Rethinking Data as a Strategic Asset for Banks

Tightening margins and digital disruption compel banks to reimagine business models and balance sheets. Data—often buried in legacy systems or fragmented across silos—remains an undervalued and underleveraged asset. Banks that elevate data to the level of financial capital will outperform those that treat it as operational exhaust.

Hidden Asset on Every Balance Sheet

The modern bank is a data company in disguise. Every transaction, every risk assessment and every customer interaction generate an exhaust of digital signals. However, less than 1% of banks actively leverage these signals into strategic advantage.

Meanwhile, new competitors are using AI-native models to extract intelligence, deliver personalization and minimize regulatory risk at scale.

Three converging pressures make it urgent for banks to treat data as a balance sheet asset:

• Margin Compression: Interest rate cycles and cost pressures demand operational efficiency at scale
• Regulatory Scrutiny: Basel III, DORA and BCBS 239 demand fully auditable and explainable data pipelines
• Digital Disruption: Eroding traditional customer relationships with data-native experiences championed by Fintechs and big tech

From Siloed Data to Monetized Intelligence

AI can transform how banks collect, govern, analyze and monetize data through four core pillars:

• Data Modernization: AI empowers banks to transform fragmented information into strategic business intelligence, delivering real-time visibility into customer portfolios and enhancing precision in decision cycles. With modern architectures, such as data mesh and lakehouse frameworks, banks can overcome data silos and enable domain-driven, self-service analytics. These strategies also bolster financial risk management through early warning systems that enable proactive mitigation, while automated reporting streamlines compliance. By integrating data from multiple sources, business teams reduce manual data entry, freeing up resources for in-depth customer portfolio analysis and strategic decision-making.
  Banking professionals who leverage AI and advanced data management capabilities drive innovation and enhance decision-making, significantly contributing to strategic goals. These advancements not only optimize operational efficiency but unlock new opportunities for growth and competitive advantage.
• AI and Analytics: AI-driven personalization in banking is aimed to enhance customer engagement with solutions across the value chain, from customer prospecting to acquisition to servicing. High-quality, well-governed and real-time data enables AI models to deliver accurate predictions, personalized experiences and proactive services. Without clean, diverse and compliant data, AI initiatives risk bias, inefficiency and regulatory breaches.
• Governance and Trust: AI-driven decisions impact credit, risk and compliance, becoming a strategic imperative for banks to adopt governance and trust. Regulators and customers demand transparency, making explainability, traceability and audit-readiness non-negotiable. Emerging trends, such as Explainable AI (XAI) for model interpretability, automated compliance through policy-as-code and real-time audit dashboards are paths to ensuring AI guardrails. Data lineage and metadata management ensure traceability across hybrid environments, while bias detection frameworks safeguard fairness in credit scoring and underwriting. Global regulations like EU AI Act, DORA, and Basel III are accelerating adoption of governance-first architectures.
• Data Monetization: With the growth of API banking and Fintech ecosystem, banks can move beyond operational efficiency to generate revenue from customer data. Open banking regulations allow customers to take control of their banking data and seek new products and offerings from banks and Fintechs.
  Innovative models such as selling anonymized consumer spending insights to retailers and governments, offering API-based data services to Fintechs for account aggregation and externalizing fraud detection capabilities as “Fraud-as-a-Service” are some of the use cases being adopted by banks. Banks partner with advertisers for transaction-enabled marketing campaigns, creating new revenue streams from precision targeting.

Path Forward

Banks that treat data as a capital asset—subject to investment, governance and performance management—will unlock new economic levers. This isn’t just an IT initiative. It’s a boardroom imperative.

Persistent enables that transformation, helping banks reclassify, re-platform and realize value from their most untapped asset: data.

Key Steps to Operationalize Data as an Asset

To move from vision to value, banks must take a structured, pragmatic approach to transforming data into a managed, monetizable asset. Below are five key steps to guide that journey:

• Establish Data as a Strategic Priority: Treat data as an enterprise-wide initiative owned by both business and technology. Define a data mission aligned to business outcomes—revenue growth, risk reduction, or regulatory readiness.
• Modernize Data Architecture: Break down legacy silos by migrating to cloud-native, scalable platforms that unify structured and unstructured data. Enable real-time analytics, scalability and performance at lower cost.
• Embed AI and Analytics in the Workflow: Use AI to operationalize insights across credit scoring, KYC, fraud detection and personalized marketing, by integrating models into core business processes and decision systems.
• Build a Data Governance and Trust Layer: Implement governance frameworks that enable explainability, transparency and regulatory compliance. This includes metadata management, lineage and ethical AI practices.
• Productize and Monetize Data Assets: Identify internal data assets that can be packaged as services or insights for external partners (e.g., ESG scoring, benchmarking tools). Establish operating models for value realization and monetization.

Turn Vision into Execution

Data proves to be a lever for operational efficiency when supported by AI/ML algorithms. Persistent helps banks eliminate human error and enhance reliability with AI-led data operations that significantly improve decision-making capabilities. We helped a leading bank in the APAC region achieve 92% accuracy in data extraction and decision-making across various functions. Within a year, the bank experienced a remarkable 40% improvement in productivity, reducing transaction turnaround time from 30-45 minutes to seven-to-eight minutes.

By automating data migration, enhancing observability and seamlessly integrating across cloud platforms, Persistent enables banks to unlock value from data swiftly, securely and at scale.

We deliver tangible benefits for banks seeking to modernize their data capabilities with iAURA, our AI-driven data modernization accelerator, tailored for complex financial data.

Banks can speed up modernization by to 50%, reduce operational costs by as much as 60% through automation and improve AI model accuracy by up to 80% with robust data governance and clean foundations. iAURA ensures minimal business disruption with phased migration strategies, while empowering business users to easily extract insights using natural language.

Contact us today to take the first step in transforming your data into a competitive advantage.