The ‘business as usual’ approach to AI for finance

By Stephanie Vaughan, Director, iManage RAVN

Following the Royal Commission’s misconduct enquiry into the financial sector in Australia, financial institutions are having to undertake extensive remediation.

However, some programmes that are coming at a huge cost and significantly impacting bottom lines. Analysts predict that remediation cost to the sector could reach $2.15 billion in 2020. 

Given the enormous scale of the remediation exercise – financial institutions have to go back more than a decade to identify the customers and amounts that must be refunded – it’s no wonder that technologies such as artificial intelligence (AI) and machine learning are being drawn upon to assist with the effort.

This is precisely what law firm MinterEllison has done for the financial institution it’s representing. The firm is using AI to take an innovative approach to document review that greatly streamlines the task, handles it more efficiently than people alone could hope to, and saves the client money. 

This is a fantastic usage of AI to tackle a specific problem. Financial institutions shouldn’t limit themselves to only using AI reactively, however. Instead, they should aim to incorporate it into everyday operations – in essence, making AI “business as usual.”

What this means is that while a specific event – such as remediation, or the LIBOR rate change – might serve as a trigger to start using AI to review documents, financial institutions should use that exercise as a foundation that they can continue to build upon and leverage. This allows institutions to create a continually updated baseline of knowledge about their contracts and documents, helping them map out where any risk potentially lies.

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The benefits of taking this BAU approach to AI are severalfold. For starters, when another trigger event happens, institutions aren’t in reactionary mode, scrambling to gauge how much risk they’re exposed to, because they’ve already been employing AI to analyze and better understand their document estate. Better yet, they can use the knowledge that AI surfaces to proactively identify risks – or opportunities – in their contracts.

To reach this stage, financial institutions should ensure that AI is widely adopted throughout the organization, rather than in just one or two business units. You don't want one business line to have detailed knowledge about the customer relationship and in-depth knowledge of what's outlined in the agreements while another business line doesn't. The goal should be for the entire financial institution to have the same level of insight and provide the same high level of service to its customers.

Financial institutions are gaining a deeper understanding of existing process limitations that have amplified business problems. Fortunately, the maturation of AI technology solutions addresses both the evolving business issues and what it takes to facilitate adoption across the entire institution, including programmes and support levels necessary to achieve success. This is a key factor in making AI business as usual, rather than a technology that is only deployed for special cases like remediation. In taking this BAU approach, financial institutions create a foundation for data-driven processes that can assist their businesses in many ways – from more efficiently tackling regulatory or compliance issues in the future, to better-serving customers and identifying commercial opportunities today.

By Stephanie Vaughan, Global Legal AI Practice Director at iManage RAVN

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