The Integrated Data Overlay Approach to Solving Banks’ Data Challenges

Banks are under intense pressure from a variety of quickly evolving trends. Clients want more from them; market volatility is challenging their trading strategies; and regulator scrutiny is deepening.

In response, regional banks and large banking organizations (LBOs) are offering more products to clients and promising ever-more automated access to a greater array of asset classes. If that wasn’t enough to be dealing with, the mid-tier banks are also upgrading their retail and commercial banking operations.

Artificial intelligence (AI) and advanced machine learning (ML) hold the promise of streamlining and managing those transformations. They also pose new risks: without good quality and trusted data, these new technologies can find their capabilities are and prone to potentially costly operational and compliance errors.

Mid-tier banks, however, don’t always have the best setup to ensure data trustworthiness. Their often fragmented legacy tech stacks can be incompatible with the processes of modern data architecture.

A solution is at hand. Many banks have adopted an integrated data overlay strategy, establishing a dynamic, enterprise-wide data layer that unifies structured and unstructured data in real time. This strategy has the benefit, too, of providing a data foundation that will enable the integration of future technologies, especially AI-powered tools, that will drive continued growth and development.

In this white paper, we will examine:

• The gathering pace of external economic and regulatory pressures that LBOs and other mid-tier banks are facing
• The operational pain points that are being exposed by these new challenges
• The technological aspirations of banks in response to their changing business environments
• The difficulties of applying those to ill-suited technological infrastructure
• The elegant solution offered by integrated data overlays