RiskSmart X is specifically designed for prime brokers, risk managers, and operations executives at sell-side institutions. It provides tools to assess and manage risk exposure to various counterparts, and to prepare for regulatory changes, audits, and government inquiries. Since its launch last year, the product has enabled users to determine the margin needed from their buy-side clients. With the addition of the new CCP Margin Calculator, users can enhance their risk management capabilities by calculating the margin requirements from their exchange counterparts, as and when necessary.

“One of TS Imagine’s key differentiators is our ability to cover all asset classes in real time,” Andrew Morgan, President and Chief Revenue Officer of TS Imagine, tells TradingTech Insight. “While many businesses rely on batch or overnight processing to calculate margin, RiskSmart X enables this at the click of a button. Our primary focus up until now has been on helping FCMs and prime brokers manage their clients’ funding and obligations, offering sophisticated and complex risk management tools within RiskSmart X, including VaR, stress tests, and historical Monte Carlo simulations, which are essential for prime brokers and sophisticated hedge funds. With the introduction of the CCP margin calculator, we are now providing our clients the capability to manage the street-facing side of their operations.”

Morgan points out that since the G20 derivatives market reforms were initiated 15 years ago, global regulators have gradually expanded the range of instrument types that need to be cleared centrally, for obvious reasons such as financial stability and transparency. “As a result of those reforms, there has been approximately 20% compound annual growth in the number of centrally cleared trades, a trend that is expected to continue,” he says. “That means that more business lines are coming within the scope of these regulations. Another significant development is the increase in automated liquidity and algorithmic trading, which accelerates the velocity of trading. This heightened speed amplifies the potential for rapid market movements. Therefore, having real-time insight into market activity and being able to anticipate margin requirements across all relevant venues is exceptionally powerful.”

TS Imagine operates globally, with staff in every major financial centre, allowing for extensive coverage within RiskSmart X’s CCP Margin Calculator. Currently, the platform supports 33 clearinghouses, over 50 global exchanges, and 13 methodologies. Calculations are updated continuously throughout the trading day, and TS Imagine plans to expand its coverage in response to client needs and developments in global financial markets.

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“For organisations such as QUOD, obtaining consistent and comprehensive data is crucial,” QuantHouse’s Head of EMEA Sales and Business Development, Rob Kirby, tells TradingTech Insight. “By partnering with Quant House, QUOD can now receive both real-time low-latency market data and historical market data with the same symbology. They can leverage our historical data to train and back-test their AI trading models, before transitioning to production using the same format with low-latency real-time market data. This data can feed into their smart order router and algorithmic technologies, creating a seamless process.”

Across electronic trading, financial institutions are increasingly adopting AI and machine learning (ML) to improve trading and execution outcomes, says Kirby. “A significant challenge in implementing these technologies is ensuring the accuracy of the data on which AI systems are trained,” he says. “QuantHouse’s high-quality, extensive historical data is now being employed to train QUOD’s AI/ML models, enabling them to adapt to and anticipate market movements. They can also utilise our real-time data to optimise the timing, price, and quantity of trade executions, thereby minimising transaction costs and market impact.”

Traders no longer need to adjust their TCA assumptions or trading strategies manually when unexpected market events happen, suggests Kirby. “They can now analyse the cost associated with each trade, optimise trading strategies and ultimately improve trade executions right at the point where it is needed most: as part of the trade execution.”

Kirby is optimistic about AI’s role in the trading environment. “Looking ahead, it will be interesting to observe the uptake and success rates of clients using traditional methods to execute in markets compared to those utilising AI trading models. This is something we will only fully understand over time. And although risk management is crucial for anyone executing in markets, whether due to human errors or unexpected machine behaviour, I’m confident that AI will save both time and money by processing vast amounts of data rapidly, allowing traders to concentrate on their core strengths.”

Commenting on the partnership, Quod Financial’s Chief Revenue Officer, Medan Gabbay, said: “In financial services, the performance of your technology is defined by the quality and speed of the data that powers your systems. This has never been more true or more important than now, as we go through a transition of data automation and AI/ML. QuantHouse has proven to be an exceptional partner in this data journey.”

The post QuantHouse Partners with QUOD to Enhance AI-Driven Trading Algorithms & TCA appeared first on A-Team.

This presents an opportunity as well as a challenge to the sell-side. If they can help their clients streamline and enhance workflows to reduce manual intervention, minimise errors, and accelerate trading and investment decisions, and if they can provide them with a way to lower operational costs while also enabling fast and profitable responses to market opportunities to better generate alpha, they can gain a serious competitive advantage.

Servicing the buy side ecosystem

With multi-asset strategies becoming more and more common, firms increasingly look to their sell-side providers to facilitate trading across a diverse range of instruments and asset classes through a single interface – whether UI or API – and to handle complex orders involving multiple instrument types, such as structured trades or multi-asset baskets for example, across different trading venues and diverse markets.

There is also an increasing emphasis on data-driven decision-making. While systematic and quantitative traders have always relied on data and models, fundamental and ‘quantamental’ firms are increasingly relying on data-driven insights to drive – or at least support – their investment and trading strategies. Firms now seek from their sell-side providers not only market data, analytics, and research, but also well-documented open APIs that allow them to seamlessly integrate such data into their proprietary models to inform and execute their trading strategies.

“The real challenge for the sell-side is adopting a technology strategy that balances their own internal needs with the ever-changing needs of their clients, one that effectively serves both,” observes Medan Gabbay, Chief Revenue Officer of multi-asset trading solutions vendor Quod Financial. “The buy side have their own technological ecosystem, made up of Portfolio Management Systems, Order Management Systems, applications for creating and managing trading strategies, various types of analytics tools, spreadsheet-based models, and a wide range of other systems they use in their day-to-day trading activities across the front, middle and back office. Forward-looking sell-side firms understand that a key part of their role is to facilitate this ecosystem, by using their technology to help clients trade their chosen markets in the way they want to trade them, as well as providing the necessary analytics and data in a format that helps them identify trading opportunities and manage their investment strategies.”

The key question for the sell side is, how to achieve the necessary agility in technology that will enable them to respond to the changing demands of their clients?

Moving beyond the buy-build debate

Several options exist. There are a number of well-established vendors who sell ‘off-the-shelf’ trading platforms, which can address many of the sell side’s needs. These platforms provide a range of essential features such as liquidity access, connectivity, order and execution management, analytics, and market data handling. However, while such off-the-shelf systems are generally adequate for day one requirements, they often lack the flexibility to rapidly adapt to changing customer needs and the dynamic nature of the markets. Firms relying solely on these platforms might therefore find themselves constantly behind the curve, limited as they are by their vendor’s upgrade and development cycles.

At the other end of the spectrum, firms may opt to build their own bespoke platforms tailored to their own specific requirements.  While this offers maximum control over the design and development process, it’s an expensive and complex undertaking, and is out of reach for most firms, other than tier one banks with substantial technology budgets.

A third option is becoming increasingly popular amongst forward-looking firms, that of buy and build. Vendor platforms that are built on modern, scalable, and adaptable technology, can be quickly deployed to meet a firm’s immediate needs and then adapted, customised and expanded as requirements evolve.

This type of approach offers various benefits, according to Gabbay. “Platforms built on this type of architecture are highly interoperable, easily integrating with other systems on both the front end – through desktop widgets for example – and the back end, through APIs. They are also much more scalable, capable of being deployed on hosted services including Cloud, on-premise, or a hybrid of the two, which leads to improved performance and better customisation to the clients specific infrastructure requirements. Additionally, being built around a component-based architecture, they offer flexibility and allow for rapid customisation, as individual modules can be created and adapted to suit specific customer requirements, new areas of functionality, evolving business processes, or changing regulatory and market structures.”

Gabbay points out that trading platforms architected in this way can also be more easily integrated with clients’ trading desks. This level of integration benefits both the client – for example through more efficient and transparent trade execution and real-time order/position monitoring – and the sell-side firm itself, by providing a better understanding and greater visibility of their clients’ activities and workflow.

For sell-side firms with limited resources, or those that believe their resources can be better invested in creating IP and not rebuilding existing technology, this approach can offer the best of both worlds – the rapid implementation and comprehensive functionality of a vendor platform, together with the flexibility, adaptability, scalability and capacity for integration of a custom-built solution. By adopting such an approach, a firm can distinguish itself from competitors who use generic or outdated vendor platforms, and compete more effectively with larger tier one banks that have developed their own solutions.

Artificial intelligence and machine learning

Another strategic choice for the sell-side is how to make best use of Artificial Intelligence (AI) and Machine Learning (ML). Although neither are new in Capital Markets, interest in AI has exploded since OpenAI introduced ChatGPT in November 2022. Since then, firms have identified a wide range of applications for Generative AI (GenAI) and the use of Large Language Models (LLMs).

One area where GenAI can add significant value in modern, component-based trading platforms, explains Gabbay, is its ability to accelerate the development and testing lifecycle, by automating coding processes and influencing all disciplines involved in defining, building, testing, operating, and supporting complex requirements. This allows firms to bring new functionality to market much more quickly than was previously possible.

“GenAI can generate test scenarios automatically by analysing the code base and understanding the purpose of different components,” he says. “It can then identify potential test cases, simulate different scenarios, and generate test data, thereby eliminating the need for manual test scenario creation. Additionally, by leveraging ML and AI algorithms, it can simulate user interactions, input test data, and validate the expected outputs. This automation reduces the reliance on manual testing, speeds up the testing process, saves time and effort, and improves overall efficiency.”

Outside of GenAI, modern trading platforms can also utilise ML within algorithmic trading, identifying and exploiting patterns in trade execution by analysing market conditions, liquidity, and order book dynamics. By scrutinising vast amounts of historical and real-time order book data to identify patterns and trends, ML-trained algorithms can determine the optimal timing, price, and quantity for executing trades, thus minimising transaction costs and market impact.

ML is also being increasingly used to develop intelligent Algo Wheels. These allow firms to analyse their incoming flow, so that the right execution strategies and order routing destinations can be automatically chosen, and optimised based on current market conditions and client-specific requirements.

Primary considerations for the sell side

Given the numerous challenges that sell-side firms face from a trading technology perspective, and the various choices they have available, what are the key considerations they need to take into account when evaluating trading platforms?

First of all, support and training are vital aspects of any technology implementation. Even the most intuitive platforms require a period of adaptation, and comprehensive training is crucial to maximise their potential. Vendors should provide robust support services to assist with both onboarding and continuous usage. This includes not only technical support but also strategic guidance to help teams leverage the platform’s full capabilities. Adequate training and support ensure that any investment in trading technology yields the maximum possible return.

Interoperability is another key factor. “A new trading platform should integrate seamlessly with existing systems to avoid operational disruptions,” advises Gabbay. “Ensuring smooth interoperability minimises the risk of data silos and ensures that all parts of your trading ecosystem can communicate effectively. This not only streamlines operations but also enhances data accuracy and decision-making processes.” Platforms that fail to integrate well can lead to significant headaches, requiring additional resources to bridge gaps between systems and potentially leading to costly errors.

Scalability is also essential for any trading platform. As trading volumes increase and new asset classes are added, the platform must scale efficiently to handle these changes. Scalability includes the ability to automate processes and manage higher trading volumes without performance degradation. “A scalable platform supports business growth by ensuring that system performance remains robust even as demands increase,” says Gabbay. “This scalability is not just about handling volume but also about expanding capabilities and accommodating new functionalities as trading strategies evolve.”

Flexibility around customisation is also important, according to Gabbay. “The platform should be capable of swiftly adapting to evolving workflows without causing bottlenecks,” he says. “Your technology shouldn’t become an obstacle, but a facilitator of change. Customisable platforms ensure that you can tailor the tools to meet specific trading needs.”

Key success factors

It’s clear that the dynamic nature of the buy-side presents both challenges and opportunities for sell-side firms. To stay competitive, banks and brokers need to consider a technology strategy that balances their internal needs with the ever-evolving demands of their clients. Whether choosing off-the-shelf platforms, bespoke solutions, or a hybrid approach, sell-side firms might want to prioritise agility, integration, and scalability in their technology stack.

Additionally, the strategic use of AI and ML can significantly enhance trading efficiency and decision-making processes. By embracing these advanced technologies and maintaining a flexible, client-centric approach, sell-side firms can not only meet the complex requirements of today’s market but also position themselves for sustained success in the future.

Robust support and training, seamless interoperability, and the ability to scale and customise are also critical factors that will determine the sell-side’s ability to capitalise on market opportunities and deliver superior value to their clients.

The post Evolving with the Market: Technology Strategies for Modern Sell Side Firms appeared first on A-Team.

By integrating Boltzbit’s AI technology, Liquidnet can now offer members and partner syndicate banks faster access to trading and data distribution, processing and displaying bond deals at a rate significantly faster than its previous parsing technology. This ensures that bonds are quickly available through the company’s deal announcement dashboard and new issue order book.

“This partnership improves the speed at which we can process messages, create, and then send structured data directly to our clients, which in turn allows them to quickly populate their OMS and prepare for trading,” says Mark Russell, Head of Fixed Income Strategy at Liquidnet, in conversation with TradingTech Insight. “The quicker we can do this, the better it is for those clients. Beyond this, the clients of our new issue Trading Platform (grey market) benefit as we are able to launch the new bonds on the screen earlier, giving those clients earlier access and more time to trade.  More trading time on our visible trading platform means more transparent data points, which is very useful for the syndicates and issuers as they get a view as to what is going on in the market.

“Structuring the bond data is not done in a single step, during the bond creation process we need to interpret the market chat, back and forth messaging, that drives the final structure of the bond,” explains Russell. “Our system needs to be able to capture and update any changes to the meta-data, such as coupons, issuers, benchmark, maturity etc. that describe the bond and feed those changes into the trading platform and other information platforms.”

He continues: “We’ve automated this process extensively with our partners at Boltzbit, creating a tool that handles the heavy lifting of structuring this data into a comprehensible bond format. Our partnership with Boltzbit is focused on speeding up and enhancing accuracy, bypassing traditional parsing tools and leveraging artificial intelligence instead.”

Boltzbit’s GenAI technology utilises the data captured from messages exchanged across various mechanisms and channels to create a large language model (LLM) that transforms the information into a structured and usable format.

“This process might seem simple, but it was actually extremely challenging,” explains Dr Yichuan Zhang, CEO and co-founder at Boltzbit. “Firstly, it involves very complex business processes. It’s not just about parsing one email; understanding the context of the conversations and the associated business processes is essential. Secondly, this is a highly specific solution, requiring the model to be extremely accurate and to follow the precise logic of the business flow around new issues. Finally, the solution needed to be highly secure and deployed in a way that allowed Liquidnet full control.”

Since the launch of its primary markets offering in 2022, Liquidnet has achieved record trading volumes in its new issue order book and increased participation from over 35 European syndicate banks, highlighting the company’s commitment to modernising primary markets and delivering substantial value to clients and the industry.

In addition to partnering with Boltzbit, Liquidnet has previously collaborated with NowCM and BondAuction, reinforcing its dedication to fostering efficiencies and connectivity for investors, banks, and issuers through strategic partnerships.

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QUODD’s QX Digital Platform gives customers access to market data functionality and content for front, middle and back-office workflows. S&P Global Market Intelligence now supplies the Platform with independent pricing and liquidity data for bonds, offering advanced security look-up and query capabilities using pre-defined or custom templates. Transaction data analysed and aggregated to generate pricing content encompasses nearly three million corporate and sovereign bonds, municipal bonds, and securitised products.

Integrating S&P Global Market Intelligence’s bond pricing and reference data with global equities and funds through QUODD is designed to enhance the QX Digital Platform’s display capabilities and connectivity for downstream wealth management users. The integration allows users to optimise their market data consumption, maximise their market data spend, reduce costs without compromising quality, and improve workflow efficiency. It supports daily pricing, reference data, and corporate actions while automating data usage entitlements for customised workflows.

“We have incorporated access to S&P pricing and reference data into our extensive content catalogue, which is a mix of proprietary and third-party data sets,” Bob Ward, CEO of QUODD, explains to TradingTech Insight. “This collectively amounts to 150 data sources and 250 billion data points in our data lake. We have made all this data available via several access points. Users can access this data as individual datasets via several communication methods (QX Marketplace), they can access digitally online and view and extract on demand (QX Digital), and now they can programmatically access multi-asset class data into third-party applications (QX Automate).”

QUODD has now signed numerous clients across multiple market segments with similar workflow concerns. The key drivers are timeliness, simplicity, and easy accessibility, as Ward outlines in the following use cases:

New issues research – New debt instruments are released into the market daily, and firms need pertinent terms and conditions to classify them correctly in their systems. The QX Digital Platform is tied into the real-time S&P bond reference data API to retrieve those details as soon as S&P does.

New asset setup – Banks price assets based on the issues that their clients hold. “A current customer told us this week that they set up 850 new assets in their system in June alone,” says Ward. “They are constantly accessing QX Automate to pull the data they need to properly set up those securities in their system based on asset type, sometimes multiple times a day. This gives them the timing and flexibility to retrieve data at any time to meet their client’s pricing needs.”

Price challenges – The Price Challenge process via the QX Digital Platform is supported by the integrated S&P Price Viewer tool, as Ward explains: “This tool gives our customers direct access to the S&P bond evaluators for price challenges. As bond pricing varies by provider, prices can differ, and customers need to confirm the most accurate evaluation. Price challenges are affirmed or updated usually within a few hours. The S&P pricing methodologies are transparent to all of our customers.”

Security master maintenance – “Most of our customers use QX not only for pricing but for global security master maintenance using our Corporate Actions solutions,” says Ward. “Many parameters can be set, such as Voluntary vs. Mandatory Date parameters, based on Effective Date or Announcement Date, or the ability to hone in on specific events that affect things like reorganizations, which affect shares and price. Maintenance tasks like identifier changes, name changes, M&A, etc., are all important in maintaining a security master.”

In addition, the platform has embedded proprietary calculators such as an Accrual & Amortization tool that provides the requisite buy & sell tickets on certain fixed income instruments, leveraging content from S&P to meet and exceed the functionality available in the legacy terminals.

Modern technologies and delivery models have been integrated into the QX Digital Platform to meet the new need for data on demand, and these innovations keep QUODD ahead of its competitors, says Ward. “Most providers today have layers and silos of technology, leading to increased inefficiencies and lower quality. QUODD is designed from the ground up for the future, and with our cloud-native platform, we can deliver our content into customisable client workflows that are turnkey, scalable, and cost-effective. Building from this platform allows us to meet customer needs today with very low switching costs while opening new options for even more advanced integrations as their digital strategy continues to evolve.”

Ward states that the two defining characteristics of the technology are a cloud-native platform that is purpose-built to power the full breadth of market data apps and APIs and the ability for companies to manage their preferred consumption model and frequency of data updates. “By virtue of technology reducing the friction of integrating and onboarding new sources of data on a self-service, on-demand, and connected basis, the QX Automate module delivers a superior experience, enabling customisation and integration at the same time; and because we are cloud-native with a modern tech stack, we can build faster and respond to customer requirements with more agility and transparency,” he says.

In terms of market data spend, Ward points out that under QUODD’s pricing model, clients only get charged for what they use and the frequency of that use. “This pricing approach, combined with a single integration point for a client’s entire security master, coupled with the improved workflow for the employees (no more swivel chairing), provides a very good value for our clients,” he says.

Looking ahead, QUODD has a number of customer-driven projects in the pipeline, including leveraging AI to help build third-party adapters at a quicker pace, and using AI to expand the company’s proprietary data sets.

The post QUODD Enhances QX Digital Platform with S&P Global Bond Data Integration appeared first on A-Team.

In addition to news content, LSEG will equip Dow Jones’s editorial teams with LSEG Workspace, incorporating the latest in workflow and productivity tools to support a data-driven newsroom environment. Journalists will benefit from comprehensive LSEG data sets, including Datastream, Fundamentals & Estimates, StarMine models, and SDC Platinum’s premier deal insights.

The Wall Street Journal’s coverage of mergers and acquisitions (M&A) and capital markets will be bolstered by over four decades of data, insights, and league tables provided by LSEG. Moreover, LSEG will serve as a key source of deals data, featuring prominently in the WSJ Investment Banking Scorecard.

The partnership will also see the co-development of an enhanced news experience within LSEG Workspace, curated by senior Dow Jones editors. This tailored news service, designed for the Workspace audience, is slated for launch in early 2025, with LSEG being Dow Jones’s first partner in this new enterprise-focused subscription model.

Combining Dow Jones’s real-time, industry-leading news with LSEG’s advanced classification, tagging, and search technologies, the collaboration aims to enrich news feed offerings for LSEG subscribers, who will gain access to Dow Jones’s text feeds, which LSEG will use to enhance its real-time news, news archive, and news analytics services.

David Schwimmer, CEO, LSEG, commented: “The inclusion of the latest news, commentary and analysis from Dow Jones and The Wall Street Journal is a powerful new addition for our LSEG Workspace users. Our partnership will also see Dow Jones benefit from our world class data and analytics capabilities to support a data-driven newsroom across all of its channels.”

Almar Latour, CEO of Dow Jones and Publisher of The Wall Street Journal, added: “This partnership with LSEG is key to delivering the world’s best news, information and analysis to business leaders across the globe. Combining the strength of both brands will serve the needs of LSEG Workspace users and enhance our newsrooms.”

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Options Technology supports trading at numerous venues worldwide with its managed infrastructure and connectivity services, along with private financial cloud services that combine hosting with direct market access, total cost of ownership reduction, and top-tier resiliency and security.

“One of the aims with this collaboration is to enable customers to utilise the Options Atlas feed more efficiently, says Grethe Brown, CEO of DiffusionData, in conversation with TradingTech Insight. “Our approach allows us to handle data transformation and distribution, significantly reducing egress costs by delivering only the specific data the trader needs. Unlike the standard practice of sending all data, we provide a sophisticated distribution that transmits only the delta—the difference between successive data points—resulting in greater efficiency and cost savings. Also, from a DiffusionData perspective, our team will benefit from collaborating with a larger entity like Options. This partnership will enhance our capabilities, providing a more comprehensive solution with Options’ consolidated data service than we currently offer.”

The Diffusion framework offers control over end-to-end data flow, creation of personalised data streams, and efficient data delivery through patented bandwidth optimisation, which will enable clients to fully leverage Options’ consolidated data service.

“There are numerous applications for personalised data streams. For instance, in FX liquidity provision, a bank might quote different FX rates to different tiers of customers,” says Brown. “Additionally, they may choose to apply a delay to some of their data, allowing them to charge lower fees to customers who receive the data with a 15-minute delay.”

Danny Moore, President and CEO of Options, commented: “Our partnership with DiffusionData represents a significant advancement in our ability to deliver robust and scalable data solutions to our clients. By integrating Diffusion’s cutting-edge data streaming technology with our consolidated data service, we are not only enhancing data delivery but also empowering our clients to gain real-time insights and make informed decisions faster and more efficiently. This collaboration underscores our commitment to providing innovative and reliable infrastructure that meets the evolving needs of the capital markets.”

Grethe Brown, CEO of DiffusionData, commented on the partnership: “By integrating our Diffusion framework with Options’ consolidated data service, we are providing clients with a powerful solution that combines real-time data streaming with unparalleled control and efficiency. This collaboration will enable users to harness the full potential of their data, delivering seamless and personalised data streams that drive better decision-making and operational performance. Together, we are setting a new standard for data delivery in the financial services industry.”

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Considering the use of GenAI, an early audience poll question asked to what extent organisations are committed to GenAI applications. Some 46% said they are testing GenAI apps, 24% are using one or two apps, and 20% are using a number of apps. Nine percent are researching GenAI and 2% say there is nothing in the technology for them.

Value of GenAI applications

A second poll questioned which GenAI applications would be of most value to a delegate’s organisation. In this case, 53% of respondents cited predictive analytics, 39% risk assessment, 39% KYC automation, 28% fraud detection and 19% portfolio management.

The panel shared their own use cases, with one member experimenting with GenAI to produce programming code and creating an internal chat box for data migration, as well as scanning data to surface information that can be categorised, sorted, filtered and summarised to create ‘kind of conversational extracts that can be used.’

All agreed that GenAI produces some low hanging fruit, particularly in operational activities such as KYC automation, but that the technology is too young for many applications, leading firms to build capability internally before unleashing GenAI apps for customers as there is still work to do around issues such as risk integration and ensuring copyright and data protection are not compromised. One speaker said: “There is a lot of experimentation and some research to do before we’re confident that we can use this at scale.” Another added: “There are just not enough skilled people to allow us to push hard, even if we wanted to. There’s a real pinch point in terms of skills here.”

Risks of adopting GenAI

Turning to risk, a third audience poll asked the audience what it considered to be the biggest risk around adopting GenAI. Here data quality was a clear leader, followed by lack of explainability, hallucinations, data privacy and potential misuse. Considering these results, a speaker commented: “We’ve already got existing policies and governance frameworks to manage traditional AI. We should be using those to better effect, perhaps in response to people identifying data quality as one of the key risks.”

The benefits of AI and GenAI include personalisation that can deliver better products to consumers and improve the way in which they interact with technology. From a regulatory perspective, the technologies are focused on reducing financial crime and money laundering, and resulting enforcements against fraudulent activity.

On the downside, the challenges that come with AI technologies are many and include ethical risk and bias, which needs to be addressed and mitigated. One speaker explained: “We have a data science lifecycle. At the beginning of this we have a piece around the ethical risk of problem conception. Throughout the lifecycle stages our data scientists, machine learning engineers and future engineers have access to python libraries so that when they test models, things like bias and fairness are surfaced. We can then see and remediate any issues during the development phase so that by the time models come to validation and risk management we can demonstrate all the good stuff we’ve done.” Which leads us to the need, at least in the short term, for a human element for verification and quality assurance of GenAI models in their infancy.

Getting skills right

Skills were also discussed, with one panel member saying: “We are living in a constantly more complex world, no organisation can claim that all its workforce has the skill set necessary for AI and GenAI, but ultimately I am hopeful that we are going to create more jobs than we are going to destroy, although the shift is not going to be easy.” Another said: “In compliance, we will be able to move people away from being data and document gatherers and assessors of data in a manual way to understand risk models, have a better capability and play a more interesting part.”

Taking a step back and a final look at the potential of GenAI, a speaker concluded: “Figuring out how to make safe products that we can offer to our customers is the only way we have a chance of reaching any sort of utopian conclusion. We must chart the right course for society and for people at work, because we’re all going to be affected by generative AI.”

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TTI: Stephen, can you elaborate on the rationale behind introducing usage-based pricing and risk-free trials in G8? How do these changes help clients better understand the value scaling of your platform and benefit from its flexibility and cost efficiency in developing and deploying trading technologies?

SM: Our aim with the usage-based pricing was to disrupt the traditional pricing model. Our platform accelerates development, allowing applications to be built in days rather than months or years. However, with traditional pricing, it can be challenging for clients to understand how such value scales from an MVP to a full production system. We explored various pricing models and, by reverse engineering our previous pricing for other trading technology solutions, we found that usage-based pricing based on virtual CPUs was the most effective metric. This approach resonates with clients because the need for more CPUs correlates with the complexity, number of users, or volume of transactions, aligning with their perception of the platform’s value. And it follows a tiered consumption model: from 0 to 10 CPUs, it’s $x per CPU; from 10 to 25, it’s $x minus 20%, and so on, so as usage increases, the per-CPU price decreases.

With the risk-free trials, we now offer a trial period with a nominal developer charge, meaning clients only incur costs at runtime, whether in a test or production environment. Our clients appreciate this low-risk, low-cost way to prototype and test new ideas quickly.

It’s also important to note that our platform is cloud-agnostic. Clients can run it on their private virtual machines, on-prem infrastructure, in their private cloud, or on public clouds like Amazon, Google, or Microsoft. Clients value this flexibility, as it allows for a multi-cloud strategy without incurring substantial fixed costs from a single cloud vendor. This also enables them to back up against different cloud providers.

TTI: Can you explain the different categories within the new Genesis Marketplace in G8 and how each category helps expedite the creation of trading and risk management applications in financial markets?

SM: The Marketplace comprises three different categories. The first category includes off-the-shelf solutions, such as the Trade Allocation Manager (TAM), a post-trade allocation confirmation system, and the Automated Quoting System (AQS), a multi-asset class quoting application that supports RFQ business flows between investment firms and their treasury desks or broker-dealers. We are continually expanding these solutions. The second category consists of specific industry vendor integrations, such as Bloomberg TOMS. The third category is what we call components, which perform specific functions commonly found in financial applications.  These are a combination of integration and workflow and include things like alerts, reporting, user management and FIX gateways.

When you use our platform, you have access to all three categories within the Marketplace. We are heavily investing in expanding the Marketplace so developers can utilise these components, with only a nominal developer fee, and pay based on CPU usage. We see this as a significant disruption to the traditional buy versus build paradigm.

TTI: How do your strategic partnerships with major financial institutions like Bank of America, BNY Mellon, and Citi influence the development direction of the Genesis platform? Can you elaborate on their role in shaping your product offerings and training initiatives?

SM: They play a crucial role. We have a strategic investor forum with these partners every six weeks, where they help guide our platform roadmap based on feedback from all our clients. We gather extensive input from clients about the functionalities and solutions they are building, or would like us to build within the platform, giving us a comprehensive understanding of their needs.

Accessibility and ease of use are major priorities for our partners because they want to get large groups of developers on Genesis.  So we’ve focused heavily on our training program, the Genesis Academy. Initially, we were doing a lot of the development work for our clients ourselves, but this has now shifted significantly. Our investors provided invaluable feedback on training materials, documentation, certification programmes, and what we call developer enablement and evangelism. They also wanted the ability to scale through their systems integrators and consulting partners, so we’ve collaborated with these parties to enable them to use the platform effectively.

A lot of the feedback revolves around the tools they want to see in the platform product roadmap, as each partner has different requirements. For instance, BNY Mellon, the largest custodian in the world, is using the platform to drive new business innovation. Citi and Bank of America are exploring how the platform can support new consortium opportunities or innovate around existing consortia.

Regarding tooling, we’ve recently released Genesis Create, a web-based tool that allows users to create new applications in minutes, and Genesis View, which enables rapid UI development by converting screenshots into UI code, using generative AI. It’s immensely valuable to have strategic investors who are deeply invested in our technology roadmap.

TTI: G8 includes enhancements in desktop interoperability and improved FDC3 support. How do these enhancements facilitate better integration and communication between different trading systems and platforms, and what role do open APIs play in this process?

There’s a lot happening around desktop interoperability, and interoperability in general. All our UI components are FDC3-ready out of the box. We are completely vendor-agnostic, working seamlessly with platforms such as OpenFin and interop.io on the desktop interoperability side, where we continue to expand our FDC3 capabilities. And with Genesis Create, you can enable FDC3 for your UI components from the outset.

We’ve always maintained an open approach, with open APIs from the beginning. On the server side, our technology integrates with various vendor systems such as Murex, Calypso, Fidessa, TT, ION, SS&C, Bloomberg, and Symphony. Whether it’s server-side data integration or technical APIs like REST and MQ, we ensure quick data transfer. Additionally, we support industry-specific protocols such as FIX and SWIFT, maintaining openness and flexibility across our platform.

TTI: What impact have the free trials and training had on client adoption, and how are consulting partners contributing to this new approach?

SM: We’ve received excellent feedback from our current clients, many of whom previously used our managed applications or products. They’ve expressed that they always wanted to use the platform, and now, with no real barriers to entry, they can. They can start using it for free, get comprehensive training, and access the Marketplace. They are only charged at runtime, once they see the value in a test or production environment. We’ve also engaged with new clients who had heard about us but hadn’t interacted with us before. They now understand how to engage with us, and clients who weren’t platform users but had specific solutions built by us can now see how scalable and user-friendly our technology is, thanks to the tooling and extensive training materials we provide. The pricing model also makes sense to them.

We also collaborate closely with consulting partners, who find this proposition very appealing. Previously, they focused solely on the professional services aspect, but now they can also participate in revenue shares by introducing new clients and implementing solutions for them. This approach encourages the entire industry to think differently about the buy versus build dilemma, or what we now call ‘buy to build’. This model allows clients to enjoy the benefits of both buying a solution and having the flexibility to build on it. This concept has really resonated with our client base.

TTI: To wrap up, where do you see the most significant opportunities for software innovation in the financial markets industry, and how does Genesis help firms address their innovation backlogs?

SM: There are several areas, but I’ll highlight two key ones. Firstly, there’s a significant focus on replacing spreadsheets and end-user computing systems. This process can be very complex because it’s not just about the spreadsheets themselves but also the workflows and how these spreadsheets are communicated within an organisation. These systems often have numerous integrations, with data being uploaded and downloaded between different systems. We see a lot of work in this area.

Another critical area is vendor consolidation. We frequently get asked if we can replace a specific vendor. However, rather than simply ripping and replacing existing systems, we excel at what we call ‘vendor scaffolding’. This approach involves integrating our technology on top of, next to, below, or around existing vendor technologies. It enables clients to start innovating quickly around their current technology stacks. Additionally, this method allows for the clean-up and optimisation of the existing vendor technology implementation. Vendor scaffolding is a significant use case because it enables clients to innovate rapidly without the substantial risk of replacing entire systems.

TTI: Thank you, Stephen

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Opening the summit and introduced by A-Team president and chief content officer Andrew Delaney, Edward J. Achter from the office of applied AI at HSBC set the scene for the day, noting the need to build AI and GenAI products that are responsible and ethical and can be scaled, and describing the importance of educating and engaging the workforce to ensure solutions are used effectively and ethically.

In more detail, the keynote speaker explained the explosion of interest in AI and GenAI following the release of ChatGPT and a change in conversation at financial institutions. He also warned of risks inherent to the technology including fairness and bias, data privacy, and the deliberate spread of false information. To mitigate risk and create value, Achter emphasised the need to get your data house in order and, perhaps a long time in the asking, pay attention to data leaders as data is the lifeblood of AI and GenAI applications.

Also important to consider are regulatory requirements around AI and GenAI, addressing the carbon emission costs of using LLMs, and perhaps most importantly, writing a clear company policy that can be shared with all stakeholders. Demonstrating the benefits of AI and GenAI products can turn scepticism into an understanding of benefits, including productivity gains that can be measured, and change negative perspectives into positive approaches to doing more with the technology.

Ultimately, a skilled workforce, educated customers, technology used in the right context of conduct, and confidence across the organisation will result in value creation.

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