AI: Is the back office ready ...?

It’s hard not to see that the advent of artificial intelligence (AI) signifies a wave of advancement for financial markets. The enhancement of front-office operations through superior decision-making, process optimisation, and efficient trade execution has been lauded as the most important advancement in trading in the last decade. This technological leap, however, raises broader concerns in the back office, where AI poses challenges in handling and processing new patterns of behaviour.

A catalyst on back office advancements

AI certainly comes with the expectation of great returns, but it will also expedite the volume of transactions and communications for back-office systems. The increased speed and complexity in front-office operations, now more than ever between asset classes, markets and jurisdictions, will necessitate a re-thinking of how back-office systems are deployed as well as their resilience, stress testing and overall governance. Many vendors are already delivering tools to help sift and prioritise alerts in trade surveillance, seek out anomalous patterns of behaviour and optimise credit; however, incumbent technology is often limited by its ageing architecture and firms should ask tough questions of their providers, align with their roadmaps and ensure resiliency is at the forefront of future plans.

Preparing for a new era of message growth

A further concern is understanding and monitoring the interoperability between AI applications in both the front and back offices. A significant upsurge in transactional volumes and trading complexity could easily strain less advanced infrastructures, potentially resulting in processing delays and inaccuracies in post-trade operations, risk management, and regulatory compliance. In an industry where T+2 settlement is still a thing (!!), it’s hard not to see that the back office could easily be left behind.

Furthermore, in a similar way to how some algos are known to ‘game’ each other, LLMs could generate perpetual loops, price spikes and market contagion, further necessitating the deployment and active monitoring of kill switches.

Interoperability and Systemic Robustness

It is imperative that firms focus on fortifying all platforms, ensuring risk systems are capable of handling message spikes, rapid changes in strategy and dynamic benchmarking. This will involve adopting unified standards and protocols that facilitate unimpeded communication and data exchanges between front and back-office AI applications. Furthermore, as capacity stress testing becomes more paramount, so will the interplay with cloud providers and the elasticity and backup provision they can provide - maintaining operational efficacy.

Monitoring our AI cousins

It’s hard not to end this article without a nod to the duality of human interaction alongside our new AI cousins.

Regulatory bodies must stay abreast of technological advancements and consider how to police AI tools. This may involve setting standards for AI implementations, ensuring transparency in AI-driven decisions, and mandating periodic reviews and audits of AI systems. By doing so, regulators can ensure that the integration of AI into financial markets remains a benefit, not a risk, for the industry's stability and integrity.

The growth of AI into both front and back office systems is inevitable and poses significant advantages as well as complexities. As AI continues to reshape the financial landscape, firms should remain vigilant, revisit their system architectures and consider the implications in terms of interoperability as well as the risks, both internally and externally.