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Compliance and AI: greater efficiency and quality

Compliance and AI: greater efficiency and quality

At a time when regulation is becoming increasingly restrictive for financial institutions, how can the Ai help improve communication between supervisors and financial institutions?

Compliance has always been based primarily on people. The regulatory burden for financial institutions over the past decade has steadily increased, resulting in a sharp increase in the demand for compliance professionals.

To address these challenges, companies have had no choice but to recruit more and more compliance staff to address the growing regulatory constraints.


Making use of new technologies essential

However, this has not reduced the number of fines issued by supervisors. New records are broken with each new regulatory breach identified at a Financial Institution (FI). As the limits of human beings have been reached, the help of technology is therefore necessary.

Financial institutions and regulators have understood that by harnessing the power of technology, especially artificial intelligence (Ai) and the learning machine, a considerable part of the compliance function can actually be automated. As a result, regulatory constraints for compliance institutions and professionals have been reduced.


Ai issues for the financial sector.

Ai issues for the financial sector are vital. We must not fall behind. In response, the ACPR launched a public consultation in 2018 (see responses to consultation) with various actors (banks, insurance companies, fintechs, foreign central banks).

This consultation focused, among other things, on the issues facing supervisors such as the governance and explanability of algorithms or the advantages of artificial intelligence techniques for the exercise of their own missions through the use of technology.


What communication between supervisors and financial institutions?

In this context, we can consider the relationship between supervisors and financial institutions (FIS) to be essential for the stability of the financial system. The exploitation of Ai in the RSS is therefore a major element of this relationship.

It can help FIS comply with their regulatory requirements by reducing reporting rules that are dependent on human interpretation and that can be sources of error.


The beginnings of an evolution: the English Digital Regulatory Reporting System

In practice, such a project has already been carried out in the United Kingdom. It was led by the Financial Conduct Authority (FCA) in collaboration with Bank Of England and a number of financial institutions. The project is called the Digital Regulatory Reporting (DRR).

This pilot program designed to evaluate the benefits of machine-readable reporting and explore how technology (recurring neural networks and the semantic web) can help financial institutions in their dealings with regulators.


A complex context

In the UK, manual reporting is a very heavy and complex task for the supervisor, with over 20 000 rules and 58 000 companies. This number is constantly increasing. Financial institutions must also interpret the necessary rules and data on their own.

For example, when the supervisor asks for data, differences in interpretation often lead to confusion or even inefficiency. This has quite expensive consequences in terms of time and money.


What are the aims?

The overall objective of this project is to reduce the time and costs required to interpret and implement the new reporting requirements. The other targeted objective is to reduce the number of individual regulatory reports that companies must produce.

To do so, FCA has explored how a machine-readable regulatory framework can interact with standardized language mapped to source data. It uses semantic web technologies to identify the most appropriate approach to data specification.


A fleeting but functional automation

FCA has already demonstrated that the concept works well. The regulator announced that it had successfully applied automatic reading technology to two different regulations. One based on capital requirements and the other on mortgage lending criteria.

Looking ahead, FCA plans to expand the scope of the project in 2019 and apply the technology to more regulations, which is an interesting development for the sector.


What conclusions ?

Communication between supervisors and financial institutions is a complex issue. On the one hand, regulations are numerous, complex and sometimes subject to interpretation. On the other hand, taxpayers are obliged to submit numerous and detailed reports.

Manual management is reaching its limits. The need for RegTech / LegalTech that uses Ai-based and machine learning technologies is becoming vital.

CFA experience, even if limited, proves that this can be achieved. Of course, human intervention is essential for validation. The Ai avoids all repetitive tasks with no added value. And it allows the human to concentrate on tasks with higher added value.

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