What to demand from a finance data solution built for banks and insurers

The current market offers many data integrators and data warehouses. But banks and insurers require solutions specific to their finance, risk and regulatory needs.

Current generic data solutions do not provide the finance semantics, scalability, auditability, real-time integration, multi-domain coverage, intuitive design or centralized data that can help financial institutions manage their data complexity.

For example, even with modern data stacks, regulatory change is still absorbed manually in financial institutions. In a 2024 global regulatory reporting survey, 40% of firms said they rely on manual processes to adapt to new rules and nearly 40% spend 10+ hours a week fixing data-quality issues.¹

Only a purpose-built solution for banking and insurance that is supported by deep industry and technology expertise can manage the uniqueness of the industry’s data requirements.

Here is what forward-looking financial institutions must look for in a finance data solution to enable greater transparency, speed, usability and governance across the entire organization.

1. Pre-built finance-specific content that accelerates compliance

Finance data solutions built for regulated industries come with embedded financial semantics, such as posting logic, chart-of-accounts hierarchies and mappings to frameworks like BIRD and IReF.

This allows firms to adapt to accounting and regulatory changes without manually recoding or rebuilding data models.

Configurable finance-specific templates let business teams modify rules and mappings directly, maintaining traceable data lineage from source transactions to regulatory outputs.

The outcome is faster implementation of new requirements and lower operational risk.

2. Real-time and event-based data processing for high adaptability 

Modern finance data solutions must go beyond batch schedules. Banks and insurers need solutions that support time- or event-based data-quality processing that reacts as transactions occur.

For example, this means running accuracy and completeness checks inside the finance system itself without exporting or duplicating data, and immediately surfacing issues through dashboards and workflow alerts.

Reusable rule templates and embedded remediation workflows enable early detection, continuous monitoring and clear audit trails.

A solution that operates this way scales easily to new reporting demands such as ESG disclosures, IFRS-based reporting or real-time liquidity monitoring, reducing latency and integration overhead.

3. Business-friendly UI with self-service reporting and validation

Finance data solutions built for banks and insurers should enable business users to configure, monitor and validate data themselves, whenever they want, without IT intervention.

A finance-aware interface allows users to independently adjust mappings, hierarchies and validation rules directly, using standardized templates and guided workflows.

This reduces operational friction while accelerating reporting cycles, keeping finance teams in control of their data definitions.

Self-service capabilities also improve data governance, as ownership remains with the domain experts who understand the regulatory and accounting context. This means faster adaptation to policy changes and lower IT workload for routine adjustments.

4. Audit-ready architecture with embedded data quality and traceability

Finance data solutions for banks and insurers should provide continuous data validation and full lineage tracking across all connected systems. Built-in data-quality rules ensure accuracy and completeness at every processing step, while metadata and lineage views allow users to trace each figure back to its source transaction.

This reduces manual reconciliation, simplifies audits and strengthens confidence in regulatory submissions. When data quality and traceability are embedded at the solution level, institutions can maintain compliance even as reporting volumes, systems and data sources grow.

A finance-specific solution also strengthens operational resilience by embedding governance, validation and monitoring across data flows. This is key to meeting emerging regulations such as DORA and the PRA’s operational resilience requirements.

5. Unified data foundation that spans accounting, finance, risk and regulatory domains

Finance data solutions for the financial services industry need to extend beyond accounting to include risk, regulatory and reporting domains within a single data model. A shared semantic layer ensures consistent definitions for exposures, valuations and capital metrics across all functions.

When finance, risk and compliance teams work from the same governed data foundation, reconciliations disappear and reporting cycles shorten.

This cross-domain structure also simplifies new reporting demands, such as Solvency II templates or climate-risk scenarios, by allowing these datasets to link naturally to existing financial and risk data.

6. Scalable and modular foundation for continuous evolution

A modern finance data solution should be scalable and modular, allowing banks and insurers to add new capabilities (e.g., data quality monitoring, ESG reporting or AI-based analytics) without rebuilding existing systems.

Industry-specific architecture and expert support ensure that the solution evolves in step with regulatory change and emerging technology. High-quality, well-governed data is essential not only for advanced use cases like AI but also for the daily accuracy of finance, risk and compliance operations.

This combination of modular design and embedded governance protects long-term investment while keeping data reliable and audit-ready as requirements grow.

7. Strong data foundations that enable AI readiness

As regulatory complexity increases and AI becomes integral to finance operations, the quality, structure and lineage of financial data will directly determine the success of AI initiatives.

This is why banks and insurers must look for a data solution that prepares them for AI adoption and prioritizes AI enablement rather than treat it as an add-on. A finance- and AI-ready data foundation provides consolidated, accessible data across all systems through shared semantics and traceable lineage. This ensures AI models work with complete, accurate and explainable data, satisfying the supervisory bodies (e.g., ECB, PRA) that are increasing scrutiny on AI in finance.

According to the 2024 joint survey by the Bank of England and FCA, 75% of UK financial-services firms are using AI, yet only 34% report having complete understanding of how those models work. This underscores supervisory concerns about transparency and explainability of AI. Only data solutions with embedded lineage and traceability meet these new standards.

Equally important is a structured back-end architecture that is capable of managing billions of daily records and transactions and supports consistent validation and fast retrieval. Solutions built on proven enterprise technology, such as SAP or proprietary frameworks, allow AI to operate at scale without compromising auditability.

AI-ready solutions also embed machine learning in the data foundation to enable anomaly identification, pattern-based reconciliation and automated mapping.

For example, consolidating branch-level loan data into a unified semantic model allows AI to detect delinquency trends faster. In insurance, integrating claims data and unstructured files within a single framework enables AI to spot fraud indicators in real time.

Firms that invest in a strong, unified data foundation can deploy AI responsibly and efficiently, turning automation into measurable insight.

Make finance, risk and regulatory data fit for purpose

Generic data platforms can connect systems, but they rarely deliver the finance semantics, controls and traceability financial institutions need to keep pace with regulatory change and run the business with confidence. The solution is treating finance and regulatory data as a domain problem and prioritizing solutions that embed governance, auditability and cross-domain consistency from the start.

If you’re assessing how to reduce manual remediation, accelerate change and strengthen transparency across finance, risk and compliance, consider SAP Fioneer’s Finance Data Suite as a reference architecture for purpose-built finance data foundations in regulated institutions.

Explore the Finance Data Suite and see how it supports your finance, risk and regulatory requirements. Book a demo today.