Agile Data in Financial Services

In financial services, data is essential for storing product information, capturing customer details, processing transactions and keeping records of accounts; the relationship between products and their underlying data has always been symbiotic.

A significant amount of data infrastructure is static, fragmented across data silos or based on legacy platforms. This has created an impedance mismatch between products and the underlying data. Minor changes to customer journeys require major development work, which compromises the agility and competitiveness of digital product and service life cycle.

Despite significant investments that have been made in underlying data infrastructure, the ability of financial services organizations to use data to drive agility in their businesses is still limited.

In addition, we are witnessing the emergence of data and artificial intelligence (AI) centric organizations. That are adopting advanced analytic models and machine learning (ML) methods to create next-generation, agile, data-powered products and services.

Enabling the shift are three strategic technologies that provide a foundation to support the transition from existing infrastructure to an agile, data-driven environment:

• Agile data flows enable the operationalization of analytical and ML models, without significant development. At the same time, they maintain the agility of data pipelines, allowing data to iteratively flow freely through the organization and move away from static and batched data pipelines.
• Converged analytics and ML provide a powerful, unified, easy-to-use data modeling and analytical environment. This environment can be deployed in a wide range of scenarios, including quantitative research, business analyst functions and operations activities, enabling data to drive agility at all levels within the organization.
• High-speed data movement transitions data from data warehouses and data lakes to hybrid data architectures, such as “data lakehouses.” Leveraging high-speed data substrates allows raw data to be ingested and passed directly to analytic data engines and ML functions in near real time.

Leveraging these foundational technologies allows organizations to gain a competitive advantage. This approach reduces analytical and processing cycles and enables production-ready data flows, that:

• Enable more accurate credit decisioning, allowing for the incorporation of advanced ML algorithms across very large datasets in near real time.
• Automate wealth advisement and investment planning, tailoring advice and automating custom portfolio construction and management activities.
• Develop and maintain ML training pipelines, ensuring that models are updated with the latest data, and are accurate and auditable.
• Provide a high-performance computing (HPC) data platform for computationally intensive risk calculations and trading models.
• Analyze customer interactions, creating intelligent products and customer journeys optimized in real time, based on streaming interaction data.

Adopting these core foundational technologies for most organizations will most likely be challenging. Care needs to be taken to align data infrastructure with both business and organizational change, enabling a shift from a historical process-centric business model to a data- and AI-driven approach.

It’s clear that data infrastructure is undergoing rapid, fundamental changes at an architectural level. Making the right strategic choice is more important now than ever as we continue to move away from static hard-coded data flows and legacy analytical platforms to open agile, data-centric environments that can deliver real competitive advantage and top-line revenue growth.

Thomas De Souza is CTO of Financial Services at Hitachi Vantara.