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The Evolution of AI
The Evolution of AI
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The Evolution of AI

26/04/2018

The explosion of big data has catalysed the arrival of AI

AI is not new, and everyone has always been interested in the potential of the technology - but for a long time it has been more fantasy than reality,” says Axel Pierron, co-founder and managing director of consultancy Opimas. It has been well documented that many organisations are burdened by legacy and manual systems that are expensive and unwieldy, having been built through years of acquisitions and organic growth. These have been increasingly buckling under never-ending streams of data, from traditional internal structured data to data from external unstructured sources – social media, emails and newly accessible government and third-party databases.

AI can offer a cost-effective way to manage and leverage vast streams of data, and has the potential to revolutionise the company–client dynamic. Nevertheless, despite its myriad benefits, without the necessary governance of the input – the data – AI technology risks becoming an expensive white elephant, with its benefits going unrealised.

In the field of securities services in particular, the evolution of big data is partly driven by regulation. Since the financial crisis, there has been a plethora of new regulation, ranging from Dodd–Frank to, more recently, MiFID II. Both buy- and sell-side firms have been forced to examine their organisational structures and look for innovative ways to collate their disparate silos of data, in order to comply with a host of transaction-and trade-reporting requirements in a cost-efficient manner.

“Regulation is definitely up there, in terms of a main catalyst behind AI in financial services; but, equally, a second factor has been the focus on reducing costs and improving return on equity,” says Monica Summerville, senior analyst at consultancy TABB Group.

AI can generate huge efficiency and quality gains through validation of data, providing proactive notification and realisation of patterns, identifying errors, and producing trade and transaction reports for compliance purposes.

The beauty of AI today “boils down to whether there is a human being doing a repetitive and manual task that can be substituted by a computer,” says Matt Hodgson, CEO of Mosaic Smart Data. “Take trade processing and reconciliations. The value of AI is that it will be able to match transactions with greater accuracy, and fill any gaps. However, it can also extract insights, identify flow and behaviour-pattern changes, and be used more as a diagnostic tool to determine what the issues are in particular areas and how they can be solved.”

AI will also be able to flag a problem before it happens – making automatic determinations that would previously have been made by a human. This can provide a firm with a system that can learn from past mistakes. An example of this would be taking six months’ worth of trade data with known, identifiable issues and using it to learn how to spot potential problems, as opposed to trying to define rules and processes for all possible scenarios.

Running before walking

The benefits of AI may be compelling, but caution is required. AI technology provides stepping-stones that must be selected correctly: it will only lead to the desired destination if it is applied to the right data sets.

Most firms need first to work on eliminating silos and normalising their data before they can successfully apply machine-learning and AI tools in a way that will allow them to extract meaningful insights and gain a definite competitive advantage.

This is a view echoed by Phillipe Ruault, head of Digital Trasformation, BNP Paribas, who emphasises the importance of identifying the business-use cases and the data required, as well as ensuring that there is a robust governance framework in place before taking the AI plunge.

It is no good having the most innovative technologies to help address the pain points and enhance customer experiences if the quality of the data is poor and uneven.

The customer hand

As Ruault points out, enhancing the customer or client experience is paramount. The role played by clients in driving forward the case for AI should not be dismissed.

Clients have become more demanding. They have been spoiled by the instantaneous as well as predictive responses from social media giants like Google, Amazon and Facebook, who have been at the forefront of using AI to engage with their end-client. With expectations driven up, these individuals now demand the same high-level service in their professional lives.

Unsurprisingly, many securities firms increasingly want to apply AI not only add value in the handling of data, but also to improve the customer experience to meet these growing demands.

As Axel Pierron notes, “There is a compelling case to use AI to provide new services and a better qualitative experience for clients, and to also offer advice. It will be difficult to differentiate, but I think it will be firms who can create new products such as operational benchmarks or advise them on distribution strategies that will have the advantage.”

“On a product level, it is how we can build a more robust big-data model that breaks down silos and uses AI and machine learning to add value and insights. However, differentiation is not just about being product-focused but also client-focused,” agrees Paud O’Keeffe, head of Client Digital Experience, BNP Paribas Securities Services. “As an industry, financial services have a long way to go before they catch up,” he adds. “However, they can do things to meet growing expectations, and not just on the regulatory landscape, to ease the burden of key pieces of legislation. We need to look at it from an innovation perspective, in terms of how we can better engage with clients. It is no longer simply giving them a black box, but co-creating or working together to find the best solutions to meet their needs. This requires a new way of thinking and changing the corporate and organisational structures, which is what we have done at BNP Paribas.”

With the right controls and strategy, the AI dream can become a reality, and its myriad benefits can be realised for companies and clients alike.

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