Environmental, Social, and Governance (ESG) has become an increasingly important area within the wealth management sector. However, ESG data consistency is still far from perfect.
The ESG sector had a bumpy 2025, with changing priorities around the world made it look like interest in the space was going to shrink. Whether it was governments moving away from ESG-focused goals or a reduction in the scope of related regulations, it was a year defined by change. Yet the sector is still a major priority for many within wealth management.
In the 2025 edition of the Morgan Stanley Institute for Sustainable Investing, it found that 79% of asset managers and 86% of asset owners expect the proportion of sustainable assets under management (AUM) in their portfolios to rise over the next two years. The reasons for this pointed towards a realisation the market is maturing and has significant opportunities.
The retail investing side also has a significant appetite for ESG. Another report from Morgan Stanley in 2025 claimed that 88% of investors globally that it surveyed, showed interest in sustainable investing, including 99% of Gen Z and 97% of Millennial investors. It asked 1,765 active investors with over $100,000 in investable assets. Of these, 59% said they intend to increase their portfolio allocations to sustainable investments in the next year.
Despite the significant interest in the market, many firms still struggle when it comes to collecting and leveraging ESG data. This is largely a result of a lack of consistency in the data. Many firms struggle with isolated systems and a lack of standardisation that makes utilising the data difficult.
According to Fredrik Davéus, CEO and co-founder of Kidbrooke, the main cause of this comes down to interpretation. He said, “ESG metrics are built on a mix of disclosures, estimates, and subjective assessments, often using different definitions, weighting models, and methodologies across providers. Unlike financial data, there is no universally accepted accounting framework for ESG, which means the same company can receive materially different scores depending on the data source.”
This is then compounded by uneven corporate disclosure standards and differing regulations by region. Ultimately, this leaves firms comparing signals that seem similar on the surface but are measuring different things.
In the same vein, Geert Bernaerts, finance manager at everyoneINVESTED, also pointed to data inconsistency being a casualty of a lack of standardisation. He said, “For years, ESG data has primarily meant one thing: labelling assets. Yet that universe is riddled with inconsistency. Data providers often disagree, methodologies diverge, and all of them tend to lag behind regulations that evolve almost yearly. In that chaos, one question remains strangely overlooked: what are the clients’ actual ESG preferences? Before institutions debate ratings, frameworks, or disclosures, they must first understand the sustainability expectations of the person whose capital is being managed.”
He continued by noting that conversations around the ESG data problem end at the wrong conclusion – a technical problem. The real bottleneck occurs before the data even enters the system. Clients interpret the questions differently, advisors struggle to ask them consistently, and teams across countries use their own variants of the same questionnaires. “The result? A river of ESG inputs that never align, because they were never captured the same way.”
The solution to this is standardising how ESG data is collected at the interaction between financial institutions and the customer.
Data harmonisation
Improving the consistency and quality of ESG data empowers firms to improve their decision making and improve the customer experience. One way firms are looking to improve their data harmonisation is through the support of WealthTech companies.
Keeping in the theme of improving the data collection process, Bernaerts sees WealthTech solutions helping firms with improved ESG questionnaires. Pointing to everyoneINVESTED’s own Questionnaire-as-a-Service solution, rather than treating them as static PDFs or homemade forms, they are designed as a data collection architecture. This, according to Bernaerts, achieves three things.
The first of these is through consistency, by ensuring every client has the same structured questions, aligned with regulatory definitions and industry standards. Secondly, it increases completeness by leveraging smart logic and dynamic questions to avoid missed fields, ambiguous responses and advisor-dependent variations. Finally, it helps transform raw answers into machine-readable data, removing text blobs and free-format notes to make the output immediately usable for investment profiling, reporting, and product alignment.
“It is not about asking more ESG questions, it’s about asking them in a way that produces decision ready data.”
When having them in a standardised format, investment suitability assessments become evidence-based, portfolio construction reflects real investor sustainability preferences, product governance gains traceability, and AI models operate on cleaner, structured datasets. “The insight is simple: you cannot fix ESG data inconsistencies by cleaning the data later. You fix them by capturing the data better from the beginning.”
Davéus, on the other hand, sees one of the biggest ways WealthTechs are helping to improve data harmonisation is by extending beyond simple data aggregators.
He said, “FinTechs are increasingly acting as interpreters rather than pure aggregators of ESG data. They are mapping multiple data sources into common taxonomies, normalising metrics at a granular level, and making assumptions explicit rather than hiding them behind composite scores. However, trade-offs remain unavoidable.
“Highly accurate data often relies on deep, manual analysis and limited coverage, while scalable solutions tend to lean more heavily on estimations and proxies. The most credible platforms acknowledge these limitations openly, allowing users to adjust methodologies, confidence thresholds, and weightings instead of forcing a single, opaque view of ESG quality.”
Benefits of structured ESG data
AI has dominated discussions of innovation for the past few years. Many have looked eagerly to the technology as a revolution but not fully understood the areas it can have the most impact. The prevailing notion of the technology is that it is perfect as an assistant to the wealth manager, rather than their replacement. As a case and point, the technology helps advisors utilise their structured ESG data.
Davéus said, “AI and machine learning are increasingly valuable in extracting signals from unstructured data such as sustainability reports, news flows, and regulatory filings, helping to improve coverage and timeliness. Structured data models then provide the framework needed to connect these signals consistently across issuers and portfolios.
“However, these tools also carry risks. Models trained on inconsistent or biased inputs can reinforce existing distortions, and automated scoring systems can create a false sense of precision. The key is governance: AI should be used to surface patterns and anomalies, not to replace human oversight or critical interpretation. When combined with transparent data models and clear assumptions, AI becomes a powerful tool for reducing noise but without that discipline, it risks amplifying it.”
When firms are able to improve their ESG data consistency, they will be able to improve the output of their advisors by reacting more accurately to market conditions and client’s risk profile.
One area that clearly benefits from better structured data is climate transition risk analysis. Davéus noted that when advisors have access to structured data that separates forward-looking transition indicators from backward-looking disclosure metrics, teams can get a different view of risk, allowing them to take advantage of opportunities that would have gone unnoticed.
“In practice, this has led firms to reduce exposure to companies showing limited investment in transition strategies despite regulatory and market pressure. Without structured, comparable ESG data, these nuances are often missed, resulting in portfolios that appear ESG-aligned on paper but are actually exposed to long-term transition risk in reality.”
Looking to the future, Bernaerts offered an insight into how the coming years will be shaped by a change in approach to ESG data.
He said, “As the industry continues to refine ESG scoring, disclosure, and reporting frameworks, one truth remains overlooked: ESG decisions are only as strong as the data that feeds them. The next generation of ESG innovation will not just enhance analytics, it will reshape the moment of data creation.
“Standardised, structured ESG questionnaires are becoming the quiet foundation of reliable sustainability analytics. And as more institutions adopt upstream data quality solutions, the industry will finally shift from fragmented ESG signals to strategic ESG insights.”
