As fintech faces more automation, clients should still come first

While much of today’s fintech debate focuses on the potential applications of the technology to financial services, just as important are the underlying reasons why the industry is pursuing innovation so aggressively right now. To a large extent, it reflects the realities of the current environment – the need to reduce costs, to achieve regulatory compliance, to protect against new forms of risk and to stay relevant in a fast-moving and uncertain environment.

However, there’s also another driver – maybe the most important one of all. It’s the client. Every fintech discussion should start and end with the same question: how can we use technology to enhance the client experience? To answer this, we need to understand which technologies will have the greatest impact and deliver the most client value today, tomorrow and in the future.

Of all the current innovations, robotic process automation (RPA), or bots for short, are the most ubiquitous in financial services today – and they’re already improving the client experience, both in our personal and professional lives. We see this clearly on the retail side of banking and in wealth management, for instance, where robo-advisors are becoming more common every day.

Unsurprisingly, progress is also being made in integrating bots into the back office. As the technology continues to mature, they will be able to take on a wider range of complex and labor-intensive processes, such as gathering data from multiple applications, managing client master data and avoiding dual data entry.

What firms find so exciting about the technology is that it’s capable of not only delivering cost and productivity benefits, but it can also drive more transformational client engagement and personalized service.

At DTCC, for example, we’re in the process of integrating bots into our infrastructure to support how we onboard a new client onto our Global Trade Repository service. Over time, we believe bots will help us mitigate risk by reducing manual error rates, speed up the process for clients by highlighting exceptions earlier and adding a level of consistency that simplifies the onboarding life cycle.

For all the benefits, however, there are also risk implications of a digital workforce. That’s why it’s so critical that automation initiatives are carefully managed and coordinated across the enterprise, and appropriate controls and governance are established before RPA programs are implemented.

Applied machine learning represents the next frontier in the digital transformation of how financial transactions are processed with the potential to enhance client value by boosting efficiencies and reducing risk. While the industry is still in the early stages of applying machine learning to solve business challenges, advancements should come quickly within the next two to five years. Data is driving this trend, and many firms are evaluating how it can be used to support business growth and enhance the client experience.

At this relatively early stage in the maturation of machine learning, it’s critical for the industry to put in place the right foundational strategy to maximize the value of the technology. There are three keys areas of focus:

First, we must create and use the most robust data sets possible. Normalizing and standardizing data prior to analysis is important to ensuring that modeling results are accurate.

Second, we must recruit, develop and retain data scientists and technologists who can work with the algorithms and validate their results.

Third, we must address concerns around data security and confidentiality. These can be thorny issues, but the industry is going to need to work through them to take full advantage of applied machine learning.

Interestingly, the technology that’s received the most exposure to date – distributed ledgers – is probably the most complex in terms of delivering client value. However, it’s also the one with the greatest potential to transform how the industry transacts and supports clients.

At DTCC, we’re leading one of the largest DLT initiatives to date – the re-platforming of our Trade Information Warehouse (TIW) – By moving it from a traditional database on to a distributed ledger and also leveraging cloud computing to enhance its scalability, optimize performance, improve flexibility, and reduce costs. This work is teaching us a lot about the technology’s potential and its limitations, and these learning will help guide us and others on how DLT can be used in other areas of financial services.

Similarly, as the industry continues to experiment with DLT, we all need to be thoughtful about how and where we apply it, recognizing that in some cases existing technology may still be the best solution. This is a critical point because our collective goal shouldn’t be to simply move current processes to a distributed ledger. Doing so is a wasted undertaking of massive proportions. Instead, we need to view the technology as a springboard to fundamentally rethink how to transform the post-trade ecosystem.

It’s impossible to predict how DLT will change market structure in the years ahead, but I can envision a future where the capital markets are more intimately integrated through distributed ledger systems; all data is captured and stored in the cloud; critical assets are digitized and natively reside on these networks; and networks seamlessly synchronize data across capital markets.

This vision of data synchronization would deliver client value through massive cost savings, risk reduction and by fulfilling the industry’s long-time goal of achieving straight-through processing from execution through settlement. In some ways, achieving this might be the easiest part because the transition from today’s technology, processes and market practices may be the most complicated and difficult task before us.

As we look to the future, there’s no doubt that technology will fundamentally improve many parts of the financial industry. To achieve this, financial firms must put aside their competitive spirits and work together to build the technology foundation of the future. The danger of working in isolation is that we will create a new disconnected maze of solutions, which will limit the enormous benefits that fintech can deliver. There’s many ways to support collaboration, such as supporting and participating in open-source organizations like HyperLedger Foundation and Enterprise Ethereum Alliance, to share our expertise and ideas. If we think and operate like this, I have no doubt we’ll enhance the client experience and drive greater client value.