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AI to Assist Regulatory Compliance and Drive Transparency and Growth in an Uncertain World

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By Lucas Wurfbain, Co-CEO, FeedStock. 

The impact of the Covid-19 pandemic on the asset management industry has been transformative and wide-ranging. Increased market volatility and a large-scale move to remote working environments, alongside the ever-present uncertainty that comes with almost daily changes in government policy are now the new normal.

The regulatory environment has also been affected; the European Commission recently confirmed that its proposed Covid-19 recovery package will include removing unbundling research requirements for fixed income and small and mid-cap companies, alongside a suspension of best execution reporting requirements under MiFID II. These roll-backs will add to the complexity and cost of running asset management businesses, for example, those that straddle jurisdictions or invest across different parts of the investment ecosystem will now be required to further monitor research consumption in order to correctly classify which research they consume and under which component of the revised regulations these sit.

Increasing regulatory divergence between the EU and the UK?

During the public consultation, German and French regulatory associations voiced concerns that MiFID II has led to reduced quality in research. This is at odds with the FCA’s opinion that the regulation has removed duplicate research and created a focus on quality. Quite what this means for the alignment of the future regulatory environment between the EU and the UK as we enter a post-Brexit world is uncertain, but one thing is clear: change is a constant.

Managing change with growing complexity

For compliance leaders, the move to remote working and the changing regulatory landscape have created a significant increase to their reporting and compliance obligations. In addition, the increasing downward pressure on fees and the turbulent market environment have further exacerbated this issue, meaning that an ever-growing compliance burden can’t be pushed to the already time starved fund managers and analysts.

Companies need the right tools and resources to address this changing landscape and to embrace a technology infrastructure that can adapt to provide the necessary insights for smarter, rapid decision making. To ensure that the asset management industry can respond quickly and effectively to these increased levels of complexity, both now and in the future, it is increasingly important to build a responsive and adaptive organisation.

A strong case for evidence-based research fee validation remains…

Asset owners should still push for evidence-based justification for research payments, regardless of the regulatory environment. Done correctly, such as by leveraging automated, AI-driven data capture, it can reduce overall costs by as much as 50% by focusing on research that delivers value to the business, efficiently and automatically. By instantly classifying interactions with Natural Language Processing (NLP) technologies, asset managers can build a data-driven, qualitative view of what is being used, and provide an evidence-based justification of research fees.

…but strive for more than regulatory focus to gain operational insight and more informed enterprise decision making

Using an enterprise data view to get insight and justification for research fees is not the only positive outcome for complex institutions looking for ways to manage the challenge posed by the exponential growth to unstructured data. Technology systems that can automate and streamline the workflows surrounding regulatory requirements, refining research fee justifications, while strengthening investment processes and business operations are ever more essential. Using technology purely for compliance has had its day: it also needs to also provide value-generating business intelligence in the modern era.

FeedStock, for example, has developed AI and NLP technology with a multilingual model to classify financial research, email and chat activities into more than 30 different categories and extracting more than 50,000 different entities using its proprietary Named Entity Recognition (NER) models. Additionally, the firm now deploys sentiment analysis models to extract instant, value-generating business intelligence which would otherwise remain hidden.

Leveraging automated AI-driven data capture solutions is increasingly essential in today’s data-driven environment. These new approaches not only enable businesses to get a more accurate dataset above and beyond the error prone data generated through historical manual inputs, but also a far more comprehensive dataset that shines a light into areas of operations that were historically considered impossible to truly analyse.

In this difficult economic environment, it is more important than ever to maximise operational efficiency by harnessing the power of cloud, AI and NLP technologies, automating non-core tasks to minimising compliance risk and costs and gain insight into business operations for smarter decision making.

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