TAS and BlockInvest sign strategic partnership to accelerate innovation in Digital Assets and Capital Markets

The journey towards the Digital Euro has officially entered a decisive phase, marking a major milestone for the entire European financial ecosystem. On 9 July 2026, the European Parliament approved the launch of negotiations with the Council of the European Union on the proposed regulation establishing the new electronic currency to be issued by the European Central Bank (ECB).

Just a few days later, on 14 July, the ECB took another significant step by selecting 36 Banks and Payment Service Providers—from more than 50 applicants—to participate in the long-awaited pilot project. The initiative is scheduled to begin in the second half of 2027 and will run for 12 months.

The primary objective of the pilot is to support the ongoing preparatory work ahead of a potential official issuance of the Digital Euro. During this phase, a beta version of the currency will be tested by thousands of ECB and national central bank employees, who will use it for online transactions, in-store POS payments, and peer-to-peer transfers between individuals.

Against this backdrop, which also involves major international financial institutions, the Italian market has demonstrated remarkable readiness. Of the 36 selected participants, eight are Italian—Isybank, Monte dei Paschi di Siena, Nexi Payments, Numia, Poste Italiane, UniCredit, Satispay and Sella—highlighting the country's strong commitment to payment innovation and its ability to play a leading role in this transformation.

The Digital Euro is designed to complement cash while reducing Europe's reliance on non-EU digital payment schemes, thereby strengthening the continent's strategic autonomy. At the same time, its introduction presents banks with significant operational and strategic challenges but at the same time great opportunities for the pan-European system.

The first major issue concerns the IT investments required to adapt existing systems or build new infrastructures capable of supporting Digital Euro payment services. A second, equally critical challenge relates to liquidity management. Since Digital Euros can also be transferred from customers' traditional bank accounts to their Digital Euro wallets, banks could experience a reduction in deposits, with potential implications for their overall funding base. Finally, another important element of correlation with initiatives related to the digital euro will be the digital identity wallet, introduced with the update of the European eIDAS Regulation to eIDAS 2.0, under which the EU introduced the EUDI Wallet (European Digital Identity Wallet), which each Member State will be required to make available to its citizens.

Indeed, in order to open a “digital euro account” with a PSP selected by the citizen, the EUDI Wallet could provide the certified data and attributes required for a simplified onboarding process.

In this context of profound transformation, TAS positions itself as a strategic partner and technology enabler for the whole financial ecosystem. Our vision is based on a fundamental principle: the Digital Euro should not be managed as a standalone silo. Instead, traditional payment services will need to seamlessly integrate with the new generation of digital asset services.

To address this evolution, TAS has developed a dedicated Digital Asset Management platform that extends well beyond the Digital Euro. The platform features a multi-product, multi-digital-asset hyperwallet and natively integrates stablecoins and other tokenised assets associated with Eurosystem DLT initiatives, including projects such as Pontes and Appia. It provides banks with a high degree of flexibility, enabling them to tailor and deliver the solutions and use cases that best meet the specific business needs of their customers, including retail customers, corporates and SMEs.

By TAS

For decades, the mainframe has been the beating heart of banking operations, and to a large extent it still is today: it manages the most critical processes in the financial sector with levels of reliability, security, and processing power that no other platform has ever truly matched.

However, the environment in which this heart operates has changed dramatically. Digitalization, cloud architectures, always-on services, and more recently artificial intelligence are redefining the way banks use applications and data. In this scenario, Mainframe Offloading has returned to the top of the IT agenda — no longer as a simple workload migration to the cloud, but as a strategic lever to make the mainframe’s application and information assets finally accessible, integrable, and capable of generating new value.

A new perspective on offloading projects

The reasons driving banks to take action are, as often happens, both economic and strategic. On the economic side, factors such as MIPS costs, infrastructure resource optimization, and the management complexity that has accumulated over the years are key drivers. On the strategic side, deeper challenges come into play: the growing difficulty in finding specialized mainframe skills, the pressure to deliver digital services faster, the need to integrate with cloud-native architectures, and the ambition to leverage enterprise information assets for advanced analytics and artificial intelligence initiatives.

The key point is therefore not replacing the mainframe, but rather redefining its role within a more open and flexible technology ecosystem.

This approach leads to the creation of hybrid architectures, where each platform does what it does best. The result is a more scalable infrastructure, capable of embracing new technologies without compromising operational continuity.

An AI Readiness project, not just an infrastructure initiative

The most interesting aspect of this evolution lies in the role that offloading plays in banks’ innovation journeys. The data stored on mainframes represents one of the most valuable assets of a financial institution, yet it often remains difficult to access for new digital services and analytics platforms, a paradox that many banks are well aware of.

Opening up this information to more modern environments means finally enabling intelligent process automation, AI-powered applications and autonomous agents, real-time analytics, shared data products across business functions, and services exposed through APIs to digital channels and partners.

From this perspective, Mainframe Offloading becomes a true AI Readiness initiative: it prepares the infrastructure so that data and business capabilities can power new application platforms, while preserving, wherever appropriate, the robustness of the core system.

A gradual journey, not a “Big Bang” migration

Offloading should not be approached as an all-or-nothing project. Experience shows the opposite: successful journeys are progressive, designed around each bank’s specific reality — applications developed over decades, layered customizations, regulatory constraints, and continuity requirements that make a one-size-fits-all approach unrealistic.

This is why every journey starts with a thorough assessment aimed at measuring expected economic benefits, project complexity, reengineering costs, performance impacts, security and compliance requirements, and business priorities.

Only after this analysis can a truly sustainable evolution plan be designed. In most cases, this results in hybrid architectures, where mainframe and new platforms coexist for a period of dual operation, ensuring service continuity while significantly reducing operational risk.

Challenges that should not be underestimated

A project of this nature also brings real challenges. Expanding the technology landscape introduces new cybersecurity and data governance considerations, while regulations such as DORA, GDPR, and PSD require rigorous control throughout the entire process.

There is also an aspect that is often underestimated: the cultural dimension. Transformation is not only technological; it involves skills, processes, and the way IT and business teams collaborate.

Addressing this journey with an experienced partner is often what makes the difference between a project that struggles to move forward and a successful initiative capable of delivering significant results within a reasonable timeframe.

TAS’s approach

TAS’s approach takes these aspects into account. We do not focus solely on technological migration; we support banks throughout a modernization journey built around the specific characteristics of each institution.

The process starts with an initial assessment designed to clearly evaluate costs, benefits, and priorities. From there, working closely with the Customer, we define a project and gradual migration strategy based on the temporary coexistence of platforms, while preserving operational continuity and application iso-functionality.

TAS’s cloud-ready solutions are designed to integrate with existing environments, enabling a progressive modernization of the IT ecosystem without ever disrupting day-to-day operations.

At the same time, TAS supports banks in opening up mainframe data and capabilities towards API-based architectures, cloud platforms, advanced analytics, and artificial intelligence initiatives, with the goal of transforming an infrastructure project into a concrete driver of innovation.

The real challenge, in fact, is making the value accumulated within core systems over years — often decades — of operations accessible, allowing banks to leverage it through new operating models, digital services, and emerging technologies.

In 2026, Mainframe Offloading is therefore becoming less and less an infrastructure migration project and increasingly an evolution project for the bank’s application and information assets.

This is the perspective through which we support our customers: preserving what continues to generate value, modernizing what limits innovation, and building an open, resilient technology ecosystem ready to address the challenges of the future.

By Nicola Mellini, Technical Sales Representative at TAS

TAS is focusing on an internal community to transform skills, processes, and operating models. From compliance to the knowledge base, through to application development, AI is becoming a widespread and structured workstream.

An internal community spanning across the organization is driving seven development workstreams in the field of artificial intelligence. The objective is twofold: to spread technological culture and skills within the organization and, at the same time, to rapidly bring concrete AI initiatives into execution. "The initiative represents the evolution of TAS Lab, which started in 2023 as an experimental space and has now been transformed into a more structured and widespread community model," says Fabrizio Annatelli, AI Program Lead, alongside Gabriele Paoleschi, from the TAS AI Community. "Adopting AI on a large scale is a journey that involves structure, processes, and above all, the organization. Therefore, we need to bring people on board, support them over time in understanding the technology's potential and risks, and develop new mindsets and skills."

Involving the company's workforce

Hence the explicit focus on reskilling, considered a key lever for managing the transformation. "Not riding the AI wave today means risking falling behind. But it’s not about replacing people: the scope of skills is shifting, as is the way they are utilized," underlines Annatelli. "TAS's response was to directly involve the company's workforce: the community, open to everyone, has seen widespread participation from employees across the company."

The TAS Community

The profiles involved are diverse: from developers to business unit and department managers. Everyone participates in the seven active AI workstreams, directly contributing to project definition and execution. "In this initial phase, we are focused on internal transformation and process efficiency," notes Annatelli. "We have structured pathways, organized into different sessions, to align the community on objectives and build a concrete execution plan. AI is not just an initiative confined to IT; it represents a cross-functional lever that requires organizational coordination and a clear structuring of processes."

The three key workstreams

Among the ongoing projects, three main directions emerge. The first concerns regulations and is among the most complex. "Working with banks and insurance companies," Annatelli premises, "a priority is to strengthen internal compliance safeguards and build a framework that clarifies what can and cannot be done with AI, in light of a continuously evolving regulatory landscape." Alongside compliance, the application development workstream leverages AI to support coding, in compliance with the principles of explainability and ethics. This is a more mature area, partly thanks to the widespread use of AI in software development, where the human-in-the-loop model remains central. However, the most strategic project is the corporate knowledge base. "AI models are generic by nature," highlights Annatelli. "Value is created when they are tailored to the specific context of the company. For this reason, we are building a structured knowledge base that includes regulatory, application, and business content. The knowledge base does not only serve to train models but enables a plurality of use cases: from proactive ticket management to generating analyses, all the way to supporting operational activities. AI agents specialize in specific tasks but do not make autonomous decisions: the responsibility always remains with humans."

From experimentation to industrialization

The community has already brought several workstreams into the execution phase and is preparing for an advanced training phase, particularly on the knowledge base. "We are structuring the system to ensure localized data governance for critical content. For the most sensitive use cases, a dedicated LLM infrastructure based on open-weight models is being trialed, with an initial summary expected by May," says Gabriele Paoleschi. "This allows us to securely manage large volumes of critical documentation. Another key element is the evolution of the operating model: no more rigid technological constraints, but adaptive processes, because AI evolves too quickly to be locked into static architectures. We need a dynamic approach capable of continuously updating itself, also in line with regulatory developments."

The first Use Cases: Between Electronic Payments and T+1 Settlement

Alongside the main workstreams, TAS is developing vertical applications, particularly in the electronic payments sector. Predictive artificial intelligence is already being used for transaction monitoring in fraud prevention using mature techniques. Generative AI enables a broader scope of applications: from the corporate knowledge base to decision support for T+1 settlement, from an advanced GUI design framework for banking applications to advanced reporting systems based on natural language. "A concrete case concerns the transition to the T+1 regulation, which reduces financial transaction settlement times to one day," explains Paoleschi. "We have developed proofs of concept to support matching and reconciliation activities, which are increasingly critical today due to shortened timelines, and here AI provides decision support by intercepting anomalies and suggesting possible corrective actions."

A look at the market

Finally, TAS continues to closely monitor market developments to integrate AI into its offerings in a way that aligns with customer needs. "We are in a phase of continuous scouting," concludes Annatelli. "AI is evolving rapidly and requires a pragmatic approach to be integrated into the business at scale. It is not a vertical technology, but a cross-functional capability that must be structured and governed."

English version by TAS

Original article from AziendaBanca by G.C. : https://www.aziendabanca.it/notizie/tecno/tas-nasce-la-community-per-scalare-ai

Within the Capital Markets industry, Intelligent Applications are rapidly evolving from a technology paradigm into a strategic lever for operational efficiency and value creation.

These applications represent the convergence point between traditional functionalities and advanced AI and Machine Learning capabilities, enabling new levels of automation, adaptability, and actionable insights.

This paradigm is built on five key design principles that guide both development and adoption.

Dynamic user experience enables applications to adapt to operational context and user profiles, delivering personalized interfaces and workflows. Embedded intelligence integrates AI directly into business processes, making it a native component of execution, control, and analytics activities. Autonomous orchestration enables end-to-end management of complex processes by combining automation, process mining, and adaptive workflows to reduce manual intervention and improve exception handling capabilities. A modular and reusable architecture, based on composable logic and API-first principles, enables the development of flexible and scalable solutions that can be rapidly integrated into existing ecosystems. Finally, unified and contextual operational data management helps overcome fragmentation and application silos, enabling advanced analytics and AI-driven functionalities.

While these principles define a clear architectural framework, the real challenge today is translating this vision into concrete use cases capable of generating measurable impact across core processes and the overall operating model.

BOSS: The Intelligent Application for Capital Markets Back Office

In this context, the evolution of our BOSS solution represents a key step forward. BOSS has been designed as an intelligent ecosystem supporting the entire front-to-back value chain within Capital Markets. The objective is clear: to bring the principles of Intelligent Applications into securities and derivatives back-office processes, historically characterized by high operational complexity and significant cost pressure.

BOSS will natively integrate AI capabilities — including LLMs, Machine Learning, and Generative AI — into operational workflows, alongside process mining, RPA, and agentic orchestration components. This approach not only automates repetitive tasks but also introduces predictive and advanced decision-support capabilities, particularly in processes such as settlement, reconciliation, and corporate actions management.

At the same time, the adoption of a modular, API-first architecture enables a composable approach in which capabilities can be progressively introduced, reducing time-to-value and simplifying integration with existing application ecosystems.

Reducing Cost to Serve: From Operational Efficiency to Scalability

The primary value driver behind this evolution is the reduction of Cost to Serve across back-office operations. In the sell-side environment, this cost is largely driven by human resources, which typically account for approximately 50–60% of the total.

This is precisely where Intelligent Applications deliver the most significant impact. Intelligent automation and process orchestration enable organizations to structurally reduce operational effort, while simultaneously improving process quality and resilience.

Analyses conducted across the main use cases show that operational effort reductions in the range of 20–30% are achievable, generating substantial economic benefits or, alternatively, enabling business volume growth without proportional increases in headcount.

Importantly, the value extends beyond direct cost savings. The real transformation lies in the ability to industrialize processes, reduce dependency on manual activities, and enable more scalable and data-driven operating models.

From Principles to Use Cases: Where Value Is Generated

The transition toward Intelligent Applications is already materializing through a range of use cases identified within BOSS, targeting the most operationally intensive areas of the back office.

Among the most relevant examples, intelligent automation of trade ingestion and validation significantly reduces manual activities and data-entry errors while improving upstream data quality. Downstream, anomaly detection models proactively identify inconsistencies and abnormal patterns, reducing operational risk and rework.

Another high-value area is settlement fail management, where Machine Learning capabilities enable true “Operations Copilot” functionalities. The application can automatically identify the root cause of issues, recommend corrective actions, and support operators during resolution, significantly reducing handling times.

In the areas of position and collateral management, predictive and optimization models improve both the quality of available information and the ability to support higher-value decisions, such as optimal collateral allocation, with direct impacts on funding costs and balance sheet efficiency.

Finally, the evolution toward operational “control tower” models, enhanced by anomaly detection and intelligent prioritization capabilities, enables proactive end-to-end process management, improving operational resilience and service quality. In this context, the introduction of natural language explanation layers and operational copilots represents a further evolutionary step, making data and insights immediately accessible even to non-specialist users.

The Opportunity

Intelligent Applications now represent a concrete opportunity to rethink the Capital Markets operating model. With BOSS, we are turning this vision into reality by developing targeted use cases that combine technological innovation with tangible business impact.

The journey is already underway: the next step will be scaling these capabilities and supporting our clients in adopting a more efficient, intelligent, and future-ready operating model.

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