such as previous winning bids or performance reports – in responding to new RFPs.

Large Language Models (LLMs) allow for the production of high-quality, compelling business prose in a fraction of the time required by a human. Combine this with seamless translation between languages, the summarisation of complex pieces of text, and an ability to alter tonality and form, bid-writing professionals have the tools to vastly improve the speed and quality of their work. Bid-writing is undergoing the same NLP revolution taking place across the spectrum of commercial writing functions.

The business case for integrating generative AI into the bidding process is overwhelming. Equally important, however, are the potential efficiency savings of this technology for the procurement sector as a whole. As stated in the preamble to the Procurement Bill, which recently passed through the House of Lords, ‘one in every three pounds of public money, some £300 billion a year, is spent on public procurement’. Amounting to one-third of total public expenditure, promoting efficiency in the procurement sector could carry huge savings to the public purse.

The tendering process itself carries an invisible cost to the procurement sector, requiring investment in bid writers, solution designers, estimators and senior management staff.The top forty strategic suppliers to the United Kingdom Government allocate approximately ten percent of the total value of their public sector contracts towards the costs associated with tendering. This equates to one hundred million pounds for a supplier that delivers one billion pounds worth of contract value. In the current economic climate, a time when public services are stretched amidst strikes and a lack of funding, it is essential that wastage is minimised.

Generative AI can be part of the solution. Just as AI-powered tools are saving banks and law firms billions in remediation costs, government suppliers can capitalise on this technology to create leaner bid teams, reducing costs and creating a competitive market advantage.

Conclusion

Taking lessons from the legal industry, a convergence of factors stimulated the rapid development of legal-tech solutions. Technological development, market conditions, and the commitment of industry-leaders to driving change were the key factors. In light of the current pressures on public services, perhaps the procurement sector is set for its own watershed moment. Certainly, the technological capability is there. What remains to be seen is which suppliers will emerge as the frontrunners in its adoption and successful implementation.

Bibliography

  • Fallman, D., How to Automate the Bid Management Process with Artificial Intelligence, Forbes Technology Council (2021)
  • Jain, S., Harnessing AI for LIBOR Transition, Straive (2021)
  • Jones, R., The End of a Scandal: Banks Near a Final Release from their PPI Liabilities, The Guardian (2019)
  • Marks, D., The Cost of Competition, AutogenAI (2022)
  • Tavares-Costa, G., The Role of Artificial Intelligence in the LIBOR Transition, Society for Computers and Law Student Bytes (2021)