Clean, GL-ready data is the missing layer in FinTech and RegTech. Learn why FS needs a true data and subledger partner to deliver trust and automation.
Most finance-ops tools promise faster close, cleaner reconciliations, and better controls. However, data management in financial services presents unique challenges that stop those promises at the door. The missing piece? A financial services data platform partner that ingests, normalizes, and subledgers complex financial data before it ever hits your workflows.
Great platforms underdeliver in financial services not because their features are weak, but because their inputs are. Without clean, general ledger-ready data, even the best reconciliation, close, and compliance tools fight uphill.
Financial services stacks are older, deeper, and more fragmented than other industries. According to a recent banking technology study of senior IT decision-makers, 55% of banks say legacy core banking systems are the biggest obstacle to achieving business goals.
This fragmentation creates real operational challenges. Industry voices report that the average bank runs 10-15 core systems that don't naturally talk to each other. If you sell close, controls, or compliance into that environment, you inherit the sprawl and its downstream exceptions.
Nearly 40% of CFOs in financial services and other industries say they don't completely trust the accuracy of their organization's financial data—a painful admission that surfaces every time automated systems encounter inconsistent inputs.
Many financial institutions also continue to suffer outages and operational risks linked to legacy and complex IT, reinforcing how brittle the foundation can be. Meanwhile, regulators and customers aren't waiting for institutions to modernize.
Your product may deliver strong performance across core finance functions. Yet, inconsistent or unstructured upstream data turns every engagement into a custom project—requiring one-off connectors, ad hoc rules, and endless cleanup efforts. The result: Data management in financial services is expensive, fragile, and slow when handled through traditional integration methods.
According to Deloitte's 2025 banking and capital markets outlook (citing a 2024 survey), more than three-quarters of banks plan to increase investment in data management and cloud—because robust data foundations are now a precondition for AI and automation. The old adage holds: Garbage in, garbage out. Without clean data, AI just produces garbage faster.
To consistently win in financial services, you need a data and subledger layer that delivers:
Essential Capabilities
This approach creates a financial services data platform that doesn't replace your capabilities—it amplifies them by ensuring the data you receive is structured and ready for scale across the financial services vertical.
When you add an ingestion and subledger tier to your platform, the benefits appear quickly:
When entries arrive standardized and traceable, exception queues shrink and automation rates rise—directly addressing the trust gaps CFOs report.
Process benchmarking organization APQC explicitly ties better data governance, common data definitions, and standardized charts of accounts to faster month-end close. Structure is the lever; impose it upstream and your close tightens downstream.
Instead of brittle, bespoke integrations for every client, you implement once against a prebuilt Common Data Model and subledger, reducing build/maintain overhead and speeding time-to-value.
Clear lineage from source to subledger to GL and back satisfies controllers and regulators while shortening the path to sign-off.
If your platform automates close, reconciliations, controls, or compliance, pairing it with an ingestion and subledger layer is essential. Without it, data management complexity keeps you from delivering the outcomes you promise—no matter how strong your features are. With it, you deliver clean, GL-ready data into your product, accelerate automation, and unlock the speed, accuracy, and trust your customers expect.
At Finray, we help leading finance platforms bridge the gap from fragmented systems to structured, auditable, GL-ready data. If you're building in this direction—or want to expand deeper into financial services—let's talk.
Contact Finray for a demo and discover how to win in financial services with clean, trusted data.
Why do finance-ops tools struggle specifically in financial services?
Financial services organizations typically run 10-15 core systems that don't naturally communicate, creating fragmented data that turns every implementation into a custom project requiring one-off connectors and endless cleanup efforts.
How does better data management impact month-end close times?
Standardized, traceable data eliminates manual cleanup and exception handling that delay closes. When you impose structure upstream through a Common Data Model, automation rates rise and your month-end close tightens downstream.
Why is clean data essential for AI and automation in financial services?
As Deloitte's 2025 banking and capital markets outlook notes, robust data foundations are now a precondition for AI and automation. Without clean data, AI just produces inaccurate results faster.
What does Finray's financial services data platform do?
Finray makes financial services data work for FinTech and RegTech platforms. We ingest fragmented data from multiple core systems, normalize it through our Common Data Model and subledger, and deliver clean, GL-ready entries—transforming complex financial services data into automation-ready inputs.