Responsible Data Use in Financial Services

In financial services, data is both the main growth engine and the biggest liability. Banks, insurers, asset managers, and fintechs are leaning on data-driven systems to improve service, fight fraud, and win new customers. Yet the same systems can expose sensitive information, trigger regulatory scrutiny, and erode trust when they are built without firm standards and day-to-day oversight.

A 2025 FICO study of 254 C-suite leaders confirms the tension: enthusiasm for new data-driven capability is high, but operational maturity is uneven, with gaps in monitoring, standards adoption, and cross-functional alignment holding back results.

The same study shows a decisive shift: leaders now view responsible data practices as a growth driver—not just a regulatory duty—because they unlock reliable outcomes and measurable returns.

This article translates the study’s strongest findings into practical actions. You’ll see where most firms stumble (security basics, compliance consistency, and collaboration), how unified platforms change the economics, and a straightforward roadmap any financial institution can adopt—grounded in clear standards, constant monitoring, and a Private Data Network with a built-in AI Data Gateway that controls sensitive data flows by design.

Executive Summary

Main Idea: Responsible data use in financial services means embedding security, privacy, and compliance into every stage of the data lifecycle through clear standards, continuous monitoring, and unified platforms—often anchored by a Private Data Network with an AI Data Gateway that governs how sensitive information flows and is protected.

Why You Should Care: Without responsible data practices, financial institutions face rising breach risks, inconsistent compliance, and erosion of customer trust—threats that can trigger regulatory penalties, litigation costs, and lost market share. Firms that act now not only reduce risk but also unlock measurable returns, with leaders in the 2025 study reporting ROI gains of 50% or more from unified governance and secure platforms.

Key Takeaways

1. Security Gaps Put Data at Risk. Only 7% of financial institutions fully monitor systems once they go live, leaving sensitive information exposed. Without continuous oversight, performance issues and breaches can undermine even well-designed initiatives.
2. Compliance Needs to Be Unified, Not Fragmented. Most organisations still rely on scattered, siloed standards, making consistent compliance nearly impossible. A central governance structure with clear authority ensures policies are applied enterprise-wide.
3. Privacy Drives Trust and Business Value. Protecting customer data is both a regulatory duty and a competitive advantage. Firms that build privacy safeguards and auditability into every stage earn loyalty and avoid costly penalties.
4. Alignment Across Teams Multiplies Impact. Less than 6% of firms align data projects fully with business goals, leading to wasted spend and poor adoption. Joint planning and shared roadmaps close this gap and improve outcomes.
5. Unified Platforms Boost ROI. A Private Data Network with an AI Data Gateway enforces standards, prevents leakage, and simplifies compliance. Organisations that adopt unified platforms report ROI gains of 50% or more.

Why responsible data practices matter now

Trust is fragile. When confidential information leaks, customers move and the brand takes years to recover. Executives in the study rank customer experience, revenue growth, and board-level pressure among the top catalysts for change—evidence that responsible data use is no longer just defensive; it’s central to the commercial agenda (see catalysts on p.14). In parallel, leaders report that defining clear standards for safe, trustworthy use is a leading contributor to reliable, repeatable value (p.6–8). In fact, the data shows leaders are reallocating attention from flashy pilots to well-governed execution that scales.

The shift is pragmatic. Firms that embed strong security, privacy, and compliance into everyday processes reduce rework, avoid penalties, and move faster because they aren’t fighting fires. The study’s takeaway is blunt: the experimentation phase is over—value now depends on solid systems, clear oversight, and shared playbooks across technology and business (p.24–25).

The security gap: where programmes fall short

Monitoring after go-live is the weakest link. Only 7% of organisations report full adoption of monitoring standards for systems in production (see the “model monitoring” bar on p.11). In plain terms, once systems go live, most operate without continuous checks for errors, misuse, or drift—leaving blind spots that attackers and process failures exploit.

Infrastructure strains security. Leaders cite three blocking issues when scaling from pilot to production (p.13):

• Unpredictable system performance (62%)—teams can’t guarantee stable behaviour at scale.
• Storage and processing limits (58%)—throttling throughput and resilience.
• Gaps in real-time oversight (≈37%)—delayed detection of issues increases exposure.

When the underlying stack wobbles, even well-designed controls underperform.

Security standards are inconsistent. Around 16% of firms report full integration of data security and customer-experience safeguards—better than post-deployment monitoring, but still low for a high-risk sector (p.11). The picture that emerges: many institutions secure some pathways but not all, and the inconsistencies become the breach route.

What this means: Security must be treated as a lifecycle obligation. Protection starts with data intake, continues through development and testing, and sharpens after deployment with live monitoring and auditable reports. Anything less leaves unacceptable gaps.

Compliance and governance: the weak seam

The study shows only 12.7% of organisations have fully integrated development and deployment standards that underpin compliance (p.10–11). That includes bias checks, performance oversight, record-keeping, and secure data handling. Leaders describe the real problem: too many standards in too many silos—”101″ home-grown playbooks that make consistent compliance nearly impossible across business lines (p.12).

Committees and boards are being formed, but maturity varies. Several experts in the report note that governance is “catching up,” with most committees only active since late 2023 or early 2024 (p.9). Without authority, shared processes, and common metrics, review boards can’t prevent drift from agreed policies.

What this means: Compliance cannot be a late sign-off. It must be embedded as a design constraint—from requirements and data access rules to deployment gates and ongoing audits. Policy should be practical and tool-enforced, not just documented.

Data privacy: protecting customers and protecting the business

Executives rank privacy failures among their top worries over the next five years. Breaches, systems that misbehave in live environments, and vendor reliability all rise to the surface (p.22–23). Leaders across roles also rank security and privacy as a prime area for closer collaboration, signalling that privacy is moving from legal language to practical day-to-day controls (p.22).

The risks are not abstract:

• Breaches damage customers immediately and trigger investigations, class actions, and fines.
• Live failures expose data because logging, alerts, and rollback paths are missing or untested.
• Third-party gaps let sensitive data move to places without the same safeguards.

Best practice from the study: make privacy testable and visible. That means checks for harmful bias, plain-language explanations of important automated outcomes, and strict safeguards for personal information. When privacy is baked into the pipeline—and reported in a way executives and auditors can understand—trust goes up and incidents go down (p.10–12, p.22–23).

Breaking down silos: alignment is the multiplier

Less than 6% of leaders report that investments, development, infrastructure, and end-user needs are fully aligned with business goals (p.15–16). In practice, this looks like:

• Teams building in isolation with different vocabularies and metrics.
• Business sponsors who expect results that the technical plan never targeted.
• Risk and compliance pulled in late, forcing last-minute changes or rollbacks.

The study quantifies the causes. Insufficient collaboration between business and technology (72%), a lack of shared understanding (66%), and no single, unified strategy across departments (58%) are the standout blockers (p.17). The result is predictable: solving the wrong problems, using the wrong data, and designing for the wrong measures of success.

What fixes it: A shared intake process; joint scoping workshops; a single requirements document that lists customer impact, risk controls, and measurable outcomes; and regular joint reviews that include business, technology, risk, compliance, and customer support (p.16–18). The more teams think and plan together, the less rework and exposure later.

The unified platform advantage

Most firms still run DIY, team-by-team stacks. That brings speed in the short term, but it multiplies tools, doubles work, and hides risk. The study shows a different path: unified platforms create shared standards, shared tooling, and shared oversight for development, release, and live operations. Leaders estimate that a common platform and tighter collaboration could boost returns by ≈50% or more; a quarter believe returns could double (p.19–20).

Why unification works:

• Consistency: One place to apply controls, review logs, and audit activity.
• Efficiency: Less duplication across business lines; fewer hand-built connectors.
• Scalability: Proven patterns reused, not re-invented, so rollouts speed up.
• Accountability: Clear owners, clearer reports, simpler attestations.

Expert voices in the study stress that a strong platform is the fastest way to enforce standards at scale and reduce the skills burden on individual teams (p.19–21). In short: fewer silos, fewer surprises, faster value.

Practical roadmap: building responsible data systems

5 steps to build a responsible data framework in financial services

Step 1: Define clear security and privacy standards.

Create baseline controls for access, logging, resilience, harmful-bias checks, and plain-language explanations of important automated outcomes. Standards must uphold customer rights and meet regulator expectations across jurisdictions (p.10–12). Publish them, train teams on them, and tie them to release gates.

Step 2: Establish governance boards and audit structures.

Stand up a cross-functional governance board—technology, data, risk, compliance, legal, operations, and front-line business. Give it authority to approve designs, set control requirements, and run scheduled audits. Define escalation paths and include customer-impact reviews for high-risk changes (p.9, p.12, p.18).

Step 3: Embed monitoring and reporting at every stage.

Don’t wait for go-live. Instrument systems in development and pre-production to catch issues early. In production, require live dashboards, alerts, and rollbacks. Use an AI Data Gateway—implemented as a core service inside your Private Data Network—to control how sensitive information is requested, transformed, retained, and shared. The gateway enforces approved uses, redacts or tokenises fields when needed, and outputs complete audit trails for internal review and external examiners (addresses the 7% monitoring gap noted on p.11 and the real-time oversight gap on p.13).

Step 4: Align business, compliance, technology, and customer teams from day one.

Make joint scoping and test-plan reviews mandatory. Capture business value targets, customer protections, and regulatory checks in the same document. Keep sponsors in the loop through short, regular “show-me” sessions so expectations track reality (p.15–18). This closes the 72% collaboration gap and reduces costly rewrites later.

Step 5: Scale through unified platforms.

Adopt a Private Data Network (PDN) as the backbone for responsible data exchange. The PDN standardises identity, encryption, logging, retention, and includes the AI Data Gateway as its access-control layer. With one platform, it’s easier to enforce policies, reuse patterns, and prove compliance. The study’s ROI estimates for unified platforms—≈50% uplift or more—show why this is the fastest route to safer scale (p.19–21).

Outcome: These steps replace ad-hoc projects with governed systems that protect sensitive information, lower compliance risk, and build trust with customers and regulators alike (p.24–25).

Conclusion: the winners will be the most accountable

The message from 2025 is consistent. Leaders are shifting away from one-off pilots and unmonitored launches to systems that are secure by default, private by design, and compliant by construction. They’re doing it because it works: fewer incidents, cleaner audits, and a clearer path to scale. The study’s data shows where to focus—close the monitoring gap (only 7% fully integrated), end the “many standards” problem, and unify the platform to lift ROI by around 50% or more (p.11, p.12, p.19–20).