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Deloitte banking alert – machine learning in financial services

4 noiembrie 2019

Financial services industry is increasingly powered by big data, new business models, advanced analytics and technology. Innovation is dramatically changing the markets and the nature of the financial system supporting it.

The UK regulators – Bank of England (BoE) and Financial Conduct Authority (FCA) – have taken a keen interest in the way that Machine Learning (ML) is being deployed by financial institutions. That is why they conducted a joint survey in 2019 to better understand the current use of ML in UK financial services.

The survey was sent to almost 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms, with a total of 106 responses received.

Key findings:

# ML is increasingly being used in UK financial services. The median firm uses live ML applications in two business areas and this is expected to more than double within the next three years.

# In many cases, ML development has passed the initial development phase, and is entering more mature stages of deployment. Deployment is most advanced in the banking and insurance sectors.

# From front-office to back-office, ML is now used across a range of business areas. ML is most commonly used in anti-money laundering (AML) and fraud detection as well as in customer-facing applications (e.g. customer services and marketing). Some firms also use ML in areas such as credit risk management, trade pricing and execution, as well as general insurance pricing and underwriting.

# Regulation is not seen as an unjustified barrier but some firms stress the need for additional guidance on how to interpret current regulation. The biggest reported constraints are internal to firms, such as legacy IT systems and data limitations.

# Firms thought that ML does not necessarily create new risks, but could be an amplifier of existing ones. Such risks, for instance ML applications not working as intended, may occur if model validation and governance frameworks do not keep pace with technological developments.

# Firms validate ML applications before and after deployment. The most common validation methods are outcome-focused monitoring and testing against benchmarks. However, many firms note that ML validation frameworks still need to evolve in line with the nature, scale and complexity of ML applications.

# Firms use a variety of safeguards to manage the risks associated with ML. The most common safeguards are alert systems and so-called ‘human-in-the-loop’ mechanisms. These can be useful for flagging if the model does not work as intended (e.g. in the case of model drift, which can occur as ML applications are continuously updated and make decisions that are outside their original parameters).

# Firms mostly design and develop ML applications in-house. However, they sometimes rely on third-party providers for the underlying platforms and infrastructure, such as cloud computing.

# The majority of users apply their existing model risk management framework to ML applications. But many highlight that these frameworks might have to evolve in line with increasing maturity and sophistication of ML techniques. This was also highlighted in the BoE’s response to the Future of Finance report.

In order to foster further conversation around ML innovation, the BoE and the FCA have announced plans to establish a public private group to explore some of the questions and technical areas covered in this report.

Questions for authorities

The report findings are consistent with the view that ML will be an important part of the way financial services are designed and delivered in the future. As a general purpose technology, it will potentially be used in areas critical to financial markets and the safety, soundness and conduct of firms. But it will also likely be usefully applied in areas that are not critical from a financial regulation point of view.

The task of the BoE and the FCA will be to continue monitoring the application of ML and identify ways to support the safe, beneficial, ethical and resilient deployment of the technology across the UK financial sector, as well as understanding its impact on the wider economy.

Firms are best placed to make decisions on which technologies to use and how to integrate them into their business. However, regulators will seek to ensure that firms identify, understand and manage the risks surrounding the use of new technologies, and apply the existing regulatory framework in a way that supports good outcomes for consumers.

Next steps

This survey constitutes a first step towards better understanding the impact of ML on UK financial services and forms the basis for a conversation around how safe ML deployment can be supported going forward.

Based on the survey findings and with an increased level of understanding, potential policy areas relating to ML will be explored. In order to facilitate this dialogue, the BoE and the FCA have announced they will establish a public-private working group on AI to further the discussion on ML innovation as well as explore some of the questions above and technical areas covered in this report. Repeating the survey in 2020 is also considered.

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Anders Olofsson – former Head of Payments Finastra

Banking 4.0 – „how was the experience for you”

So many people are coming here to Bucharest, people that I see and interact on linkedin and now I get the change to meet them in person. It was like being to the Football World Cup but this was the World Cup on linkedin in payments and open banking.”

Many more interesting quotes in the video below:


In 23 septembrie 2019, BNR a anuntat infiintarea unui Fintech Innovation Hub pentru a sustine inovatia in domeniul serviciilor financiare si de plata. In acest sens, care credeti ca ar trebui sa fie urmatorul pas al bancii centrale?