It is an interesting question because there is a broad spectrum of approaches across organisations. It fluctuates between those organisations that are leaning in and are on the front foot and those that are watching carefully and are engaged but they have not yet put all of their chips on the table. There are also organisations that are unsure of how to start and how to enable themselves and I think there’s a humility that comes with that. 

There is an interesting dynamic within AI where it is accessible to all of us, but challenging to implement within financial services. As much as there are some really exciting things that we can do with AI, in financial services we have a lot of regulation and compliance that we need to think about.

Culture within firms is also a significant factor. AI is not one team’s problem to solve, you have to get a community really working together for a truly holistic approach. 

We are starting to see a change in terms of the demands of the end users and in their actual way of working, which is impacting the character of these previously very tightly defined personas. I think that is going to have a really interesting effect in financial services.

How feasible is AI use when it comes to trading?

One of my rules with AI is that if you have to explain it and you have to repeat it, maybe don’t use it. The problem with AI is that it is a probabilistic model so if you ask the same question twice, you’re potentially going to get a different response.

That of course doesn’t mean it’s not valuable, but it means that you have to think very carefully about which use case you are solving for. So, when it comes to traders, this is really fascinating because it comes down to trust – if a trader was like a magic box and every time you shook it came out with a different answer, would you trust it?

Traders are essentially accountable, so the idea that people are going to use AI and potentially get different outcomes that they can’t fully explain in these high-risk tasks seems implausible.

Furthermore, the idea that regulators are ever going to get comfortable with AI giving the best answer only “most of the time” is highly unlikely. For that reason, I think traders are going to be around for a while.

What is meant in practice when the industry says we must adopt AI in a responsible way?

When it comes to responsible AI, the key is trust. One has to delve into what it actually means to be responsible – essentially, if you’re using AI enabled processes, can you trust it and what does it mean to trust something?

One aspect is knowing where the data has come from, another is checking for biases and establishing the ethics of models, and another is choosing the right model for the right task and taking responsibility for that selection.

At the end of the day, we are now responsible for products that are AI enabled, so a big part of that responsibility is reinforcing it, through a secondary model and maybe a tertiary model. In the event that something catastrophic happens, we must have that ability to say, ‘not a problem, we have a back-up, we’re going to switch to something else that is more suitable and continue to uphold the status quo’.

How important is that holistic approach to AI?

What’s important to note – aside from teams coming together to approach this at the same time – is the fact that AI came from outside of financial services and is now being imported into financial services. We are seeing examples in other regulated industries of how people are implementing AI which has the potential to be hugely transportable and relevant when we think about financial services.

I believe that this is going to have a dramatic effect on the way that financial services operate because in the past we’ve seen financial services very much segregated from everything else and tightly defined. That is now starting to change, and we are starting to see cross industry collaboration. 

Interestingly, we are not only focused on the research and investment in AI within financial services. As a community, we are beginning to see the potential relevance of AI investment within other industries. A part of this is greater mobility of  talent from non-finance industries, bringing relevant new perspectives and skills.

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Historically, the technology and processes to support manual data reconciliations have been very much geared towards addressing challenges around sharing the data, looking at the data and then using very linear technologies over the top. But a series of trades doesn’t simply add up to a position. It’s a very simple statement, but an important one.

This is compounded by a couple factors -the increase in transaction volumes and more complex assets being transacted. This is leading to frequent errors (due to different systems and non-linear processes) resulting in a costly mess of point-to-point reconciliations and mistakes.

We really need a very different approach than the one that exists now. One long-term solution to this is tokenisation and digitisation, where everyone is looking at the same record. But even then, that’s not enough on its own. The optimum state is having the ability to make a summation of those records by using non-linear enabled technologies which can be aligned to clear business processes.

How can new data management technologies help reduce operating costs?

It’s important to note how new data management technologies can not only reduce operating costs, but also reduce risk. This means individuals can be granted permission to access only the specific data they need therefore ensuring compliance with market data licensing and regulatory obligations while being authorised to perform the right action. Therefore, creating a safer environment: people can then see the bits of the puzzle they need for their specific role, without necessarily seeing all of it.

Again, the issue with the push towards tokenisation and digitisation, means a lot of the public blockchains have access to everything. So that’s not quite the right solution. It’s about finding the balance which reduces duplication, synchronisation issues, translation problems, and breaks, while maintaining the controls required for financial services.

What is the key driver behind the push for real-time data sharing?

It’s largely driven by the increased sophistication of the ultimate capital allocators. If you look at sovereign wealth funds and pension funds, as a macro trend, they’re starting to insource more of their own risk management. In order to do that, they need to look at the same data.

So, they’re starting to ask their managers and their custodians and administrators for data that’s at the same time point. When quick decisions are necessary, relying on data that’s 90 days old here, 30 days old there, or even just a few days outdated can hinder the process. The goal is to have all relevant information in real time to enable better, more informed decisions on a macro level. We’ve seen that trend an awful lot more where the end capital owners are becoming much more sophisticated in their approach.

What are the main pressure points for asset managers when it comes to their operating models?

There’s a change in the asset mix with the end allocators wanting both public and private assets. There is also a change in the demand for data with asset managers wanting all their data in real-time. For allocators, fee compression is a significant concern. As more machine learning, AI, and automation are introduced, they may ask, “Why should I pay 1.5% or 2% in fees when I could be paying just 0.5%?” Additionally, with interest rates staying relatively high, investors may question, “Why take on risk to earn 7% when I can get 5% or 6% from government bonds – and avoid paying you a 2% fee?”

This combination of factors has created a perfect storm: capital flight from traditional managers, pressure to lower fees when they do secure capital, and an increased demand for complex asset mixes. All of this contributes to a significant challenge for the profitability of asset managers.

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At a high level, I think that most teams are pretty excited about the prospects of AI and the opportunities that it presents. Certainly we see very good usage among our teams. Despite this, there are certain concerns, understandably, and hallucination is certainly a topic that people are aware of. But at the end of the day, before trust comes education. It’s about helping folks understand where these models can add value right now and where they can’t.    

A lot of the wariness also comes because AI models are known not to be good at certain tasks and this taints opinion when it comes to others. For example, AI models are not good at math – that’s just not what they’re trained to do. The key is to educate [our] internal users and explain that they shouldn’t get spooked because, when it comes to the analytical path, the potential is there.   

How is AI already having a positive effect on trading processes? 

The traditional ChatGPT system, similar to our design internally, is focused on quick questions and quick answers. It aims to save people 20 to 30 minutes, on average. The deep research system we are working on allows one to rigorously research complex questions for results that can potentially save people 3 to 5 days.

AI has the ability to unlock a lot of productivity for investment professionals, allowing those within capital markets to discover and digest information even faster than they were previously able to do.

You could either be the fastest or you can be best, and we all want to do both but there are a lot of factors to consider.

How are firms across the industry adapting their teams to be more AI-focused?

In the future, our firm, and others, will want every team to effectively be an AI enabled team. At this point in time, however, what is of most benefit to organisations is to take a centralised approach. We have a team that is dedicated to AI as a centre of innovation – with technical expertise, doing research and building tools, but as importantly, looking for opportunities within the firm.   

A lot of my role and the role of my team is connecting the dots and finding commonalities across teams at Balyasny. If something is working really well in one team or one vertical, it then becomes about translating that. It’s never a direct translation but taking the lessons about what’s working well somewhere and then adopting it somewhere else allows us to achieve more scale within the firm.

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As payments become more critical for large banks, we are noticing changes in settlement volumes, liquidity pressures, and operational challenges linked to market volatility. Typically, higher trade volumes increase risk due to volatility and intraday liquidity pressures related to margining and settlements. Additionally, the rise in trades exacerbates operational issues, such as mismatches, breaks, and strain on affirmation processes when handled manually.

There are both positive and negative aspects to consider. Increased volumes create revenue opportunities for firms with the technical capabilities to manage risk, intraday liquidity, and the additional operational demands. Real-time visibility and control are crucial in this context, exposing the limitations of legacy systems and processes. Real-time views of exposures, obligations, account balances, and reconciliation, as opposed to delayed processes, coupled with anomaly alerts, credit line usage, and counterparty risk parameters, become key differentiators for financial institutions.

How are firms adapting to T+1? Has the response been better than expected?

Firms have been actively preparing for the shift to shorter settlement cycles for US equities and bonds, driven by the need for greater efficiency and reduced risk in an increasingly unpredictable market. Many institutions are investing in process re-engineering to enable faster settlement times. We consider these changes “necessary but not sufficient” for a firm to declare success. We anticipate that settlement and RTGS systems will globally move towards T+0 for most asset classes within the next five years, further increasing pressures on risk, liquidity, and operational challenges.

We are heading towards a world where, for example, a fund in Asia must meet obligations in EUR, GBP, and USD for trades executed on behalf of clients seeking exposure to these markets. The settlement window may be just a few hours. They will need to conduct an FX transaction efficiently, settle the currencies soon after the trade, and use the proceeds to settle the next leg. In this scenario, back-office systems and current settlement venues, including CLS, will not be sufficient. Relying solely on custody banks would lead to suboptimal FX rates. Firms need to prepare for these market changes. Systems that offer real-time visibility and control will be critical and key differentiators for market participants.

What are the more pronounced bouts of market volatility challenging current liquidity management processes?

This summer has seen considerable market instability, primarily driven by geopolitical tensions and divergent monetary policies. These events have increased risk-taking in complex financial products, directly impacting companies’ risk exposure, settlements, and operational workload. Three factors exacerbate the problem: global market interconnectedness, which spreads instability quickly; the increased trading of riskier, more market-sensitive financial products, and shorter settlement times for cross-border transactions, requiring faster payment processing.

Liquidity pressures have been particularly severe in two key areas. Firstly, emerging market currencies have long struggled with liquidity, prompting businesses to stagger payments and schedule transactions to avoid exhausting available funds. Secondly, even in more stable currency markets, intraday liquidity remains a challenge. With interest rates still elevated, it is crucial for companies to maintain clear visibility of their balances and access collateral-backed funds. This creates opportunities for banks that manage cash reserves effectively. The current market for intraday short-term lending and currency exchange remains in its infancy, but we believe this area will grow significantly due to strong demand.

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Gen-AI is far more than just a trendy term; if you stop to consider it, it’s akin to a significant technological revolution, comparable in magnitude to the advent of the steam engine, electricity, computers, or the internet.

Generative AI enables the provision of on-demand intelligence, which is especially crucial in finance. We believe that early adopters will gain a significant competitive edge, pioneering the application of this technology. 

How is adoption across the industry coming along? 

From a technological standpoint, what’s exciting is that the entry barriers are quite low, while the potential outcomes can be remarkable. Models are constantly improving and becoming more cost-effective. This technology can be leveraged to enhance current workflows, or it can even inspire the creation of innovative working methods that result in exponential productivity growth.

The capability to customise the model with your specific data and train it to perform in a tailored manner will be critical.

What’s the best approach when it comes to implementing this technology?

I believe the initial step should be to focus on governance: establish a concise AI manifesto that clarifies acceptable applications of the technology. For example, guidelines could include maintaining a human-in-the-loop approach (humans should be the ultimate arbiter of any AI-based decisions); restricting Generative AI use to internal purposes only; and avoiding the incorporation of Generative AI into client interactions, trading activities, or any applications that might directly impact market prices. 

The second step should be ensuring you have a flexible platform that is model agnostic to allow for easy transitions between LLMs like Gemini and ChatGPT. At this stage, it’s important not to become overly reliant on a single provider. Our aim is to provide access to a Generative AI to all our staff, enabling them to engage with the technology in a data-protected setting and contribute innovative application ideas.

The third step, which is a bit more complex and what we’re currently focusing on, involves feeding the model with our internal data and training/fine tuning it to address our specific use cases. 

Has market sentiment towards AI changed?

It’s clear that there is a palpable sense of apprehension among many, and their concerns are valid. It is crucial to acknowledge those fears because of the inherent risks associated with this technology, which underscores the need for clear guidelines to manage it effectively.

In the future, there’ll be a distinct division between entities that superficially engage in ‘AI washing’—perhaps simply automating their meeting minutes or other basic tasks—and those who harness GenAI to its full potential, employing it to analyse sentiment, ideas, and conversations. It’s in these areas that GenAI will truly stand out, providing a valuable enhancement to the conventional quantitative analyses focused on pricing and financial data.

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The new rule changes published by the SEC have been primarily driven by the need to enhance market stability and reduce systematic risk. They will bolster the security of the US Treasury market by mandating central clearing for eligible securities, such as repos and reverse repos, inter dealer broker transactions and other cash transactions. In doing so, these rules are intended to reduce counterparty risk, limit contagion and increase transparency in the market.

The lessons learnt from past financial stress conditions and crises, particularly those involving non-bank market participants, have driven these changes. One counterparty defaulting could pass risk on to another party, this in turn could have a cascading effect on liquidity across the market. In addition, currently, the Fixed Income Clearing Corporation (FICC) is indirectly exposed if one of its members makes a trade with a non-member and subsequently defaults on the transaction.

With these changes, there will be a dramatic increase in the amount of daily US Treasury clearing activity processed through the FICC.

What aspect of the proposals should institutions be most conscious of?

Primarily, institutions must be aware that the SEC has introduced a regulatory change to redefine what constitutes a ‘dealer’. This is done to increase oversight of Proprietary Trading Firms (PTFs), which play a significant role in providing liquidity in the US Treasury market.

PTFs engage in trading with their own capital rather than on behalf of clients and will have to register with the SEC and FINRA as dealers. If PTFs do not want to register as dealers, they must establish a Sponsored Member arrangement.

While this is a significant change for PTFs, they already have experience delivering similar large-scale projects following the change to the T+1 settlement in May 2024 which can be applied to the upcoming dealer redefinition and central clearing changes. 

How will fixed income trading desks be impacted by these proposals?

Central clearing is likely to help ease counterparty credit limits due to better risk management and transparency provided by the CCPs and migrate previously uncollateralised bilateral contracts to the CCPs. In particular, counterparty default and fire sales risk should be much lower.

This could improve market liquidity by removing existing trading restrictions and mitigating counterparty and bilateral trading risk. This will be particularly beneficial in times of stress, as these factors will ensure that dealers don’t withdraw liquidity.

At the same time, the cost of central clearing and risk management activities will likely increase the overall costs of transactions for participants who don’t currently centrally clear transactions. These costs will be passed on from FICC members to non-FICC members.

Similarly, highly leveraged or low-margin trading strategies, like basis and relative value trades, may become uneconomical as a result of these proposals. This means that less PTFs will trade them, leading to a reduction in liquidity on the underlying asset classes, such as US Treasury actives and this is likely to counteract the previously mentioned benefits.

Are any specific strategies being developed to accommodate these changes?

For existing members of the FICC, providing sponsorship services to non-member firms represents an opportunity that could lead to significant revenue for the FICC members, as they can charge fees for this service.

Sponsoring Members could also benefit from collateral netting between them and Sponsored Members. This strategy reduces the amount of collateral needed overall by offsetting obligations against each other and achieves economies of scale on collateral management, aggregating across several balance sheets.

Another strategy being developed to accommodate changes is investment in technology. This is largely to mitigate the costs of central clearing. This includes investment in scalable transaction reporting systems, which reduce the need for manual intervention, minimise the risk of failures and reduce the marginal cost per transaction. Overall, investing in technology will make it more economical for firms to comply with the new rule changes.

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The emerging markets fixed income sphere is recovering and we expect inflows back to the asset class after years of investor exodus. The combination of Fed tightening, China uncertainties and the war in Ukraine has been a perfect storm for emerging market debt and we anticipate a rebound across all sub-asset classes: FX; rates; EM sovereigns and corporates.

We believe that dollar strength is largely behind us as the Fed embarks on a rate cutting cycle. Consequently, EM currencies should recover. EM credit remains relatively cheap compared to developed markets, while the rates outlook appears benign as many EM central banks have either started or will follow the Fed in their easing cycles. We also see plenty of opportunities in the special situations space after years of dislocation.

What key factors are affecting flows into the area? 

Inflows to the asset class should be supported by the Fed easing cycle, resilient fundamentals in EM versus DM, and US election risks subsiding towards the end of the year. Given the sound technical factors and the structural under-allocation to EM debt by large investors, we believe that inflows could be significant. For a sizeable and diverse asset class, EM debt is extremely under-owned. 

Which regions are at the fore of the firm’s focus, why? 

Asia is increasingly dominating the investment landscape, with a vast and diversified credit space, and India benchmark inclusion on the local currency debt side. We are finding some very interesting opportunities in special situations within Asia. There could also be some intriguing alpha opportunities in less liquid and under-researched frontier markets, across both hard and local currency markets.

How is your firm shifting strategies to maximise opportunities in the space?

In a general sense, the asset class has the capacity to provide high returns alongside diversification benefits. We believe it is a particularly good fit for active investors with a contrarian slant. EM investment styles and processes are becoming increasingly bifurcated between large passive or quasi-passive investors and very nimble ones that can exploit a volatile environment and sudden changes in flows or investor asset allocations. 

Why are EM bonds set to be important for desks going forward?

While EM debt is likely to enjoy strong tailwinds overall, volatility is here to stay and having the right skillsets for each area will be key to success. Indeed, EM is a mosaic of sub-asset classes rather than a unified universe. It requires very different skills to trade EM FX versus rates or credits as the liquidity and price discovery mechanisms vary markedly. Desks that are designed to be nimble and opportunistic should be able to provide alpha through skilful execution.

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One of the biggest themes in ETF evolution over the last few years has been in the increase in scope of products offered to the market. With increased competition, new launches are now targeting gaps in investors’ universes with more specific products. This has been witnessed across asset class as well as developments in actively-managed funds being launched in an ‘exchange traded’ wrapper. Some more recent examples would include funds tracking crypto, ESG or other fundamental factors such as quality or income. 

This increase in product type has widened the pool of prospective investors – both institutional and retail – and the competitive nature of the industry has seen costs reducing, which in turn brings ETFs to more investor’s attention. This expansion in both client-base and products has led to a rapid growth is assets and trading volumes.

Consequently, ETF trading techniques have evolved to source new pricing opportunities either via electronic Request For Quote (RFQ) platforms and/or ETF algos and to take advantage of ETF trading provision at the exchanges themselves. We have also seen traditional ETF liquidity provision firms moving into forming bilateral relationships with buys-ide dealing desks which has further strengthened ETF pricing.  Finally, there has been significant innovation in the ETF post-trade analytic capabilities for traders – either developed inhouse and/or utilising third party solutions creating a deeper understanding of implementation costs which can be used to improve future trading outcomes.

What role are active ETFs playing in the progression of this asset class? 

When the investment backdrop becomes more challenged, investors have to take greater consideration of investment risk rather than simply buying market exposure (beta) in order to generate positive returns. Actively-managed ETFs complement a passive approach, by providing access to specific investment processes designed to achieve specific results such as index outperformance, income generation, or factor tilts such as quality, duration or yield, all while maintaining the attributes of the ETF structure. 

Active ETFs have been released across different asset classes and have appealed to new and old ETF investors alike as they provide middle ground between passive & active investing. Through active ETFs, managers are able to offer access to internal intellectual property and house expertise such as bottom-up stock research, allocation weightings etc that not only differentiate their product but can help investors generate alpha for a portfolio alongside core passive holdings.

What are the impacts of fragmentation linked to ETFs? 

The most obvious impact of fragmentation has been on the perception of an absence of secondary-market liquidity. This has mostly likely, held back some adoption of ETFs from investors but has also led to increased innovation from all market participants to source, aggregate and efficiently price ETFs. RFQ platforms have taken the lead for traders when seeking risk prices as they reduce the opportunity cost of requesting prices from multiple liquidity providers but in an information-controlled manner. Traders have also adopted more dedicated ETF trading algorithmic strategies launched to empower dealers with the ability to access a wider range of liquidity pools at differing urgency settings which alter the child-order placement logic, often within fair-value frameworks.

On the opposite side of the trade, market makers and liquidity providers constantly search to improve efficiencies within their processes to better utilise their balance sheets to offer tighter pricing and/or greater liquidity. Fragmentation, however, means that market makers have to disperse their liquidity among multiple exchanges and trading venues reducing the volume available in each. Additionally, the costs associated with post-trade fragmentation (Central Counterparty Clearing & Central Securities Depositories) further reduces the cash market makers can commit into the market, instead having it tied up to satisfy post-trade provisions.

What can be learnt from the US? 

Aside from the simpler US ETF market ecosystem, the European ETF market would greatly benefit from increased retail ETF adoption and participation as seen in the US. Technology firms are looking to help platform providers streamline the ETF process, reducing complexity where platforms may currently struggle due to legacy systems, in order to increase/improve the ability of platforms to offer more ETF trading to their clients. Increasing adoption of ETFs from the retail community combined with improved connectivity from platforms to exchanges creates opportunities for buy-side dealers to interact with these improved volumes on exchange as professional and retail volumes create a better dynamic for orderbook trading.

Improvements could also be made in financial education – from school age through to adult investors. With greater demands being placed on individuals’ long-term savings capital, a deep and sound knowledge of all financial products would encourage people to take more control of their investment solutions at an earlier age and low-cost ETFs are well placed to form part of their investment toolkit.

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Every time we execute it’s stored in a database where the portfolio managers (PM) have access as well so they can see themselves how trades have gone. There is a certain overlap where traditionally traders and PMs have been quite segregated.

There is segregation of duties of course, but we believe that the data should be shared as much as possible for transparency. Everyone can then see how a trade performed, detail implicit and explicit costs or how long each stage in the process took. 

We also share group team chats with each of the PM groups, so we’ll be their eyes and ears while they’re traveling, with companies or doing research, as often its unproductive for them to follow real-time price movements. If anything happens, any big moves or relevant news at all we flag it into the chat and that’s how we kind of share the role. We let them know any orders we have going, how it’s going in real-time and then they can see it afterwards in the database as well.

Basically, we’re trying to ensure that both sides have enough space to do what they’re best at. 

What has influenced this set-up? 

I think it’s the firm’s flat structured approach that enables this collaboration in how PMs and traders interact. I know for us it works to be as close as possible and obviously to work in tandem. With the proliferation of outsourced trading firms popping up, it is more important than ever to demonstrate the additive value of the desk showing the difficulty another firm would have to effectively play the role of an in-house desk.

Perhaps for some firms that have particular regions, certain trading types or a larger investment universe with generic benchmarks to track could certainly outsource a portion of it in order to meet their needs.

Has this collaborative always been the case? 

It has certainly gotten more and more that way. It has evolved quite organically and we’re always looking to improve it, but it’s probably been this way for the last five years. We’re stock pickers investing in quality growth companies, so our universe is quite concentrated and it really requires that  hands-on approach rather than relying on automation. 

We’re often trading single names, and we’re very careful about the execution decisions we take and very conscious not to  leave any footprint where possible.

Would you say that traders’ skill-sets have changed over the years?

The old-style trader was a bit more brash and I would like to think this has changed, you definitely need to be more humble, thoughtful  and a deep thinker, but then also have some marketing/relationship management skills as well as some coding knowledge.

If you look at our desk throughout the day it’s a rather quiet environment, quite reflective and focussed, and it’s definitely a more quantitative type of humble, deep thinking, courteous, polite individuals we seek to maintain great relationships. 

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One of the underlying challenges is an over reliance on legacy technology and inadequate IT infrastructure. The shortening of settlement cycles across markets and asset classes, combined with increasing trade volatility, is creating new pressures on manual operational processes for both buy- and sell-side participants across multiple trade lifecycle functions. This includes clearing, settlements, trade execution, margins, and fails processing.

Simply put, with T+1 there is less time for dated, manual processes to be completed. These pressures are only going to increase as volumes globally continue to expand, and with further market structure and regulatory change, including same-day and block-level clearing, tokenised atomic settlement, and bilateral settlement, to name a few. The answer has to be innovation and digitalisation of market infrastructure. To prepare, firms must look beyond tactical changes and so-called ‘throwing bodies at it’ and start investing in evolutionary transformation.

How well are legacy systems and artificial intelligence working together currently?

We’re seeing a fairly even split when it comes to clients who are looking to launch entirely new solutions versus those who want to integrate new technologies into their existing set up.

This is a real dilemma! Legacy systems are proven, however, they are often siloed and monolithic. It feels easier to build certain types of AI into legacy systems rather than ground a newly architected, ground up build, however challenges that often crop up include compatibility issues with outdated IT architecture and having to work with fragmented or incomplete data sets. Furthermore, ground up design, architecture, engineering, testing, and launch is very expensive and takes a lot of time, even with today’s agile environments.

The answer can be buying or building an integration services layer that protects the proven legacy systems and allows you to leverage new age technologies and cloud environments to create interoperability for the future.

Fortunately, Generative AI is a new age technology that can do both, be integrated easily with proprietary systems, modern builds, and even third-party applications – providing almost instant benefits. AI can be built into co-pilots and used to unify multiple different user interfaces. We’re certainly seeing a lot of interest for this type of product in the post-trade space, for example to help drive productivity, increase scalability, and enhance risk management such as is required in a T+1 environment. 

In what ways is AI set to be most useful for traders?

AI has the potential to transform all stages of the trade lifecycle. For example, it can be used to analyse historical data and predict which trades are most likely to fail. This knowledge can be shared with the front-office to factor the costs of potential trade failures into their own decision-making, and it can also help back-office teams to avoid fails by focusing their attention and resources on trades most at risk of failure.

AI is also saving traders time on research tasks that can be better spent elsewhere. For example, at Broadridge we built BondGPT, a large language model (LLM) chat function that allows users to ask questions and instantly identify corporate bonds on the platform based on their criteria. By leveraging generative AI and incorporating real-time liquidity information, this application has created significant efficiencies in the bond selection and portfolio construction process – something that used to be incredibly complex and take up a huge amount of traders’ time.

What will be the biggest trend over the next 12 months when it comes to digital transformation?

I wish the answer was modernisation and innovation, but it is clearly resiliency and security. Of course, modernisation and innovation will play into solving those critical agendas. With cyber threats and incidents becoming increasingly sophisticated, we’re seeing a heightened regulatory focus on cybersecurity and operational resilience across the financial services sector. In fact, our latest study on digital transformation reveals that over the next two years, financial firms will boost their investments in cybersecurity by 28%.

In Europe, firms are preparing for the EU’s Digital Operational Resilience Act (DORA) which comes into force in January 2025. While this is currently the most comprehensive regulation of its kind, the thinking is reflected by other jurisdictions around the world, including Japan, Australia and the US. These regulations are complex but essential. By improving long-term operational resilience, firms can better protect themselves from the financial and reputational impact of cyber incidents and other disruptions, and at the same time, they will instil confidence in their customers by demonstrating their ongoing commitment to safeguarding their digital assets and services.

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