Financial Services & Regulation
·Singapore·12 monthsAI Sustainability Reporting for a National Financial Regulator
Built an AI-driven sustainability reporting framework for a multi-stakeholder consortium led by the Monetary Authority of Singapore (MAS), involving a sovereign wealth fund and Tier 1 international banks. The system automates the aggregation, analysis, and generation of regulatory sustainability reports that previously required months of manual effort across participating institutions.
5+
Institutions in consortium
Weeks to hours
Reporting cycle reduction
Published
Government whitepaper
Challenge
Sustainability reporting in financial services had reached a breaking point. The Monetary Authority of Singapore required participating institutions — including sovereign wealth funds and international banks like Standard Chartered, SMBC, and OCBC — to produce detailed sustainability disclosures aligned with evolving frameworks such as TCFD, ISSB, and Singapore's own Green Finance taxonomy. Each institution maintained sustainability data in different formats, across different systems, with different reporting cadences. A single reporting cycle could consume weeks of analyst time per institution, and the resulting reports still contained inconsistencies that required multiple rounds of manual reconciliation. The challenge was compounded by the multi-stakeholder nature of the initiative. Data had to be aggregated not just within each institution but across the consortium, requiring standardised definitions, consistent taxonomies, and a shared analytical framework. The sensitivity of the data — spanning Scope 1, 2, and 3 emissions, financed emissions, transition risk exposures, and physical risk assessments — meant that any AI system had to produce outputs that were auditable, explainable, and compliant with regulatory expectations. Beyond the technical complexity, there was a political dimension. Each participating institution had different levels of data maturity, different internal reporting processes, and different appetites for automation. The solution needed to accommodate institutions that were advanced in their sustainability data infrastructure alongside those that were still consolidating basic emissions data from spreadsheets.
Approach
Reluvate designed and built Project NovA as a modular AI reporting platform that could ingest sustainability data from heterogeneous sources — structured databases, Excel files, PDF reports, and API feeds from ESG data providers. The ingestion layer normalised data against a unified taxonomy aligned with MAS guidelines, TCFD recommendations, and ISSB standards. Natural language processing modules extracted relevant disclosures from unstructured annual reports and sustainability statements, mapping them to the consortium's shared framework. The analytical engine applied rule-based validation checks alongside ML-driven anomaly detection to flag data quality issues before they propagated into aggregate reports. For each institution, the system generated draft sustainability reports with AI-authored commentary that explained trends, highlighted year-over-year changes, and contextualised performance against sector benchmarks. The commentary was designed to be regulator-ready — written in the formal, precise language expected by MAS, not the casual tone typical of AI-generated text. Reluvate worked directly with MAS officials and representatives from each participating institution through a structured co-development process. Weekly review sessions ensured that the outputs met regulatory expectations. The final platform was delivered alongside a published whitepaper documenting the methodology, which has since been adopted as a reference framework by participating institutions for their ongoing sustainability reporting.
Design Notes
The core design challenge was accommodating vastly different data maturity levels across participating institutions. Rather than enforcing a single data submission format, we built an adaptive ingestion layer that could handle everything from well-structured API feeds to manually maintained Excel sheets. The system inferred data schemas on ingestion and mapped them to the canonical taxonomy, flagging ambiguities for human review rather than making assumptions. Change management was critical given the multi-institutional context. We implemented a staged rollout where each institution first ran the AI system in parallel with their existing manual process, comparing outputs side-by-side for two reporting cycles before transitioning. This built confidence in the AI outputs and allowed each institution's compliance team to calibrate their review thresholds. Exception handling was designed around the principle that no AI output should reach a regulator without a human having the opportunity to review it. The system generates confidence scores for every data point and every paragraph of commentary. Low-confidence outputs are routed to designated reviewers at each institution, with full audit trails showing the source data, transformation steps, and reasoning chain that produced the output. This was non-negotiable for MAS and became a design pattern we now apply across all our regulatory work.
Result
The consortium successfully adopted the AI reporting framework, reducing sustainability reporting cycles from weeks of manual effort to hours of AI-assisted generation with human review. A whitepaper documenting the methodology was published and adopted by participating institutions. The framework established a replicable model for multi-institutional AI-driven regulatory reporting that MAS has referenced in subsequent sustainability finance initiatives.
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