Smart Data Fabrics Enable Better Decision Intelligence in Banking

Decision intelligence has emerged as a strategic priority, enabling retail and commercial banks to make faster, more accurate decisions across fraud detection, credit risk, payments, and customer experience. However, despite significant investments in analytics and AI, many banks struggle to realize the full potential of decision intelligence due to limitations in their data architecture.

A recent report by Celent, commissioned by InterSystems, reveals that 60% of Tier 1 banks cite outdated or fragmented data systems as a major barrier to improving model performance. Traditional data platforms—such as data lakes and warehouses—often introduce latency, lack flexibility, and fail to integrate diverse data sources in real time. These shortcomings hinder model accuracy, slow decision-making, and compromise customer outcomes.

To overcome these challenges, banks are turning to a smart data fabric architecture. Unlike legacy systems, smart data fabrics enable real-time data access, integrate AI and machine learning, and operate non-disruptively alongside existing infrastructure. This approach supports dynamic, scalable decision-making across operational and analytical workloads.

This whitepaper explores how a smart data fabric architecture can be particularly effective in high-velocity use cases like fraud detection, credit origination, and customer intelligence. By connecting disparate systems and enabling real-time insights, they help banks improve model performance, enhance compliance, and deliver personalized services.

With 55% of banks planning to deploy data fabric architecture within the next year, the shift is well underway. The future of decision intelligence in banking lies in aligning data architecture with business ambition—unlocking actionable insights, improving agility, and driving sustainable growth.

For banks seeking to modernize their decision-making capabilities, smart data fabrics offer a powerful path forward.