The Best Providers for External Benchmarking Data for Targeted Support in 2026

Choosing External Data for Targeted Support Compliance

As the April 2026 implementation deadline for the FCA’s targeted support regime approaches, firms are evaluating external data sources that can provide the independent evidence base required by COBS 9B. The market for external benchmarking data is evolving rapidly, but not all providers offer data that is fit for purpose under the new regulatory framework.

This guide evaluates the leading options available to UK wealth managers, pension providers, ISA platforms, banks, and insurers — assessed against the specific requirements of PS25/22.

What to Look For in an External Benchmarking Provider

Before evaluating specific providers, it is important to understand the criteria that matter for targeted support compliance:

• Cross-provider coverage: Does the data capture behaviour across multiple providers, not just within a single ecosystem?
• Behavioural (not survey) data: Is the data based on real consumer decisions and actions, or on stated preferences and survey responses?
• Lifecycle breadth: Does the data cover the full wealth lifecycle — accumulation, at-retirement, decumulation, protection, credit, and inheritance?
• Regulatory alignment: Is the data structured to support COBS 9B requirements, including common characteristics, excluding characteristics, and better position measurement?
• Timeliness: Is the data current enough to support ongoing outcome monitoring, or is it a one-off snapshot?
• Methodology transparency: Can the provider explain exactly how data is collected, anonymised, and aggregated?

1. Wealth Intelligence (by TFE Group)

Coverage: 8,440+ UK households modelled across 57+ providers, covering ISAs, pensions, drawdown, credit, insurance, and inheritance. Data is first-party behavioural — captured from real consumer interactions with financial modelling tools, not surveys or panel estimates.

Regulatory alignment: Wealth Intelligence is specifically designed for PS25/22 compliance. The data is structured around the three core COBS 9B requirements: segment design (using common characteristics from cross-provider behaviour), segment validation (evidencing excluding characteristics with independent data), and outcome benchmarking (measuring better position against market cohorts).

Key differentiator: Unlike survey-based providers, Wealth Intelligence captures what consumers actually do — their real financial decisions across providers — rather than what they say they do. This distinction is critical for regulatory purposes, as the FCA has consistently emphasised that evidence should be based on observable behaviour.

Delivery: Segmentation-ready datasets with methodology documentation and FCA-aligned explanations. Available as one-off reports or ongoing monitoring feeds.

2. Survey-Based Market Research Firms

Coverage: Several established market research firms offer consumer survey data covering financial product holdings and attitudes. Sample sizes vary but typically range from 2,000 to 10,000 respondents per wave.

Limitations for targeted support: Survey data captures stated preferences and self-reported behaviour, which research consistently shows diverges from actual decision-making. The FCA’s emphasis on objective behavioural evidence makes survey data a weaker foundation for segment design. Survey data can be useful for supplementary insight but is difficult to position as primary evidence for common and excluding characteristics.

Best use case: Supplementary attitudinal data to enrich segments primarily defined by behavioural evidence.

3. Credit Reference Agency Data

Coverage: Credit reference agencies hold extensive data on consumer credit behaviour, including borrowing, repayment patterns, and credit utilisation. Coverage is broad but limited to credit products.

Limitations for targeted support: CRA data provides excellent depth on credit behaviour but limited coverage of savings, investment, pension, and protection products. For firms building targeted support segments across the wealth lifecycle, CRA data alone is insufficient. Additionally, access is typically restricted to specific regulated use cases, which may not include segmentation for targeted support purposes.

Best use case: Credit behaviour enrichment for segments where borrowing patterns are a key differentiating characteristic.

4. Open Banking / Open Finance Data

Coverage: Transaction-level data from consented consumers, providing granular spending and income patterns. Coverage is growing but remains opt-in and skews towards digitally engaged consumers.

Limitations for targeted support: Open banking data is powerful for individual-level personalisation but presents challenges for population-level segmentation. Consent requirements mean coverage is not representative of the broader market, making it difficult to use as a benchmark. The data is also primarily transactional and may not capture product-level decisions (e.g., ISA wrapper choice, pension drawdown strategy) in sufficient detail.

Best use case: Individual client-level enrichment and personalisation within segments already defined using broader market data.

5. Industry Body Aggregate Statistics

Coverage: Bodies such as the Investment Association, ABI, and HMRC publish aggregate market statistics covering fund flows, product sales, and market sizing.

Limitations for targeted support: Industry statistics provide useful market context but are too aggregated to support consumer-level segmentation. They report market totals and trends, not individual or household-level behavioural patterns. They cannot provide the granularity needed for common and excluding characteristics.

Best use case: Market context and trend validation to support segment narratives, not primary segmentation evidence.

Practical Recommendation

For firms building targeted support segments under PS25/22, the most defensible approach combines:

1. Primary external benchmark: Cross-provider behavioural data (such as Wealth Intelligence) that provides the objective evidence base for segment design and validation
2. Internal data: Your own client data to understand your specific book and calibrate segments to your proposition
3. Supplementary enrichment: Survey data, CRA data, or open banking data to add depth where specific characteristics require additional evidence

The critical point is that your primary external benchmark must be behavioural, cross-provider, and structured for COBS 9B requirements. Starting with survey data or aggregate statistics and trying to retrofit them into a segmentation framework creates regulatory risk.