From Systems of Record to Systems of Truth: How Context is Revolutionizing Banking

Date: 24-02-2025

For years, banking technology has been divided into systems of record and systems of engagement.

• Systems of record stored the “master” versions of critical data—core banking systems for account data, CRM platforms for customer data, and risk management systems for compliance records.
• Systems of engagement facilitated interactions with customers, whether through online banking apps, ATMs, call centers, or relationship management tools.

This model worked—until it didn’t.

With the rise of AI and ever-evolving customer expectations, this traditional bifurcation is no longer enough. Banking is now shifting toward systems of truth and systems of context.

This new approach doesn’t just transform how banks manage data—it redefines how they serve customers, manage risk, and drive operational efficiency.

From Systems of Record to Systems of Truth: The Data Revolution in Banking

Historically, systems of record combined data storage with business logic. Each platform (CRM, core banking systems, compliance tools) had its own tightly coupled database, dictating what data could be stored and how it was used. This structure created silos and limited visibility across the organization.

Enter the cloud data warehouse/lakehouse revolution. Platforms like Snowflake and Databricks now allow banks to house all data—transactions, customer profiles, credit histories, and operational metrics—in a centralized, scalable environment.

But there’s a catch: While these platforms store vast amounts of data, they don’t inherently resolve data conflicts or standardize information across different systems. For instance:

• A customer’s address in the mortgage system might not match the one in the credit card system.
• Transaction data may be recorded differently across payment platforms.
• A customer flagged for potential fraud might simultaneously be working with the mortgage team for loan approval, without either team having full visibility.
• Relationship managers may know about a client’s investment portfolio but remain unaware of recent service complaints or branch visits.

Without context—the connective tissue that links data points to people, processes, and real-time events—banks are left making decisions in isolation rather than with a comprehensive understanding of the customer or the operational landscape.

That’s where systems of truth come in. They arbitrate what is correct and canonical data, ensuring that the entire organization operates from a single source of truth (SSOT)—critical for compliance, customer service, and risk management.

In banking, master customer records, anti-money laundering (AML) databases, and regulatory compliance platforms now play the role of these systems of truth.

Why Systems of Context Matter More Than Ever in Banking

While systems of truth ensure data accuracy, they don’t inherently provide context—the situational awareness that transforms raw data into actionable insights.

Consider these banking scenarios:

• Customer Engagement: Knowing a customer’s account balance (truth) is useful. But understanding that they’re planning a large purchase, recently applied for a loan, and have had multiple service interactions provides context that enables personalized financial advice.
• Fraud Detection: Identifying a suspicious transaction is important. But understanding the customer’s typical spending behavior, recent travel history, and device usage provides context that improves detection accuracy.
• Risk Management: A borrower’s credit score is a data point. Knowing their recent financial decisions, employment changes, and economic conditions provides the context to make better lending decisions.

Stack View vs. Graph View: How Context Transforms Data Architecture

Traditionally, banks organized technology in a stack view—systems layered neatly on top of one another:

• Core banking systems at the base
• CRMs and compliance tools in the middle
• Customer engagement platforms at the top

While conceptually straightforward, this model is rigid and siloed.

Enter the Graph View:

Imagine a dynamic network where all data points—customers, transactions, products, regulations—are interconnected.

• Systems of truth form the center, ensuring data accuracy.
• Systems of context surround and connect that truth, dynamically linking data to people, processes, and real-time events.

This interconnected approach provides a 360-degree view of the customer and enables more responsive, personalized banking experiences.

AI Agents: Bringing Dynamic Context to Banking

The biggest leap in context comes from AI-driven systems that dynamically adjust to customer needs and operational realities.

Examples in Banking:

? Customer-Facing AI Agents: Imagine a virtual financial advisor that listens to a customer’s goals, accesses their full financial history (systems of truth), and provides tailored investment recommendations in real-time (systems of context).

? Employee-Facing AI Agents: Relationship managers using AI assistants can prepare for meetings with full visibility into a client’s recent activities, pending service requests, and financial goals—without digging through multiple systems.

? Fraud and Compliance Monitoring: AI-powered systems monitor millions of transactions in real-time, flagging anomalies within the context of customer behavior patterns and regulatory requirements.

? Loan Origination: AI agents evaluate not just credit scores but also employment trends, spending habits, and regional economic indicators—providing lenders with a comprehensive context for faster, fairer decisions.

Buyer-Side AI Agents: The Future of Customer Interaction

Looking ahead, customers may deploy their own AI agents to interact with banks, flipping the traditional dynamic.

• Instead of the bank pushing products, customer-owned AI agents will seek out the best options based on their individual contexts.
• Banks must ensure their systems of context are robust enough to respond to these intelligent, autonomous queries.

Activating Knowledge: The Competitive Edge for Banks

In an industry where trust, personalization, and efficiency are paramount, banks that can connect, contextualize, and action their data at scale will outperform their peers.

? Improved Customer Experience: Hyper-personalized services tailored to real-time needs. ? Reduced Risk: Context-driven insights that improve fraud detection and regulatory compliance.

? Operational Efficiency: Faster decision-making through connected, dynamic data systems.

Activating Knowledge: The New Competitive Edge in Banking

Collecting data has become table stakes. Activating that data—turning it into contextualized, actionable knowledge—is where the real advantage lies.

In an era where customers expect personalized, seamless experiences, and regulators demand transparency, banks that can connect, contextualize, and action their data at scale will win.

Depth of personalization + connected systems of truth and context = market leadership.

This approach not only enhances customer trust and loyalty but also streamlines operations and strengthens compliance.

Final Thoughts

As the banking landscape evolves, institutions that move beyond systems of record to embrace systems of truth and context will lead the future. By bridging data silos and enriching every decision with comprehensive context, banks can improve customer experiences, mitigate risks, and drive sustainable growth.

The question isn’t if banks should adopt this approach—it’s how fast they can make the shift.

Are you ready to transform your bank’s data into a competitive advantage?

Reference:https://chiefmartec.com/2025/02/meet-the-new-martech-stack-systems-of-context-and-systems-of-truth/