By Stephanie Vaughan, Global Legal Practice Director, iManage RAVN


No industry has escaped the impact of the COVID-19 pandemic, but financial services has had a particularly hectic year.

The pandemic threw a wrench into the gears of the global economy, bringing “business as usual” grinding to a halt. Many businesses and individuals found themselves unable to meet their financial commitments, requiring huge government economic interventions to stave off financial collapse and a potential global economic depression.

As we look forward to the coming year, financial services firms of all shapes and sizes will need to access every tool in their toolkit to ensure they safely navigate the months ahead. Knowledge management (KM) might just be the secret weapon they’re looking for to not only survive, but thrive, in this changing world.


The two approaches: data analytics versus content production

In recent years, different KM approaches from around the globe have converged.

The US has long incorporated a data-driven approach to KM, focusing on search, structuring data and analytics. In the UK and across other parts of the globe, KM efforts have focused less on data analytics, and more on producing content and knowhow.

For quite some time, these approaches were kept separate – even in organisations that operated internationally. But a cross-pollination has taken place, and US knowledge teams are starting to focus more and more on content curation. At the same time, UK knowledge teams are looking to analytics and data-oriented projects to extract new insights.

As the two approaches merge, KM becomes better suited than ever to deliver value to organisations in new ways. This is for two reasons. First, it enables processes to be defined and helps join the dots between different exercises. Second, it enables knowledge to be delivered to the people who need it in new and better ways.


KM helps to define processes

Within most financial services organisations, innovation and KM have been separate functions. Most of the press coverage and hype has been associated with themes such as AI and blockchain. In the meantime, knowledge managers have had their noses to the grindstone getting on with their work, outside the spotlight.

Increasingly, however, firms are realising that that innovation doesn’t happen on its own. Through lack of adoption of technology or lack of tangible change, people are starting to realise that scaling innovation is part of a wider process that requires an in-depth understanding of internal processes. This, together with increasing acknowledgement of the cost to the wider institution of inefficiencies and lack of processes in a highly regulated environment, is where KM steps in. With a focus both on content creation and data analytics, KM can provide the vital role of increasing understanding around key processes. Only once those processes are understood can technology start to enable real and lasting change.


Example: LIBOR

The upcoming LIBOR transition provides a helpful example.  Financial institutions have financial products linked to LIBOR valued at more than USD 350 trillion. LIBOR is due to be phased out at the end of 2021. This means that all documentation underpinning these financial products must be amended so that they no longer refer to LIBOR. This is known as the “LIBOR re-papering” exercise.

The first problem financial institutions face is to identify all those products that do, in fact, refer to LIBOR. Because of the scale on which this exercise must be carried out, it is often not cost-effective to employ humans to undertake this exercise. Instead, financial institutions have deployed AI tools to identify documents that contain references to LIBOR, in order that these documents can be subjected to analysis and amended. This isn’t over-hyped AI: this is the identification of a problem that needs a solution powered by AI.

It is also a problem that could have been lessened if there had been effective KM processes in place. Indeed, it is off the back of repeated regulatory changes such as LIBOR that many financial institutions are beginning to realise just how important KM is. KM plays a vital role here to help with  remediation – i.e. cataloguing the processes and best practices that must be applied to this exercise, so that financial products can continue into 2022 and beyond. Additionally, KM also assists by allowing financial institutions to have in-depth knowledge and data insights across their contracts. An effective KM system allows firms to combine content-based and data-centric approaches making solving problems such as LIBOR easier.


KM helps to bring discrete processes together

The KM role goes even further, using the LIBOR opportunity to identify other risks during a large-scale contract analysis.

For example, what COVID-19 specific scenarios could these same processes be applied towards? Are there leases in a commercial real estate portfolio that need to be renewed? Are there hidden deadlines contained in contracts that would otherwise be overlooked? LIBOR has presented an opportunity for financial institutions to overhaul their contract management systems. AI plays a role in all of these exercises – but it is KM that it is pulling the strings and spotting the links between what would otherwise be discrete exercises.


No office necessary

Even businesses with the most advanced KM functions have traditionally relied on people “popping their head around your door” to have a chat. As workforces get used to remote working, KM has a huge part to play here.

In a distributed work environment, it is harder for employees to rely on know-how being stored in people’s heads and inboxes. Here, KM only grows in importance, delivering existing best practices and curated content to the knowledge workers who need it to carry out their jobs effectively.

AI again works hand-in-hand with KM here, providing new ways to proactively surface important knowledge assets and offer them up to professionals. The partnership between AI and KM will inevitably lead to Netflix-like suggestions: “people accessing this template also viewed this survival guide.” This partnership is nothing without a KM team focusing on the right content and the right data points.

The post-pandemic world will not be without its challenges, but financial services firms can take comfort in the fact that they have KM as a secret weapon at their disposal, and KM can rightfully and deservedly bask in some of its newfound glamour.


About the author

Stephanie Vaughan is Global Legal Practice Director at iManage RAVN. She leads the team at iManage that engages with law firms, corporates, and professional services firms to help them understand and adopt artificial intelligence technology in a practical manner – while fostering a culture of innovation in their organisations. A lawyer by training, prior to iManage RAVN, she was at international law firm, Allen & Overy, where she worked in the Market Innovations Group and the Derivatives and Structured Finance Group, delivering global technology focused projects to clients. For these projects, she was involved in everything from design to delivery and ongoing running of the programs.

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