Applications for artificial intelligence in securities finance and collateral management

Following numerous hype cycles, artificial intelligence (AI) is now gaining widespread adoption, with the potential to radically transform many areas of the financial services industry.

Many of the large tech firms are now making their AI platforms available as open source products, for example, IBM’s Watson and Google’s Deep Mind. This democratises the ability to build solutions using AI engines and massive computing power offered by these firms, and will result in an explosion of new AI based solutions in the coming years.

AI thrives on large amounts of data. In our industry, securities finance trade reporting under SFTR and derivatives transaction reporting under EMIR may result in structured and tagged data in large quantities.

This could provide an opportunity to apply cognitive computing algorithms against SFTR data. However, this will depend on whether the depth and timeliness of publicly available data allow for an interpretation of market trends in a way that can guide future decision making.

This article covers the following topics:

  • What are the latest advances in AI in financial services?
  • Why this time is different than previous technological breakthroughs
  • Potential applications for AI in securities finance and collateral management
  • Will AI replace humans and what are the limitations of AI?

What are the key latest advances in AI?

Driving recent advances are three key underlying developments:

  1. Larger data sets
  2. Cheaper processing power and storage that facilitates faster learning
  3. More sophisticated algorithms

In the past few years, there have been major leaps forward in the ability of machines to perform certain tasks better than humans. For example:

  • JPMorgan Chase introduced a system using its Gaia machine learning and big data platform to review commercial loan contracts. Work that used to take loan officers 360,000 hours can now be done in a few seconds.
  • Speech recognition error rates fall from 8.5% to 4.9% in the space of one year. Speech recognition is now three times as fast as keying in text on a cell phone.
  • Image recognition error rates drop from over 30% in 2010 to around 4% in 2016.
  • IBM’s Watson supercomputer begins to run an actively managed ETF using AI – the Equbot with Watson AI Total US ETF
  • Quants at Goldman Sachs claim to have reverse engineered and rebuilt some of the risk premia components of hedge fund strategies. This includes arbitraging M&A activities and making bond and currency market bets. The firm is now selling these strategies at a fraction of the cost of traditional hedge funds through its Alternative Risk Premia and Hedge Fund Beta funds.

Why do these advances represent a step change from previous AI technology – why is this time different?

The developments described above represent a revolutionary rather than evolutionary change in the capabilities of Artificial Intelligence. Rather than humans explicitly programming machines to perform tasks, machines can now learn from examples.

This means that a machine can keep improving on its performance without the need for humans to detail exactly how to accomplish the task. This represents a significant change from past practice.

The main areas of artificial intelligence can be categorized as follows:

  • Image recognition
  • Natural language processing
  • Robotic process automation
  • Machine learning
  • Predictive analytics

What are the applications for AI in securities finance and collateral management?

The recent advances in natural language processing and predictive analytics/deep learning lend themselves to significant automation of a variety tasks currently performed manually.

Areas that could be automated though the use of natural language processing include:

  • Legal agreement electronification
  • Regulatory analysis
  • Reconciliations and disputes
  • Client and counterparty communications

We could also see the advent of more voice based control and use of virtual assistants. For example, a collateral manager could start her day by asking: “Alexa, how much additional collateral do I need to source to balance my portfolio today?” From there, the booking of collateral movements and other tasks could also be controlled by voice.

Areas of the securities finance and collateral management process where predictive analytics (the use of large datasets to better inform decision making) could be deployed include:

  • Trading strategy, transaction pricing and risk management
  • Collateral optimisation, stress testing and liquidity forecasting
  • Counterparty credit risk analysis

Limitations and challenges around artificial intelligence

AI solutions are currently able to perform some tasks better than humans. For example, beating the world’s best human player at Go. However, AI currently has a very narrow intelligence compared to the far broader intelligence of humans. AI systems are currently very specialized around a single specific task. The AI solution that beat the world’s best Go player is unable to pilot a driverless car or even play a game of checkers. Emotional intelligence, empathy and creativity are other areas where artificial intelligence still lags far behind human performance.

The black box nature of machine learning, from which it is very difficult to determine the exact steps an AI system took to reach a certain outcome, raises major issues around audit trails. Auditability is a key aspect of governance for financial institutions, and this could increasingly become a problem that drives significant regulatory scrutiny of AI in the future.

This lack of visibility in terms of process also makes error identification extremely difficult in the event something goes wrong, for example, during the flash crashes that have occurred in recent years.

Because of the factors above, AI will therefore most likely become another useful tool to enhance human trading activities rather than result in the complete replacement of humans in financial services.

Rapid uptake may be ahead as regulations implemented

The use of AI tools presents interesting possibilities in securities finance and collateral management. In conjunction with an increasing amount of data available to the industry, there is potential to create new applications that reduce the time spent on mundane day to day tasks and improve strategic decision making.

In the next few years, market participants will largely finish implementing the recent round of regulatory initiatives. This is when we could begin to see more rapid and widespread uptake of these technologies as a way to achieve competitive advantage. This will also coincide with the maturing of AI tools for a wider range of business applications.

While this will entail change for human traders, collateral managers and operations personnel, it is important to note that machine learning systems rarely replace a person’s entire job or a full end to end process. The successful AI solutions of the future will combine the strengths of both humans and machines to perform tasks more effectively than either of them could do individually.

Martin Seagroatt is the marketing director for Broadridge’s securities financing and collateral management business unit. Previously he was marketing director at 4sight Financial Software. In June 2016 4sight was acquired by Broadridge and the 4sight Securities Finance and Collateral Management System rebranded as the Broadridge Securities Financing and Collateral Management Solution. In his 12 years in the industry he has specialised in securities lending, repo and OTC/listed derivatives collateral management solutions. Prior to that Martin worked as a business expert in technology systems for risk management in the energy industry.

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