ESG formally already exists in banks: there are questionnaires, metrics, reports, and often external data providers as well. The problem begins when this data needs to support real action—preparing regulatory reports, calculating portfolio-level metrics, supporting the credit process, or answering a simple question: where exactly did a given value come from, and who validated it?
This is when it becomes clear that, in many organizations, ESG operates more as a collection of scattered files, questionnaires, and data sources than as a coherent data system. Information is collected manually, passes through several teams, and often has no single owner. The data exists, but it is difficult to fully trust it.
This is the main challenge banks face today: not a lack of ESG, but a lack of consistent, controlled, and auditable ESG data.
What ESG Looks Like in Many Banks Today
In practice, the picture is usually similar. Some data is stored in Excel spreadsheets, some in questionnaires completed by clients or business units, some comes from external vendors, and some is kept locally by individual teams. On top of this, there are internal interpretations, manual mappings, and supporting files designed to “stitch” everything together for reporting purposes.
This model develops gradually. First comes a new reporting obligation, then the need to collect data for taxonomy purposes, followed by another area, a new metric, or a new provider. Each requirement is addressed locally and quickly, but over time the bank ends up operating across multiple parallel data sources that do not form a coherent whole. The greater the scale, the more apparent it becomes that the bank is not working with a single ESG data model, but with a collection of temporary solutions.
The Diagnosis: Lack of Trust in Data, Not Lack of Data
Excel itself is not the problem. The problem arises when it becomes the primary environment for managing ESG data across the organization. In such a setup, it becomes difficult to answer basic questions: Which value is current? Where did it come from? Who entered it? Has it been verified? Is the same value being used in another report? Is the client’s taxonomy classification consistent across systems?
If answering these questions requires searching through multiple files, email correspondence, and manual reconciliations between teams, the bank does not yet have a stable ESG data environment. The problem, therefore, is not the lack of data but the lack of trust in it. Data is fragmented, inconsistent, sometimes duplicated, and processed according to different rules by different units. As a result, every new analysis begins with determining which data is actually correct.
The Problem and the Risk: No Single Source of Truth
For ESG Offices and IT/Data teams, this is a critical issue. When the same information exists in parallel across several locations, discrepancies quickly emerge. One report shows a different value from another, one team applies a different classification than another, external vendor data is not consistently mapped to internal data, and the profile of the same client can look different every time the data is used.
In practice, this means there is no single source of truth. Without it, ESG cannot function as a banking process—it becomes a series of manual operations. It is difficult to build reliable reporting, feed ESG data into scoring models, use it to support credit decisions, or develop new green products.
This also leads to a loss of control, not just inefficiency. When ESG data is collected manually and processed across fragmented spreadsheets, the risk of errors and inconsistencies increases, while reconstructing the complete data trail becomes increasingly difficult. This creates not only operational problems but audit risks as well.
The message here is simple: ESG in Excel will not stand up to audit scrutiny. A spreadsheet is not inherently a bad tool, but it was not designed to serve as the foundation of a banking data process that must be repeatable, controlled, and resilient as requirements continue to grow.
The Solution: A Client and Portfolio ESG Data Repository
Banks do not need yet another “ESG system” understood as a standalone tool disconnected from the rest of their architecture. What they need is an ESG data layer that structures the entire process: a single environment where data is collected, integrated, standardized, classified, and made available for reporting, models, analytics, and operational processes.
Such a layer does not replace the bank’s existing systems. Instead, it organizes the flow of data between them. It combines internal and external data, eliminates repeated manual processing of the same information, and creates a common model that can be used across different areas of the organization. This is where true ESG maturity begins—not with another report, but with an environment in which data has one meaning, one source, and one controlled lifecycle.
What Should Such a Solution Include?
The foundation is data integration from multiple sources: internal systems, client data, external databases, and ESG data providers, together with standardization and mapping to a common data model.
The second element is automated classification: supporting taxonomy logic, mapping data to the required categories, and reducing manual work in areas where banks currently rely on interpretations scattered across different teams. The greater the level of automation, the lower the risk of errors and the greater the repeatability of the process.
The third pillar is a client ESG repository: a single place where the bank stores a complete set of ESG information linked to the client, product, and portfolio. This gives ESG, IT, and business teams a shared foundation for further activities. Instead of starting from scratch every time, the bank has structured, accessible data that is ready to be used across subsequent processes.
ESG Starts with Data
Many banks have ESG initiatives in place today, but not every bank has ESG data it can truly trust. The challenge is no longer simply to collect information. It is to build a single, consistent, and auditable data environment that enables ESG to be used in a repeatable and operational way. Only then can banks report effectively, analyze reliably, feed data into models, and integrate it into credit processes.
Moving from Excel to a system is not a technical project to be postponed until later. It is the foundation of the bank’s entire approach to ESG. ESG does not start with a report. It starts with data.
We are a team of specialists working primarily on projects for the financial sector. On our blog, we share insights from real-world projects: we discuss technologies, analyze implementation approaches, and highlight what works in practice. We create content that helps better understand IT and make informed decisions — both from a business perspective and within technology teams.
