The new business, which will be known as Gresham, will be based in London with former Gresham Technologies chief executive Ian Manocha continuing the role at the company and Mark Hepsworth, who headed Alveo, taking the chair’s position.

The combined companies marry Gresham Technologies’’ transaction control and reconciliations, data aggregation, connectivity solutions and regulatory reporting capabilities with Alveo’s enterprise data management for market, reference and ESG data.

The range of data automation and process solutions it can offer will reduce the total cost of ownership of clients’ data, Gresham said.

“The combination of the two firms accelerates our journey to bring digital integrity, agility, operational efficiency and data confidence to financial markets globally,” said Manocha. “It creates a comprehensive set of solutions for data automation, operational efficiency, data management, analytics and risk mitigation for financial and corporate clients globally.”

The terms of the deal were not disclosed but Alveo’s majority owner, technology-focused private equity firm STG, backed the merger.

London-based Alveo was founded in 1991 as Asset Control, one of the first third-party enterprise data management service providers. It changed its name in 2020 after becoming a cloud-native, managed-service provider.

Gresham Technologies began life as Gresham Computing offering real-time transaction control and enterprise data integrity solutions.

Hepsworth said the newly enlarged company will be able to meet the increasing data demands of clients.

“We can now offer clients greater scale and a wider range of solutions that will simplify their operations and enable them to manage data more effectively,” he said.

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Last month the California-based company deepened its association with Databricks, providing the full range of its AI-powered Intelligent Data Management Cloud capabilities within Databricks’ Data Intelligence Platform. The expanded partnership will enable joint customers to deploy enterprise-grade GenAI applications at scale, based on a foundation of high-quality, trusted data and metadata. That followed the unveiling of a similar association with Snowflake. It was also selected by Microsoft as the Independent Software Design (ISV) design partner for the software behemoth’s new data fabric product.

The frequency of the rollouts in recent months has been dictated by the rapidity with which Informatica’s financial institution clients are seizing on the potential of AI. Many are struggling to bring the technology into their legacy systems, while others have a vision of what they want to do with it but not the capability to implement it.

With the market also heavily weighted towards capitalising on the growing generative AI space, Informatica group vice president and head of EMEA North sales Greg Hanson said new developments and enhancements are on the cards for the near future.

“The critical foundational layer for companies is to get their data management right and if you look at the current state of most large organisations, their integration and their data management looks a bit like spaghetti,” Hanson tells Data Management Insight.

“They realise, though, that they have to pay attention to this strategic data management capability because it’s almost as fundamental as the machinery that manufacturers use to make cars.”

Rapid Change

Hanson says that the pace of innovation at Informatica is the fastest he’s seen in his two decades at the company because its clients understand the operational benefits to be gained from implementing AI-based data management processes. This “unstoppable trend towards AI” is being driven by board-level demand, especially within financial services, a sector he describes as being at the “bleeding edge” of technological adoption.

Many have had their appetites whetted by AI’s ability to streamline and improve the low-hanging fruit challenges they face, such as creating unique customer experiences and engagements. To embed and extend those AI-powered capabilities across their entire organisation, however, will take more effort, says Hanson.

“Their ability to harness data and exploit AI’s potential is going to be the difference between the winners and losers in the market,” he says. But the drive to get results quickly may lure firms towards rash decisions that could create more problems later.

“They need to think strategically about data management, but they can start small and focus on a small use case and an outcome that they can deliver quickly, then grow from there.”

Make it Simple

Among Informatica’s clients across 100 countries are banks such as Santander and Banco ABC Brasil, US mortgage underwriting giant Freddie Mac, insurer AXA XL and online payments provider PayPal. Among the services it’s providing such institutions are broad cost reduction by the optimisation of reference data operations and the simplification of their broader data processes.

This latter point is key to helping clients better use their data, says Hanson. Arguing that without good data inputs, AI’s outputs will be “garbage out at an accelerated pace”, he says that many companies have overcomplicated data setups that are hampering their adoption of the technology. By having separate tools to manage each element of their data management setup – including data access, quality, governance and mastering capabilities – large firms are strangling their ability to make AI work for them.

“But now complexity is out and simplicity is in,” Hanson says. “As companies modernise to take advantage of AI, they need to simplify their stacks.”

Enter GenAI

Informatica is helping that simplification through a variety of solutions including its own GenAI-powered technology for data management, CLAIRE GPT – the name being a contraction of “cloud AI for real-time execution”. The technology began life simply as CLAIRE seven years ago. Last year, however, it was boosted with the inclusion of GenAI technology, enabling clients to better control their data management processes through conversational prompts and deep-data interrogation.

Comparing the new iteration to Microsoft’s Copilot, Hanson says CLAIRE GPT now offers clients greater capabilities to simplify and accelerate how they consume, process, manage and analyse data.  Adding fuel to its firepower is CLAIRE GPT’s ability to enable individual clients to call on the combined metadata of Informatica’s 5,000-plus clients to provide them with smarter outputs.

While almost all of Informatica’s offerings are embedded with its new GenAI technology, the next step will be to ensure the company’s entire range of products benefits from it.

“Data management is complex and costly for many companies and it massively impacts the ability of the company to release new products, deliver new services and create more pleasing customer experiences,” he says.

“Our job with GenAI as the fundamental platform foundation is to offer more comprehensive services around that foundational layer of data management, and more automation and productivity around the end-to-end data management journey.”

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A survey by data security provider Dasera found that 73% of institutions questioned said they had high levels of confidence in their ability to fend off ransomware attacks, data breaches and other unauthorised uses of data. Nevertheless, records of attacks showed that those firms were among the worst affected in 2023.

“The significant number of breaches contradicts high confidence in their security strategy, suggesting overconfidence in their security posture,” the report, entitled The State of Data Risk Management 2024, stated. “The sector remains a prime target for cyberattacks due to valuable data, indicating a gap between perceived effectiveness and actual vulnerability.”

The report compared the perceptions of companies in a range of high-profile data-focused sectors, including healthcare and government, with statistics on data breaches compiled by a variety of organisations and studies. These include the Verizon Data Breach Security Report, Kroll’s Data Breach Outlook Report and the Identity Theft Resource Centre.

Record Year

The Dasera survey said the combined conclusions of those studies showed that 2023 was a “record-breaking year” for breaches.

According to Verizon, the financial services industry suffered 477 data security incidents in 2023, compared with 380 for IT firms and 433 in the healthcare sector. Only government bodies suffered more, at 582. Kroll found that financial firms accounted for the largest proportion of attacks, at 27%.

Two-thirds of breaches originated externally. With the balance coming from internal “threat actors”, the financial services firms were among the least protected against attacks from within their own systems.

The report found that 77% of breaches within the sector came from basic web application attacks, miscellaneous errors and system intrusions.

“The survey underscores the importance of adopting integrated and automated data security strategies to address these challenges,” the Dasera report stated. “Reliance on outdated, manual processes and slow adoption of automated systems contribute to current vulnerabilities. Organisations must prioritise modern, proactive approaches, including regular audits, strategic use of technology, and external consulting, to effectively navigate the evolving landscape of data risk.”

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The fixed income data includes reference data and pricing across government, sovereign, agency and corporate securities, while the transcript summarisation capabilities use GenAI and machine learning models to offer a comprehensive overview of an earnings call by providing a summary of the transcript organised by topics and sentiment.

Other updates in this round include ongoing expansion of deep industry-level and segment-specific data across banking, energy, insurance, real estate and technology, media and telecommunications, and seamless notifications of event updates by enhancing ability to automatically sync corporate and industry events within the platform’s events calendar with users’ personal calendars.

“This latest release underscores our commitment to continually innovate with new technologies and capitalise on synergies from our merger with IHS Markit,” says Warren Breakstone, head of Capital IQ solutions at S&P Global Market Intelligence. “The addition of robust fixed income content further expands the value proposition of S&P Capital IQ Pro and the introduction of earnings call transcript summarisations, along with further advancements in AI-powered platform search capabilities, creates new efficiencies for our users.”

Recent additions to the platform include AI-enabled search and the integration of IHS Markit content including loan pricing and analytics, and Purchasing Managers’ Index indicators, country risk scores and economic data. The company’s acquisition of Visible Alpha created a premium offering of fundamental investment research capabilities to be offered as an add-on to S&P Capital IQ Pro.

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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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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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The London-based company raised the money in a Series B funding round led by venture capital firm Highland Europe and AXA Venture Partners (AVP). It follows a £15m cash raise in 2021 and the granting of a £30m debt facility from Kreos Capital last year.

Finbourne’s cloud-native offering, which is built on its SaaS-based LUSID platform, enables financial institutions to manage the data they need for front-to-back office processes, including portfolio and risk management. Its network of functionality and data is boosted by AI and helps institutions streamline workflows and increase revenue, Finbourne said.

“Over the past few years, Finbourne has built a revolutionary SaaS platform that is enabling many of the world’s biggest financial institutions to move from legacy, siloed solutions to a modern data architecture, allowing full, real-time visibility and optimal decision making,” said Highland Europe partner Tony Zappala.

The company plans to use the new funds to expand its sales, product and marketing capabilities in the UK, the US, Ireland, Singapore and Australia.

The Series B investment follows Finbourne’s announcement that it has deepened its engagement with post-trade operational workflow specialists Tasksize. The partnership, which began in 2021 when Tasksize was integrated into the LUSID platform, will see its expertise deployed to improve management of Finbourne clients’ post-trade processes.

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For FINBOURNE clients, the integration with Taskize aims to reduce resolution times, lower human error, and boost operational efficiency and staff capacity. Additionally, Taskize’s detailed management reporting promises better decision-making and operational resource allocation.

We’ve known about Taskize for a while and have had an integration with them for several years,” says Gus Sekhon, Head of Product, FINBOURNE Technology. “However, this enhancement goes much deeper, providing an automated intercompany exception management service that is ready to go. The interesting part is how it works: FINBOURNE’s own workflow invokes a Taskize Bubble (the Taskize term for a protected intercompany workflow) which connects and coordinates across the client’s counterparties through that Bubble, notifying changes back to FINBOURNE. This creates a seamless intercompany break resolution service that can be invoked during any client process, allowing breaks to be worked through and resolved efficiently.”

Building on the initial integration of Taskize into FINBOURNE’s LUSID platform in 2021, the expanded partnership further strengthens the external exception management and query resolution capabilities for the company’s front-to-back investment technology users. The new integration enables FINBOURNE clients to better manage the exceptions that often disrupt the IBOR and ABOR calculation process. By utilising Taskize workflow solutions for exception resolution, users can now dynamically prioritise and resolve complex IBOR and ABOR post-trade exceptions, which helps overcome the challenges posed by shorter settlement times in the new T+1 settlement environment, says Sekhon.

“From the perspective of IBOR & ABOR, the fundamental point for an efficient operations workflow is ensuring the data is as clean as possible from the start,” he says. “This means time can be spent on product research or performance analysis rather than wasting the majority on data cleansing, as some companies do. Coordinating across counterparties and pushing results back through the workflow eliminates the need to access multiple systems, hunt for the right contact, monitor progress, and check on task completion. This coordination in one place represents a significant efficiency gain for our clients in this cleansing process.”

The functionality is part of FINBOURNE’s Horizon programme, allowing clients to seamlessly access, integrate, and connect with various external technology and data sources within the LUSID platform.

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The deal was announced in April 2024 and sees Rimes move on from ownership by Swedish private equity firm EQT Partners, which acquired the company in February 2020 firm saying it had made a ‘significant growth investment’ to work with Rimes CEO, president and co-founder Christian Fauvelais to expand the company’s data management solutions and extend capabilities and partnerships.

In June 2021, Brad Hunt, formerly lead of strategy and business development at BNY Mellon, was appointed as CEO of Rimes with a focus on developing the company’s value proposition by delivering modernised outsourced data management solutions addressing industry issues of data complexity and cost. Fauvelais retained a role on the Rimes board and provides strategy guidance.

Commenting on Rimes’ latest acquisition by Five Arrows, Hunt says: “Rimes is well positioned to help shape the future of investment data solutions by leveraging its customer-centric ethos, deep domain expertise and cutting-edge technology. I look forward to helping more clients achieve their goals through our flagship benchmark data services, outsourced data management capabilities, investment management platform, and AI services. Our impressive growth over recent years reflects our commitment to innovation and excellence, and we will accelerate this with Five Arrows.”

Rimes was set up in New York in 1996 to provide managed data services with a focus on index and benchmark data. It has since been developed to offer a portfolio of data management solutions spanning EDM-as-a-Service, index and benchmark data management solutions, security and price master services, ESG data management solutions, ETF data management services, and data distribution and warehousing services.

The company’s Matrix investment management platform, which was acquired in October 2021 as part of Matrix IDM, a Sydney, Australia-based provider of a cloud native investment data management platform, is designed to help asset owners, institutional investors and asset managers solve complex data management solutions and make better informed investment decisions.

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The company was set up in 2019 by co-founders who met at University College London while doing PhD research work. They went on to work in financial institutions, where they experienced problems of getting code into production at any speed, internal bottlenecks holding up developers, and the pain points of code reviews.

As well as optimising code and reducing costs, Artemis plays well into financial institutions’ sustainability goals by running better code faster, descreasing compute usage and providing energy savings.

Artemis scans software code on-premises or in the cloud. It uses TurinTech’s LLM, which has been trained on millions of lines of code and informed by the team’s proprietary knowledge, although it can also be used with other LLMs, perhaps less effectively, and takes hardware into consideration to allow legacy systems to perform to the best of their ability.

Use cases of the solution include identifying weaknesses in code and providing recommendations for optimal changes that enhance performance, noting code that could be sped up or improved by modifying particular lines, and analysing code bases to predict their efficiency – all with a human in the loop but reducing resource requirements overall.

Kanthan concludes: “Everyone wants to use AI, but will it add value to the business? LLMs are just another form of data, so you need apps for use cases. TurinTech has an app for code optimisation and is, at the moment, leading the market.”

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