The 2025 Financial Services & Insurance Pulse: A Sector at a Turning Point

Based on findings from the Dun & Bradstreet Financial Services & Insurance Pulse Survey 2025, interpreted by Dajon Data Management.

A sector ready to modernise – but held back by data

The Dun & Bradstreet Financial Services & Insurance Pulse Survey 2025 presents a clear but challenging message for the industry: Financial Services and Insurance (FS&I) organisations are ambitious about AI, yet constrained by the state of their data.

Across global respondents, 39% of leaders identified internal AI as their top business priority for 2026, with digital transformation following closely at 36%¹. However, the same leaders also ranked cybersecurity (53%) and data quality (44%) as their biggest operational concerns².

This signals a tension many firms will recognise, enthusiasm for AI innovation is outpacing the readiness of the underlying data.

Confidence gaps and data reality

When asked about confidence in their organisation’s data, 64% of FS&I leaders admitted they were not fully able to make informed business decisions based on the data they have today³.

The survey highlights three recurring technical issues that explain why:

• 59% struggle with duplicate records
• 52% operate with siloed data across departments
• 55% report low trust in datasets
• 52% have seen AI projects fail due to poor data quality³

These numbers reveal that weak foundations, not weak ambition, are the greatest barrier to transformation. Poor data management creates risk and friction everywhere – in compliance, customer onboarding, reporting, and model performance.

Third-party risk: The invisible cost of poor data

The Pulse Survey found that 91% of FS&I firms have been negatively impacted by third-party risk in the past year⁴. These incidents – including data breaches, financial losses, and reputational harm – cost an average of $706,000 per firm, rising to $1.5 million in Germany.

Despite the scale of these losses, only 26% of firms reported using a unified or cloud-based platform for managing counterparty data, and 44% said they invest in external or third-party data enrichment⁴.

In many cases, organisations are still manually researching suppliers and partners – or even relying on generic AI search tools to assess counterparties – introducing inconsistencies and compliance risks.

The report’s case studies show how better data integration pays immediate dividends. One global bank created a unified third-party view using persistent identifiers and hierarchy mapping, while a leading insurer saved 684 hours annually by embedding data APIs directly into its GRC platform⁵.

Both examples show the commercial payoff of clean, connected data: faster onboarding, fewer review cycles, and better risk visibility.

Cyber, fraud, and compliance: Three risks with one root cause

Cybersecurity (79%) and fraud (78%) rank as the top operational risks for FS&I leaders². Yet 38% of respondents still feel their organisations are not fully prepared to manage these threats².

These risks are increasingly intertwined with data quality. Fragmented telemetry, inconsistent customer identities, and missing lineage all erode resilience. When different systems hold conflicting information about the same customer or supplier, investigations slow down and control logic becomes unreliable.

The regulatory context adds urgency. The UK’s Financial Conduct Authority (FCA) and Prudential Regulation Authority (PRA) continue to stress operational resilience, while the EU’s Digital Operational Resilience Act (DORA) heightens expectations for third-party and ICT risk oversight. Each of these frameworks depends on trustworthy, traceable data.

From aspiration to execution: The data quality roadmap

The Pulse Survey’s findings support a pragmatic four-step approach to becoming “AI-ready”:

1. Discover and deduplicate

Map every customer, supplier and third-party record. Eliminate duplicates and define survivorship rules.

2. Enrich and connect

Link internal data to external identifiers, hierarchies, and sanctions data to close visibility gaps.

3. Automate checks

Integrate these enriched datasets into onboarding and risk workflows with clear explainability and audit trails.

4. Govern and monitor

Assign ownership, define data thresholds, and monitor for drift and quality breaches.

This structured approach reduces rework and provides a tangible bridge between the board’s AI ambitions and what the data can safely support.

What Dajon brings to the table

At Dajon Data Management, we translate these insights into action. Our work focuses on strengthening the data foundations that enable FS&I organisations to accelerate transformation safely and compliantly.

We help clients:

Digitise their records archives

Turning the largest untapped source of business data into structured, searchable, and governed assets. Using intelligent document processing (IDP) and OCR, we extract metadata and link it to live customer, policy, and counterparty systems.

Capture, migrate, and integrate data across legacy platforms

Establishing a trusted golden record accessible to every consuming system.

Embed governance

That ensures data quality, lineage, and security remain intact over time.

For financial services leaders, this means AI models trained on verified data, onboarding teams freed from manual checks, and risk functions equipped with accurate, auditable insights.

The bigger picture: trust as a strategic asset

The Pulse Survey’s ultimate takeaway is that data trust is now a strategic differentiator. Boards increasingly understand that digital transformation doesn’t begin with technology, it begins with confidence in the data that drives it.

The firms that will lead the next wave of financial innovation are not those deploying the most AI tools, but those that have built a complete, compliant and connected view of their business universe.

That journey starts with the records already in your possession: The contracts, KYC files, and historic archives that define how your organisation operates. When digitised and integrated, these archives become the connective tissue of the enterprise, powering everything from risk analysis to AI-driven automation.

1. Dun & Bradstreet, Financial Services & Insurance Pulse Survey 2025, Section 04, p. 17 (internal AI 39%; digital transformation 36%). [↩]
2. Dun & Bradstreet, Financial Services & Insurance Pulse Survey 2025, Section 04, p. 18 (cybersecurity 53%; data quality 44%; preparedness 38%). [↩] [↩] [↩]
3. Dun & Bradstreet, Financial Services & Insurance Pulse Survey 2025, Section 03, pp. 13–14 (64% lack data confidence; duplicates 59%; siloed 52%; low trust 55%; 52% AI project failure). [↩] [↩]
4. Dun & Bradstreet, Financial Services & Insurance Pulse Survey 2025, Section 02, pp. 11–12 (91% impacted by third-party risk; $706k average loss; 26% unified platforms; 44% external data investment). [↩] [↩]
5. Dun & Bradstreet, Financial Services & Insurance Pulse Survey 2025, Case Studies, pp. 20–21 (global bank unified third-party view; insurer saved 684 hours via API integration). [↩]