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Why digital transformation in financial markets is no longer just a ‘nice to have’

Digital transformation is the adoption of technology to transform services or businesses, not only by replacing manual processes with digital processes but also by replacing older digital technology with newer technologies. 

While such transformation was once seen as a ‘nice to have’ or something that financial services companies could get around to eventually, we are now at the point where digital transformation has become mission critical. Not only has the global pandemic ushered in a wave of change by forcing every business to adopt new ways of working - and to learn how to use new technologies - but digital transformation has now also swept into the regulatory landscape and that tide is unlikely to turn back again. 

Globally, regulators are enacting reforms that compel companies to provide more detailed information, if not in real-time, then usually within a 24–48-hour window. Dodd-Frank reforms enacted in the US relating to Qualified Financial Contract (QFC) Recordkeeping, alongside the Financial Accounting Standards Board (FASB) CECL standards require firms to report on multiple data points within 48 hours.

The new European Banking Association guidelines are a demanding set of regulations relating to data management, infrastructure, and reporting processes, that require near-real-time data gathering and ongoing reporting for the lifetime of a loan. The level of detail and accuracy required, starting from June 2021 for new loans, has never been requested by regulators before and means that banks must radically reduce their reliance on manual processes. 

Aside from the proliferation of increasingly demanding regulatory reporting requirements, the proliferation of data makes information gathering more challenging, time-consuming and costly. Many firms have already reached the tipping point where the effort and cost of manually compiling data means they must embrace new technology to give them the capability to manage their information effectively. Thankfully there’s also a proliferation of new technologies such as AI, Blockchain and digital assets to help transform manual processes and workflows. But technology is changing faster than many of us can keep pace with and one of the challenges of digital transformation is simply keeping up with that change. 

Understand the terminology and technology

Understanding the important nuances and differences between terms like artificial intelligence, machine learning, natural language processing and so on, as well as how these technologies have evolved since their inception, is paramount for those wanting to embrace innovation and to start taking advantage of the benefits.

The dictionary definition of artificial intelligence (AI) is “the ability of computer systems to perform tasks normally requiring human intelligence and discernment”. To go a bit deeper, AI is actually a term which covers several types of artificial intelligence. These subsets can be divided by technology type – some require machine learning (ML) or natural language processing (NLP), for example. But they can also be differentiated by the level of intelligence embedded into an AI machine.

Machine learning is an approach to AI that enables a machine to automatically learn from past data. Machine learning can be applied to speech or image recognition, medical diagnosis, predictive analytics, and extraction of data, to give some common examples. Using AI and NLP, for example, new technology platforms can extract key terms and provisions from loan documents in a fraction of the time taken by manual processing and with a high degree of accuracy, making them adept for meeting the new regulatory compliance requirements.

Natural language processing, or NLP for short, is the application of ML to human language. NLP focuses on the interpretation and manipulation of human language (chat bots, speed to text, written language, etc). NLP has advanced enormously since the rule-based approach at its inception - and even more so in the last few years with new and improved methods of machine learning.

This level of complexity is a key barrier, and often a fear, for many organizations undergoing change management. Even if they procure and adopt new technology solutions, they worry that they might not have enough technically minded staff to actually understand, use and make the most of these new resources. 

Responding to this challenge, many organizations have embraced the growing movement towards so-called ‘no-code’ solutions. No-code is a perhaps slightly misleading term which does not mean that there is no computer code in the solution but rather that the code sits ‘under the hood’ and the end user doesn’t need to call on IT support or have any specialist technical knowledge in order to use or manage it. 

The next stage of digital transformation

It is clear that the process of starting, and then seeing digital transformation through, is daunting for many organizations, especially in more traditional industries or those, like financial markets, that are heavily regulated. Yet, regulators are accelerating change through the application of tighter rules based on a newfound awareness of the benefits that digital transformation can offer in terms of transparency and speed of access to information. 

Beyond the need for regulatory compliance, however, there are additional benefits to be had by firms who decide to embrace digital transformation rather than ignoring or delaying it. One of the key benefits is enhanced data management and valuable, actionable insights into your data which would simply not be possible without the use of technologies like AI.

Data is your greatest commodity

Data is your most vital asset, which will remain untapped without technology. In fact, it's been estimated by the IDC and Gartner that between 80-90% of organizational data is unstructured and unanalyzed because the cost and effort to manually gather and organize it is prohibitive. A recent piece in Harvard Business Review examining the challenges of digital transformation summed the problem up neatly “Data presents an interesting paradox: Most companies know data is important and they know quality is bad, yet they waste enormous resources by failing to put the proper roles and responsibilities in place. They often blame their IT functions for all these failures”.

Transforming the way data is managed can decrease costs and create efficiencies but crucially it can also help manage and reduce market risk. The Covid-19 pandemic has led to greater market volatility over the past 12 months, making transparent oversight more important than ever. Being able to use AI tools to automate processes and analysis means institutions can respond more quickly, stay aware of risks, and go on to make decisions which are backed up by accurate data. 

What is sometimes also forgotten is that better management of data using AI can not only create efficiency and manage risk but can also offer rich insights into critical information, market trends and customer behaviour which can lead to new services and give the business an edge over its competitors. Investing in technology then becomes not just a cost but an integral part of the company’s long-term growth strategy. Organizations who can successfully tap into all their valuable sources of data will have a powerful market advantage.

Established institutions are increasingly in competition not just with fintechs and digital banks but also with the ‘techfins’ (the likes of Google, Amazon, Facebook etc who are all moving into the financial services space). One of the edges these platform companies have is naturally their technology expertise but more important is the data their technology gives them access to, and upon which most of their value is created. Without investing in digital transformation, the rise of the techfins and their encroachment into the space traditionally occupied by banks will only continue to pose a greater threat.

Despite all this, research published by Cornerstone Advisors in a report on Rebounding from the Pandemic shows that just 14% of banks who self-report as already being halfway or more through their digital transformation journey have deployed machine learning tools to date. Looking at all the external and internal drivers pointing towards the urgent need for digital transformation, the question is, if not now, when?

The Eigen Document AI platform uses AI and NLP to ensure regulatory compliance, more effectively manage loan data across the credit lifecycle and provide enhanced access to critical data. You can find more information on Eigen’s platform and request a demo here.