AI use cases for sustainable finance
This page lists a selection of AI applications to support sustainable finance, along a "data-to-decision" workflow with five stages: collecting data, ensuring its quality, extracting insights, anticipating future outcomes, and drafting content to communicate results.
The page will be regularly enriched with new cases.
Data collection and extraction
Simplifying Double Materiality Assessment via IPA
The double materiality assessment a mostly qualitative procedure meant to assess how a company affects ESG components and, in turn, how the ESG components can affect said company. Performing a DMA requires to first determine a series of impacts, risks and opportunities and then go through any available information source and perform an assessment of factors such as Severity or Likelihood of each. As the list of IROs can be very large, the process can be very time consuming for the teams performing it. This use case explores how intelligent process automation can support accelerating the process.
Real-Time Market Intelligence for Swiss Real Estate
Real estate is the largest asset class operating without market-wide transparency. In Switzerland, data on buildings and investment vehicles is scattered across annual reports, PDFs, and proprietary databases. This opacity distorts prices, leaves climate exposure unpriced, and directs capital without visibility into what it funds, following the well-know principle: what does not get measured cannot be managed. QuanthomeAI strives to address this issue by connecting users to a unified database through natural language queries
Pilot use case to extract insights from stewarship reports
In the past few years, Stewardship has become one of the main sustainable investment approaches applied by financial services companies in Switzerland. A significant portion of Asset Managers (more than 40%) and a great majority of Asset Owners (83%) do not perform stewardship by themselves, but rely on a third-party provider. This means that a large number of Asset Managers and Asset Owners rely on third party reporting to assess the effectiveness of these activities. If they have several providers, they need to screen several such reports in different formats - the workload increases accordingly, which, especially for some Asset Owners with limited resources, can become an issue.
Data curation and quality assurance
Building trustworthy data foundation via machine-learning
For most portfolio managers and asset owners, data reliability and quality remain a central challenge in sustainable investing. Human error in capturing reported data, combined with corporate disclosures that are often incomplete, inconsistent, or difficult to compare across peers and jurisdictions, undermines data confidence. In addition, differing reporting boundaries and definitions further complicate consistency. These issues reinforce the well-known “garbage in, garbage out” problem: unreliable inputs lead to unreliable outputs, limiting investors’ ability to make sound decisions, manage risks effectively, or meet regulatory and client expectations.
Data analysis and insights
Sentiment Analysis on corporate sustainability reports
Corporate sustainability reporting is a critical tool for evaluating climate and environmental performance. Yet, in practice, its value is undermined by complexity, inconsistency and the absence of verifiable standards. To overcome these challenges, the Department of Finance at the University of Zurich has developed two AI-driven tools: ASKCLIMATE and ASKNATURE. These tools are designed to provide free access to high-quality sustainability analysis and bring greater structure and traceability to corporate sustainability disclosures. They can be used by the public or by professionals, e.g. investment managers.
AI-Driven Network Analysis to assess Carbon Markets integrity
Carbon markets are systems that enable the trading of carbon emission allowances or credits, assigning a monetary value to greenhouse gas emissions in order to drive climate action. However, the credibility of these entities is increasingly undermined by the presence of "hot air" credits, which have the potential to distort price signals, mislead investors and ultimately risk slowing global climate progress. To address this, ZHAW is developing an AI-based network analysis tool designed to uncover inefficiencies, detect patterns of risk, and enhance market accountability, with the aim of aligning financial flows with meaningful climate action.
Human Guided AI for Scaled and Trusted Risk Insights
Banks, asset managers, and corporates face growing complexity in identifying and managing business conduct risks arising from biodiversity, climate, human rights, and corruption across their business relationships, portfolios, and supply chains. Risk assessments based solely on self-reported disclosures often obscure material issues and delay timely intervention. To address the challenges stated above, RepRisk employs a hybrid approach that combines advanced AI models with human intelligence.
Satellite image and computer vision to assess biodiversity value-at-risk
The impact of economic activity on the environment, also called “nature risks”, is becoming a serious concern that can have far-reaching and lasting consequences. These consequences include threats to global health, food and water security, aggravation of poverty and migration, and even geopolitical destabilisation. However, sustainable finance faces a core obstacle: the lack of reliable, science-based data on the environmental impacts of corporate activities. ZHAW is working to address this issue by bridging the gap between measurements of environmental impacts and the resulting financial costs
Predictive analytics
AI for ESG Integration: Training Machines to Predict Sustainable Alpha
Investment managers face the challenge of integrating ESG factors with traditional ones like value, growth, and momentum. Mastering this integration is crucial for optimizing stock selection and portfolio construction. RAM-AI has developed a deep learning framework to model the complex interactions among different input features, and to integrate ESG and traditional factors for predicting stock returns.
AI-based Analysis for Real Estate Portfolio Decarbonisation
For institutional investors, assessing the sustainability of real estate portfolios remains a significant challenge. A key objective of the initiative by Conser and ZHAW is therefore to examine whether the CO₂ reduction commitments and net-zero targets of real estate funds are realistic given the current condition of their building stock, and whether the required renovations are both achievable and aligned with reported measures.
Content generation
AI supporting investment managers in decision making
The 17 Sustainable Development Goals (SDGs) were established in 2015 by the United Nations. They can be broken down into 169 actionable targets, with 232 indicators used to measure the progress toward each goal. Investors are however confronted with a lack of a standardized reporting framework and the absence of common reference data across. This drove Pictet to develop a systematic and independent tool to assess the SDG alignment of the companies composing their discretionary management portfolios, and to identify SDG improvers, i.e. companies that are on a transition journey.
AI-powered investment due diligence
Portfolio Managers and Investment Analysts are under pressure to quickly assess the sustainability performance of a potential portfolio company ahead of an investment decision or an upcoming investment committee meeting, for example. The challenge is not just the volume of data but its complexity, and the need to translate it into actionable insights under time pressure. ClarityAI has developed AI-powered tools to help investment management teams in this process, with a focus on providing efficiency gains in data aggregation, curation, and preliminary assessment.
Any suggestion, comment or question? Reach out to Romain Leroy-Castillo, Director & Artificial Intelligence lead at SSF
