What is your regulatory reporting telling you about your upstream processes?

As with anything in life, you get out what you put in. Higher quality inputs lead to better outputs. The same principle also applies to firms’ regulatory reporting, upstream processes, and risk management.

Where trouble begins

In our experience working with clients on regulatory reviews, many reporting issues originate in upstream processes and risk management. These problems often begin at trade booking and ripple all the way down to what is reported to regulators.

Many firms operate across multiple booking and risk systems, including those for collateral cash management and valuations. This complexity often comes from business growth, where new or “bolt-on� systems are added to existing architecture, avoiding the need for updates in the short term, but causing more trouble down the road. Information barrier controls – designed to separate private-side MNPI from public-side activity – further fragment systems and data flows.

Firms that handle this well usually have a consolidated data lake. However, not all firms have that capability, or they haven’t done it in a way that supports effective regulatory reporting. Furthermore, there are multiple end users of this collated data and the reporting data is a really good gauge of how data sits ‘upstream’ and will be used by different parts of the firm. If the quality is bad coming into the reporting layer, it will likely be bad upstream.

Inconsistencies pass through the system

Regulators now look for signs of weak internal controls. Investigations often reveal inconsistencies in data lineage – from upstream bookings and risk systems to the final reported figures. Tolerance for these gaps is decreasing, especially when they impact the accuracy of reports.

It is critical for firms to have a clear view of their risk and positions across all systems, and regulators expect this visibility to be reflected in regulatory reports.

Investing in a review

With the wave of regulatory rewrites slowing in 2025, now is a good time for firms to invest in a review of operational processes. Taking a closer look at trade processing flows can reveal underlying issues that may be compromising report accuracy.

An investment in understanding your data lineage and looking at the design of your internal data models has a track record of delivering operational alpha and ROI. The development of an industry standard data model, the ‘Common Domain Model’ (CDM) presents an opportunity to upgrade your internal data models in a way that will offer efficiency gains in data transparency and interoperability with other industry participants. In addition to regulatory reporting, there are other regulatory benefits to ensuring smooth data flows, such as supporting the upcoming requirements of the EU and UK T+1 accelerated settlements cycle. 

 A number of recent fines have mentioned that fined firms are bringing in a third party to review processes – an approach that is viewed favorably by regulators. What we typically find is that firms with an accurate and reconcilable reporting data set often have smoother upstream operations. To promote excellent regulatory compliance in the long term, upstream processes deserve a careful look.

Peering into the future is a tricky business – especially in the ever-changing realm of data, analytics, and AI. But if there’s one thing we’ve learned, it’s that uncertainty never stopped us from trying. After all, we’re in a part of the technology profession where predicting the unpredictable is often part of the job description.

We called upon seven of our data-powered innovation movers and shakers to dust off their (frozen) crystal balls and share their visions of what 2025 has in store. Their insights reveal a world where AI balances on the edge of legality, cloud platforms morph into something entirely new, and synthetic data booms with promise – and no, it’s not artificial hype. From “vertical AI� that digs deep into industry needs to conversational AI that knows what you want before you do, these trends give a glimpse of the fascinating, yet challenging, future of data and AI.

Will their predictions come true? Only time will tell, but one thing’s for sure: 2025 is shaping up to be a year we’ll be talking about for a long time to come. And data and AI are right in the middle of it.

Let’s dive in.

AI is not a crime

AI is not a crime, though sometimes it feels like one, given its swift advance beyond legal bounds. While identity theft, deepfakes, and media manipulation emerge from Pandora’s box, many AI ethicists focus on fairness and transparency, skirting around AI’s darker uses. As AI matures and criminal applications surge, discussions will inevitably shift from theoretical ideals to practical realities, especially when organizations face public lawsuits under new AI regulations. This shift will force experts to tackle how AI can harm society directly. So, while AI is not a crime, it certainly invites a compelling conversation about AI and crime. Let’s face it: When it comes to AI, the real crime would be ignoring the conversation altogether. – Marijn Markus, AI Lead, Managing Data Scientist, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

Augment my governance

Prepare to be captivated. AI agents are about to revolutionize data management in the upcoming year. They will shoulder burdensome data tasks, enabling companies to reach new pinnacles of productivity and efficiency. With AI seamlessly managing data collection, analysis, and access for us, we can finally focus on something much more crucial: getting value out of data for the business. It’s high time to achieve that, isn’t it? The future of data management is upon us, and missing the opportunity of augmenting is not an option. Because in this data game, those who augment govern – and those who don’t get governed. – Liz Henderson, Executive Advisor, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

Cloud encounters of the third kind

As we look towards 2025, cloud data platforms apply for a new round of transmitted change. We all recognize the need for high-quality enterprise data as a foundation for relevant, trustworthy AI. Add to that the need to adhere to regulations, data sovereignty, privacy sustainability and cost. It soon becomes apparent that a smart mix of different and diverse cloud approaches for data will play a crucial role in the upcoming year. I expect to see a pendulum swing towards larger investments in cloud data platforms, yet it will be clouds of a different kind. Or, to put it differently, the forecast calls for cloud cover – but with a whole new kind of silver lining. – Prithvi Krishnappa, Global Head of Data and AI, Sogeti

Let’s talk better

Conversational AI will continue to be a hot topic in 2025. Contact center transformation, leveraging “classicâ€� AI and generative AI, will help save labor costs by billions and improve customer service significantly. These technologies can handle routine inquiries and provide instant responses, freeing human agents to deal with more complex issues. Imagine a world where you no longer have to press 1 for assistance – AI will anticipate your every need before you even know you have one. While human agents may become less central, customer satisfaction might reach an all-time high as AI enhances efficiency and personalization in ways we never thought possible. It’ll be the talk of the town in 2025. – Monish Suri, Global Google Partnership Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

When AI goes vertical

We will see a major rise in domain-specific vertical AI solutions that are finely tuned through rigorous test-driven prompt engineering. These purpose-built AI models will deliver more reliable and precise insights tailored to the unique needs of their industries. For instance, in healthcare, imagine AI predicting patient outcomes like a crystal ball, analyzing vast datasets of medical histories and treatment plans to conjure better patient care and optimized resources. In financial services, AI will become the ultimate fraud-buster, identifying unusual patterns in real time and safeguarding assets with previously unseen precision and confidence. Vertical AI solutions will not only streamline operations but also spark innovation by providing industry-specific intelligence and efficiency. The only way is vertical! – Dan O’Riordan, VP, AI and Data Engineering, ÎÚÑ»´«Ã½

The semantics of confidence

We’ve seen many companies adopting the principles of data mesh and semantics as part of their modern data analytics platform strategy. If nothing else, it’s needed in 2025 and beyond to comply. For example, the EU AI Act requires close tracking of the purpose of AI models and the underlying data used to train it. This can only be done by enhancing data platforms with semantics, connecting original data sources, forged data products in AI models, and all business dashboards and AI-infused applications. It creates high levels of confidence in both data and AI, next to many new, innovative opportunities to leverage data. The endgame? Nothing less than a full digital twin of the enterprise, a hallmark of data mastery. – Arne Rossmann, Innovation Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

Synthetic data boom

I predict a boom in synthetic data. But first of all, what is synthetic data? It’s artificially generated but realistic data that mirrors real patterns without using sensitive information. Why is synthetic data crucial? It tackles privacy, security, data scarcity, and control issues. Traditional data sources are hitting their limits. Privacy laws are tightening, and real-world data often lacks the diversity we need. Synthetic data lets companies create datasets that mimic real shopping behavior in retail or complex production processes in manufacturing without exposing sensitive info or being held back by data gaps. I foresee that 2025 will be the year synthetic data moves center stage. Companies ready to leverage it will build powerful, adaptable models faster than ever. The synthetic data boom will be anything but artificial. – Dinand Tinholt, VP, ÎÚÑ»´«Ã½ and Data, North America, ÎÚÑ»´«Ã½

Interesting read?

ÎÚÑ»´«Ã½â€™s Innovation publication,ÌýData-powered Innovation Review – Wave 9Ìýfeatures 15 captivating innovation articles with contributions from leading experts from ÎÚÑ»´«Ã½, with a special mention of our external contributors from, and.ÌýExplore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Rapid technological advances, evolving global challenges, and growing demands for reform are reshaping the security and judicial landscape. How will emerging trends in digital transformation, artificial intelligence, and surveillance technologies alter the way in which law enforcement and justice systems operate? And what critical questions do they raise around privacy, ethics, and accountability?

A focus on justice reform, restorative practices, and addressing systemic inequalities is reshaping the way societies approach crime and punishment. Coupled with new threats posed by cybersecurity risks, geopolitical instability, and climate change, this will significantly impact both national security priorities and global cooperation in the coming years.

Six key trends shaping the future of security and justice

Redefining future operations

The trends shaping security and justice in 2025 reflect a complex interplay of technological innovation, social change, and global challenges. As advancements in AI, cybersecurity, and surveillance technologies redefine how law enforcement operates, the demand for accountability, privacy protection, and fair use of these tools will become more pronounced.

As new threats emerge, from cyberattacks to the impacts of climate change, global cooperation and adaptive strategies will be essential for maintaining both public safety and human rights. The future of security and justice will be defined by the need to navigate these complex, interconnected issues, while ensuring that technological progress serves the greater good.

There’s a crucial need to demystify bio-engineering solutions to demonstrate their value, gain public trust and understanding, and drive adoption and economic growth.

The COVID-19 pandemic was a moment in history, when global governments, private institutions, academia, and society as a whole, coalesced around the need and to benefit global wellbeing. When a vaccine was produced and rapidly approved through extraordinary measures, the collective relief was palpable, but there was an unexpected problem.

Public trust in science, representation, and a lack of bio-literacy around mRNA vaccine technology challenged the progress of the global vaccination program. Governments worldwide invested hundreds of millions in fighting disinformation about bio-engineering solutions.

For a new innovative solution to be adopted, the people it serves must trust that it will work in their best interests, and trust that those developing solutions have their best interests at heart. In the case of such new and emerging deep-tech as bio-engineering solutions, with their implications to natural evolution, building trust and understanding is more important than ever.

There are clear steps we can take to build this trust, to drive adoption of bio-engineering solutions and related socio-economic growth.

Low levels of literacy and trust: policy makers, citizens, and markets must better understand bio-engineering solutions to unlock their potential

Recent data from the ÎÚÑ»´«Ã½ Research Institute highlighted the growing divide between industry innovation and public understanding: 96% of companies reported active engagement with, or plans to develop, bio-solutions. And yet, research by the showed that 76% of UK respondents feel they lack sufficient information to make informed decisions about bio-based products. The same report found that 61% have never heard of engineering biology. Although these findings are UK-specific, similar trends are evident globally. For example, the CRI report also revealed that 65% of startups and corporations view low public bio-literacy as a significant barrier to adoption.

The consequences of this gap are profound. One showed that decades-old public resistance to the distinct domain of genetically modified organisms (GMOs) shaped overly cautious regulatory frameworks that impede advancements in plant genome editing to this day. As a result, solutions to critical challenges, such as food security, continued to be delayed. More recently, hesitancy around mRNA vaccines has shown how a lack of communication can erode trust and create a vacuum for misinformation to grow. Without proactive public engagement, societally-valuable bio-based innovations such as bio-based plastics, precision farming, and sustainable fuels risk similar skepticism, potentially stalling their adoption. Governments and industries must act collaboratively, ensuring consumers feel like active participants in this journey, not passive spectators.

Business and government: shared responsibility to unlock economic growth through engineering biology vision

The challenge of bridging the gap between innovation in bio-engineering solutions and public understanding is a shared responsibility. It demands coordinated action from both businesses in industry and policymakers.

Businesses must go beyond simply creating innovative bio-products – they should actively engage citizens and a broad stakeholder group throughout the development process by employing user-centered and co-design techniques. This not only builds trust and understanding with consumers but of course ensures stronger market fit and a greater chance of commercial success.

To unlock the economic growth potential of bio-engineering solutions, policy makers also need to engage in similar user-centered and co-design techniques early in the policy making process through techniques like ‘Policy Labs.’ Policy Labs help rapidly map ‘policy whitespace’ where a new emergent theme or technology means there are few existing policy ideas or policies to build on. If policy teams become blocked due to a lack of understanding of complex new technologies, this prevents private markets from progressing due to a lack of support or legislation and regulation that unnecessarily blocks development. 

The importance of Policy Labs

Policy Labs are a proven technique to test early policy hypothesis, to ensure policy solutions will work for all stakeholders. For instance, if there’s to be a proposed investment by the Department of Environment Farming and Rural Affairs (DEFRA) into alternative proteins or GM crops, the farming industry should be engaged to test early policy hypothesis to see whether proposed solutions, services, regulations or legislations are likely to empower farmers with new economic means. Or disempower them by creating new competition in the markets or an even more complex post-Brexit funding landscape.

A Policy Labs approach also ensures better cross-departmental working and inputs from the relevant government bodies that should be involved, across such a broad problem space. For example, in the UK, the Department of Business and Trade (DBT) or Department of Science Innovation and Technology (DSIT) might work with DEFRA to provide a better holistic approach and collaborative understanding to launching new solutions by data sharing agreements. Subject Matter Experts (SMEs) and private sector specialists in Engineering Biology could also be engaged through the policy labs process to ensure policy ideas are technologically viable, operationally feasible, and economically scalable in markets.

Open and honest dialogue

Governments should also be creating an ongoing dialogue with citizens to ask what assurances they need to feel confident about bio-engineering solutions (products and services) and how perceived risks can be addressed. Transparent, accessible communication mechanisms are key, alongside independent oversight to ensure safety and accountability. As the puts it: ‘Public engagement, improvement of public perception, and building trust are critical factors for the growth of the bio-economy and market.’

A call to action for bio-economy solutions

Engineering biology has the potential to solve some of the world’s most pressing population scale challenges, from climate change to healthcare. But without better collaboration between governments, industry, and the markets they serve, progress on bio-engineering solutions will stall. By employing co-design both in the product development and policy making lifecycles, we will build better understanding and trust among stakeholders and citizens, while enabling policymakers and businesses to ensure bio-solutions not only innovate but also meet public needs.

At ÎÚÑ»´«Ã½, we provide end-to-end support in Engineering Biology and AI for Science strategy, helping public and private sector clients accelerate bio-solutions while addressing the challenges outlined here. From fostering public bio-literacy and engaging citizens to building transparency into innovation and driving growth through regulatory collaboration, we leverage our insight drawn from practical laboratory bio-engineering, to advise our clients on delivering impactful, trusted, and scalable outcomes. Together, we can draw up a bio-economy strategy that maximizes public trust in the solutions that will shape the world for years to come.

The time is now to act. Together, we can do more than just create bio-solutions; we can create a bio-ready society. Get in touch to explore how we can help accelerate your engineering biology journey.

The jump from cloud FinOps to on-demand consumption FinOps is underway, it’s time to embrace the evolution and unlock greater business value.

Cloud FinOps was built around utilizing the public cloud to manage financial investments and has become a standardized part of today’s technology landscape. However, FinOps cloud cost management is no longer enough – it’s time to evolve. To do so, it’s worth considering what cloud FinOps is currently defined as to better highlight our solution.

The FinOps Foundation, a project to advance the community who practices FinOps, defines cloud FinOps in their Framework as: “an operational framework and cultural practice which maximizes the business value of cloud, enables timely data-driven decision making, and creates financial accountability through collaboration between engineering, finance, and business teams.â€�¹

However, with the growth of on-demand technologies like SaaS and Gen AI, FinOps needs to go further. And with this evolution in business and technology comes a new focus on operating expenses (OpEx), particularly in how these technologies affect cost. Companies need to evaluate the value of OpEx spend, while FinOps need to underline the benefits.

To put it simply it’s time to ask: What’s the cost of a click?

It is undeniable that cloud FinOps has proved its worth both quantitatively and qualitatively in terms of cutting the fat out of technology investments. It’s driven elevated business value and has become a staple discipline across organizations.

Still, there are a number of limitations that contemporary FinOps practices have run into. These primarily circle around common themes such as isolation and lack of strategic input. And with companies seeking to manage their OpEx spend, ensuring that FinOps and on-demand technology integration continues can become complicated by a lack of visibility among FinOps teams.

Let’s examine these challenges in more detail. 

Isolated FinOps Teams 

There’s no denying FinOps teams do a great job in their areas of influence, however, those areas are often too limited. This is especially true when executive sponsorship wanes and other priorities take precedence. The focus on cloud FinOps seems to return only when a negative cost incident occurs, and this only further isolates these teams. 

Low demand management influence 

Even the most successful cloud FinOps capabilities are limited in their influence in demand management. Specifically, in how FinOps teams influence acquiring new technologies and resources to support the growth of the discipline. Sure, they can highlight cost implications and even help shape cost-effective solutions, however, they can rarely influence demand management directly – especially as part of a wider, end-to-end system.  

Limited strategic reach 

FinOps teams usually report to a technology leader who isn’t likely sitting in the C-Suite. Naturally, this limits the influence on strategic decision-making that FinOps has. This is a compounding problem, as other areas of the business group beyond the IT organization are driven by C-Suite priorities and bridging that gap is hard – if not impossible – in a large enterprise.  

Isolated from other initiatives 

To add to this gap, initiatives from senior leadership most often do not happen in concert with FinOps teams. The consequence can impact how architectural principles are re-designed, or result in frugal investments made in architecture that should support FinOps. It translates into a lack of FinOps integration with the wider business. The result? Unplanned or unexpected complexity later.  

Singularly focused on cloud and public cloud services 

In the current market, there is an ever-increasing variety of consumption-based technologies in use. FinOps teams who are only using public cloud are limiting the potential value they can generate. This in turn results in the web challenges we’ve described. It’s precisely the place where traditional cloud FinOps stumbles, and where exactly is that leading us?

While the FinOps Foundation is widening their scope to include SaaS and AI, within the overall FinOps community, these concepts are still in their infancy. This is true both in terms of implementation but also integrating them into current FinOps practices.

However, we believe this is a vital moment to start the shift towards acquiring new technologies and re-defining goals. This is reinforced by the fact that there is an increasing amount of tooling available to not only identify, but also to introduced automated optimization of various FinOps processes. And with the continued adoption of machine learning and AI (which is already rolling out as AI for FinOps) – this will only accelerate.  

Welcoming the new era of FinOps:

Cloud Consumption On-Demand 

From the very beginning, the rise of Cloud Consumption On-Demand in FinOps must consider all consumption-based technologies and business strategies. This includes everything from SaaS, Gen AI, AI infrastructure, and other cloud FinOps services. In turn, these technologies must be introduced to encompass the entire business value chain.

To do this, FinOps teams need to be challenging traditional capital expenditure (CapEx) governance systems. In a world where a growing number of technologies are purchased under OpEx scenarios, this can be complex. In other words, FinOps teams must not only prove long-term financial benefits but provide a solid short-term business case that tangibly shows how Cloud Consumption On-Demand technologies create cost savings and better efficiency.

It also means re-defining how FinOps is perceived. From their current periphery, FinOps teams must harness on-demand technologies to demonstrate their value with faster and automated solutions, reduced expenses, and elevated data-led decision making. This will help showcase the strategic value FinOps teams are creating.

Additionally, FinOps teams need to underline that the challenges they face are business problems. It is not a question of managing IT systems or technologies. This is a matter of delivering better business value thanks to the benefits of on-demand technologies, such as accurate budgeting and elevated forecasting results.

FinOps teams can also deepen collaboration across business units with on-demand technologies, For example, providing more flexible scalability that best fit the immediate business need. This can help keep various disciplines, like finance and engineering, connected and transparent in terms of financial accountability via integrated tools and platforms with accurate, real-time data sharing. 

Transforming company culture

While harnessing on-demand technologies and more modern FinOps practices are vital, it is education that will really make an impact. FinOps teams have the opportunity to transform a company’s culture by pushing for stronger education around on-demand technologies and their potential. Such a step is not only desirable but business critical. 

These cultural practices that on-demand FinOps can create will help spark greater levels of integration across business lines. This translates into the reduction of silos across the organization. Ideally, the new FinOps will fully integrate with demand architecture, purchasing, on-demand technology, reporting, analysis, finance forecasting, and more.

It’s time to evolve the FinOps framework 

This new world of FinOps will play a far more active role. Powered by on-demand technologies, the discipline can escape the limitations that reliance on public cloud has introduced and in turn, generate greater value. Most importantly, it gives FinOps teams deserved recognition as fundamental players in modern business practices. 

Do you know the cost of a click?

With Cloud Consumption On-Demand, we can help diagnose the potential of FinOps has in store for companies, while supporting them with action plans to help them make it a reality.

It’s time to welcome in the new era of cloud FinOps…

… It’s time to know the cost of a click.

Reference: 1.

The future of public administration lies in partnerships—not silos—with citizens, businesses, and civil society. In an era of rapid digital transformation, while the guiding principle of providing accessible, inclusive and high-quality public services remains fundamentally unchanged, the way public administrations are creating value for their citizens is undergoing a profound evolution.

As technology evolves and societal challenges grow more complex and interconnected, traditional siloed structures are increasingly being replaced by dynamic ecosystems where value co-creation is critical to the success or failure of public interventions.

In 2021, . Today, this approach has become a widespread foundational principle. Key technological enablers are driving this shift, empowering public administrations to move towards a collaborative approach of public service delivery that brings together governments, businesses and citizens to address challenges more effectively. From leveraging interoperability to dissolve boundaries and advance data-sharing ecosystems to the rise of GovTech, proactive service delivery and the transformative potential of government AI, these key trends are laying the groundwork for a smarter, more inclusive and efficient public governance designed to meet the demands of modern, interconnected societies.

Five trends in public administration

Time for action in an increasingly interconnected world

To fully harness the potential of these digital trends, public administration leaders must adopt an action-oriented approach. A combination of political commitment to digital transformation, inter-agency collaboration and leveraging robust PPPs to bridge resource gaps and accelerate innovation will be key. Together they will help to overcome budget constraints, siloed institutional frameworks, cultural resistance to change and complexities in measuring and reporting progress that still afflict public administrations worldwide. While strategically investing in cutting-edge technologies like AI, leaders must also champion a culture of continuous learning and upskilling among civil servants, ensuring they are equipped to leverage effectively these emerging tools. Ultimately, aligning digital strategies with citizens’ needs through human-centered service delivery will enable administrations to build trust, improve efficiency, and deliver meaningful public value in an increasingly interconnected world.

While Mulder trusts his gut; Scully trusts the facts – in fraud detection, we need both. Hybrid AI blends the intuition of LLM with the structured knowledge of a knowledge graph, letting agents uncover hidden patterns in real time. The truth is out there—now we have the tools to find it.

Fraud detection can be revolutionized with hybrid AI. Combining the “intuitive hunches� from LLMs with a fraud-focused knowledge graph, a multi-agent system can identify weak signals and evolving fraud patterns, moving from detection to prevention in real-time. The challenge? Rule sets need to be cast in iron, whereas the system itself must be like water: resilient and adaptive. Historically, this conflict has been unsolvable. But that is about to change.

A multi-agent setup

Large language models (LLMs) are often criticized for hallucinating: coming up with results that seem feasible but are plain wrong. In this case though, we embrace the LLM’s gut-feeling-based approach and exploit its capabilities to identify potential signs of fraud. These “hunches� are mapped onto a general ontology and thus made available to symbolic AI components that build on logic and rules. So, rather than constricting the LLM, we are relying on its language capabilities to spot subtle clues in text. Should we act directly on these hunches, we would run into a whole world of problems derived from the inherent unreliability of LLMs. However, this is the task of a highly specialized team of agents, and there are other agents standing by, ready to make sense of the data and establish reliable patterns.

When we talk about agents, we refer to any entity that acts on behalf of another to accomplish high-level objectives using specialized capabilities. They may differ in degree of autonomy and authority to take actions that can impact their environment. Agents do not necessarily use AI: many non-AI systems are agents, too. (A traditional thermostat is a simple non-AI agent.) Similarly, not all AI systems are agents. In this context, the agents we focus on primarily handle data, following predefined instructions and using specific tools to achieve their tasks.

We define a multi-agent system as being made up of multiple independent agents. Every agent runs on its own, processing its own data and making decisions, yet staying in sync with the others through constant communication. In a homogeneous system, all agents are the same and their complex behavior solves the problem (as in a swarm). Heterogeneous systems, though, deploy different agents with different capabilities. Systems that use agents (either single or multiple) are sometimes called “agentic� architectures or frameworks.

For example, specialized agents can dive into a knowledge graph, dig up specific information, spot patterns, and update nodes or relationships based on new findings. The result? A more dynamic, contextually rich knowledge graph that evolves as the agents learn and adapt.

The power is in the teaming. Think of the agents Mulder and Scully from The X-Files television show: Mulder represents intuitive, open-minded thinking, while Scully embodies rational analysis. In software, there always have been many Scullys but, with LLMs, we now have Mulders too. The challenge, as in The X-Files, is in making them work together effectively.

The role of a universal ontology

We employ a universal ontology to act as a shared language or, perhaps a better analogy, a translation exchange, ensuring that both intuitive and analytical agents communicate in terms that can be universally understood. This ontology primarily consists of “flags� –generic indicators associated with potential fraud risks. These flags are intentionally defined broadly, capturing a wide range of behaviors or activities that could hint at fraudulent actions without constraining the agents to specific cases.

The key to this system lies not in isolating a single flag but in identifying meaningful combinations. A single instance of a flag may not signify fraud; however, when several flags emerge together, they provide a more compelling picture of potential risk.

“This innovation shifts the approach from simple fraud detection to proactive prevention, allowing authorities to stay ahead of fraudsters with scalable systems that learn and evolve.�

Hybrid AI adaptability

The adaptability of the system lies in the bridging between neural and symbolic AI as the LLM distills nuances in texts into hunches. They need to be structured and amplified for our analytical AI to be able to access them. As Igor Stravinsky wrote in his 1970 book Poetics of Music in the Form of Six Lessons, “Thus what concerns us here is not imagination itself, but rather creative imagination: the faculty that helps us pass from the level of conception to the level of realization.� For us, that faculty is the combination of a general ontology and vector-based similarity search. They allow us to connect hunches to flags based on semantic matching and thus address the data using general rules. Because we work in a graph context, we can also explore direct, indirect, and even implicit relations between the data.

Now let’s explore how our team of agents picks up and amplifies weak signals, and how these signals, once interwoven in the graph, can lead the system to identify patterns spanning time and space, patterns it was not designed to identify.

A scenario: Welfare agencies have observed a rise in fraudulent behavior, often uncovered only after individuals are exposed for other reasons like media reports. Identifying these fraud attempts earlier, ideally at the application stage, would be extremely important.

Outcome: By combining intuitive and analytical insights, authorities uncover a well-coordinated fraud ring that would be hard to detect through traditional methods. The agents map amplified weak signals as well as explicit and implicit connections. Note also that the system was not trained on detecting this pattern; it emerged thanks to the weak signal amplification.

One of the powers of hybrid AI lies in its ability to amplify weak signals and adapt in real time, uncovering hidden fraud patterns that traditional methods often miss. By blending the intuitive insights of LLMs with the analytical strength of knowledge graphs and multi-agent systems, we’re entering a new era of fraud detection and prevention – one that’s smarter, faster, and more effective. As Mulder might say, the truth is out there, and with the right team, we’re finally close to finding it.

Start innovating now –

Implement a universal ontology

Create a shared ontology to bridge neural (intuitive) and symbolic (analytical) AI agents, transforming weak signals for deeper analysis by expert systems and graph-based connections.

Form specialized multi-agent teams

Build teams of neural (real-time detection) and symbolic (rule-based analysis) AI agents, each specialized with tools for their role.

Leverage graph technology for cross-referencing

Use graph databases to link signals over time and across data sources, uncovering patterns like fraud faster, earlier, and at a lower cost than current methods.

Interesting read?

ÎÚÑ»´«Ã½â€™s Innovation publication, Data-powered Innovation Review – Wave 9Ìýfeatures 15 captivating innovation articles with contributions from leading experts from ÎÚÑ»´«Ã½, with a special mention of our external contributors from, and .ÌýExplore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Europe is building a data-driven economy in a changing geopolitical context. As it strives for both innovation and sovereignty, decentralized ecosystems offer a way to create value with data, while safeguarding freedom of choice.

Data has the potential to transform processes, businesses, economies, and society by unlocking new kinds of value creation. It’s also how we are going to make AI work as a crucial component of the future European data economy—but only if that data is built on strong foundations that ensure its quality and relevance.

Of course, value creation depends on the data that’s available to you, and you might not have all the data you need. That’s why data needs to be shared and combined. In this article, we consider how data spaces meet this need, offering what the as the “ability to provide the essential foundations for secure and efficient data sharing�. While our focus in this article is on European data spaces, we recognize that this is becoming a relevant topic around the world.

Why a decentralized data economy makes sense for Europe

Data spaces are, in effect, decentralized ecosystems that have a powerful resonance in the world today. Indeed, recognizing their huge potential, the European Commission established a series of domain-specific/sectoral common European data spaces designed to help “unleash the enormous potential of data-driven innovation�.

We see three main drivers for these common data spaces in Europe: geopolitics, commercials, and choice. In the first instance, in light of the unstable geopolitical landscape, data spaces give you assurance that all your (data) eggs aren’t in one basket.  You select the datasets you want to reside in what data space. Interoperability and portability can help avoid the dreaded lock-in effect where changing from one service provider to another might be prohibitively complicated. Commercially, data spaces address any exposure to potential monopolistic lock-in effects by individual companies cornering the market in data platforms. Then there’s the matter of choice. You choose who you interact with in a common data space, which puts you in control of who to share data with.

Why we need data spaces

Sharing data is key to data-driven growth. Indeed, it’s a vital aspect of the European strategy for data. But over-reliance on data platforms predominantly controlled by a limited number of international technology firms introduces potential vulnerabilities regarding data security, access, and strategic autonomy. We may also lose the ability to share data on our own terms, in accordance with our own values—freedom, privacy, control.

An alternative future for Europe is to share data on a sovereign basis. And that implies across industries. That’s why we’re so excited to be working on the DSSC and on Simpl, the open source, smart and secure middleware platform that supports data access and interoperability among European data spaces.

Beyond technology to value creation

Let’s not forget that a data space is only an instrument. It’s what you do with it that matters. In a data space you will be able to aggregate, combine and correlate data that you can’t today because it is stored in different places. And that’s where we begin to create significant value from data, specifically in a number of areas, as follows.

1. Global challenges: Data spaces will prove inordinately useful in tackling grand challenges that cut across sectors and geographies. Here we’re talking about achieving mission-oriented policy goals, such as reducing healthcare inequality and achieving net zero/carbon neutrality targets. For example, the (EHDS) will be an enabler of patient empowerment, with better access to and control over health data. Further, increased reuse of EHDS data for research and policy making will improve public health interventions. A 2025 from the World Economic Forum in collaboration with ÎÚÑ»´«Ã½ suggests the EHDS could generate €5.5 billion savings over ten years. We’ve already seen the huge value of data sharing in a global crisis when, in the Covid 19 pandemic, our governments needed data from many areas at once to form policy—healthcare systems, pharma, mobility, employment and economic data. There will be future pandemics.
2. Innovation: Data spaces will undoubtedly contribute to data-driven innovation across the EU as it continues on its mission to build the Single Market for Data. The states, “Common European Data Spaces will enhance the development of new data-driven products and services in the EU, forming the core tissue of an interconnected and competitive European data economyâ€�.  In this respect, the combination of data from different sources across sectors can produce fascinating new applications. Think, for example, of the traffic flow in a city, where the observation of vehicle movement and a subsequent adjustment of traffic lights can help avoid congestion, and the monitoring of parking lots eases the burden of finding a parking spot, possibly connected to a recommendation of a charging port for the car’s battery. The energy grid could then be supplied with better anticipation of demand peaks and control energy distribution accordingly.  The seamless integration of real-time public transport data can then be used to recommend the best option for getting from A to B.
3. Efficiency: Data spaces will help in the more efficient use of resources and improve public services. A great example here is that of road surface observation. By correlating data from cars’ electrical sensors, it becomes possible to monitor, in real time, the deterioration of the road, and carry out preventative maintenance to optimize spend / return. And returning to the healthcare sector, access to comprehensive patient histories in a shared data ecosystem has the potential to lead to better and faster diagnosis and treatment.
4. Science and research: Shared data can create new evidence bases for scientific and medical research. Let’s consider the following scenario—I drive to work in a convertible most days; the farmer of the field sprays an experimental fertilizer; later I develop  neurological issues but doctors are unsure how to treat them. In the future we might be able to correlate this illness with the exposure to the fertilizer by aggregating mobility data, air quality data, times that the farmer used the fertilizer, and the contents of that fertilizer.

Questions at the edges of our data economy

The potential for value is clear, but there are numerous challenges still to overcome—and they are not principally digital ones. One unknown factor is what it will cost to set up and run a common data space. At this point we don’t have an adequate way to price data, so this question remains unanswered. Other questions include: How can we quantify the value of new data-driven business models vs traditional business models? And how can we pinpoint the strengths and weaknesses of data ecosystems and technologies?

The answer to all of these questions at present is that we are all on a journey with common data spaces. We improve every day and the answers will come. But it is hard to imagine that the massive contribution of sharing data to the common good will not outweigh the costs and barriers that need to be overcome.

Above all, the decentralized model depends on participants’ willingness to share data. That means they must trust the other participants and the infrastructure. There is no other way to build trust except enabling people to say no. Letting people choose in itself invites trust.

Europe can do data differently

Data spaces are a way for Europe to reap the benefits of data for economic growth and positive societal outcomes, while affirming European values in the digital domain. They remain an integral part of the European strategy aiming to make the EU a leader in a data-driven society.

Find out more

Peter Kraemer will speak about the future of data sharing in Europe at the Data Spaces Symposium in Warsaw on 11-12 March. Register at