Embracing Data, AI, and Emerging Technologies for Competitive Advantage

Francesco Giordano, Head of Data Technologies, Analytics & BI Solutions, General Investments Group

Francesco Giordano, Head of Data Technologies, Analytics & BI Solutions, General Investments Group

Through this article, Francesco Giordano, Head of Data Technologies, Analytics & BI Solutions, explores the evolving role of data, AI, and emerging technologies in asset management. He discusses the challenges of applying machine learning in financial markets, the need for scalable data infrastructures, and the transformative potential of quantum-ready technologies. He also examines how asset managers must adapt to technological advancements such as AI, blockchain, and cloud computing to remain competitive. He concludes by emphasizing the critical role of data strategy and innovation in shaping the future of the industry.

Driving Data and AI Transformation: My Role at Generali Asset Management

My current role ensures that Generali Asset Management effectively utilises its informational assets by efficiently applying technologies, best practices, and existing technological resources. This role is akin to a VP of Engineering but emphasises Data and AI components.

My teams are responsible for developing and managing internal solutions, including data products, data infrastructures, service portals, generative AI, and machine learning products, while ensuring the application of engineering best practices. Additionally, one of my key responsibilities is to guarantee that our data strategy is implemented optimally, driving the profound transformation we expect over the coming years.

Navigating Complexity in Financial Markets: Challenges in Machine Learning Applications

The primary challenges stem from the inherent complexity of financial markets. Financial markets are difficult environments to navigate, and the nature of time series data presents intricate mathematical challenges that must be approached scientifically. Additionally, the sector's highly competitive nature complicates matters further. In this context, it is uncommon to find readily available literature on effective strategies for machine learning, as any information that could provide a competitive edge – if any – would typically not be publicly accessible.

Building a Scalable Data Infrastructure: Enhancing Collaboration and Performance in Asset Management

Asset management firms are inherently complex organisations that combine specialised expertise from various fields. As a result, different organisational units often have diverse backgrounds, languages and cultures, depending on the market segments and product lines they manage or their specific challenges.

In this context, ensuring efficient information flow between teams while controlling operational costs represents a significant competitive advantage. A data infrastructure that fully meets these needs must enable a federated development model for data products. This means managing data truly as a product, which entails several key aspects: a strong focus on user needs, immediate comprehensibility of data models and data points with clear data contracts, and maximum integrability ensured through industry standards.

Moreover, the data infrastructure must be flexible enough to adopt new technologies that rapidly enhance performance or functionality. Achieving these goals requires applying platform engineering best practices, DevOps methodologies (including DataOps, MLOps, and LLMOps variations), and Site Reliability Engineering.

The Potential of Quantum-Ready Technologies: Advancing Optimization in Asset Management

I am convinced the answer is yes. In Generali Asset Management, we already had the opportunity to test the potential offered by quantum-ready technologies by implementing optimisation algorithms based on quantum models. These algorithms have delivered significant performance benefits by leveraging GPUs extensively for calculations.

When used correctly, these models can provide significant advantages in resolving complex optimisation problems frequently encountered in risk management and financial markets.

The Future of Asset Management: Navigating Transformation and Embracing Technology

The industry is undergoing a significant transformation and facing a profound identity crisis. On the one hand, the sector is experiencing similar changes as seen in banking, particularly concerning the scale of incumbents: firms must reach a sufficiently large scale to optimise operational costs and regain competitiveness against unmanaged and passive investment alternatives.

However, inorganic growth cannot be the only path forward. The market expects synergies to generate more significant results than the sum of their parts. Moreover, history has shown that mergers and acquisitions rarely lead to long-term efficiency gains; if anything, they often result in the opposite.

On the other hand, new technologies—such as widespread AI adoption, blockchain, and quantum computing—will create a clear divide in the coming years between those who successfully seize these opportunities and invest significantly in developing these strategic capabilities and those who cling to traditional asset management models and may eventually become obsolete.

In either case, Asset Managers will increasingly evolve into “Tech Companies” in the coming years. The best among them will adopt a product-driven approach to developing internal solutions, serving as the foundation for delivering tomorrow’s services. Additionally, to ensure better interoperability between firms, those who have managed their technological assets based on market standards will be better positioned to facilitate smoother integration between companies.

Finally, data. One of the critical success factors will be the efficient management of informational assets. The ability to effectively handle data—leveraging it to derive insights that drive decision-making—will distinguish the leaders of tomorrow.