Machine Learning Tools for Evaluating Sustainability Linked Financial Instruments

Abstract

This paper introduces a novel methodological framework for evaluating SustainabilityLinked Financial Instruments (SLFIs) using machine learning techniques, addressing a critical gap in the intersection of computational finance and environmental, social, and governance (ESG) analytics. Traditional evaluation methods for SLFIs, such as green bonds or
sustainability-linked loans, rely heavily on static ESG scores and manual due diligence, which
are often backward-looking, inconsistent across providers, and inadequate for capturing the
dynamic, multi-faceted nature of sustainability performance and its financial materiality.
Our research proposes a departure from these conventional approaches by developing and
validating a hybrid machine learning architecture that synergistically combines interpretable
tree-based models for feature importance analysis with temporal graph neural networks to
model the complex, time-evolving interdependencies between a firm’s operational data, ESG
metrics, and the specific key performance indicators (KPIs) tied to the financial instrument.
We formulate the evaluation not as a simple classification or regression problem, but as a
dynamic, multi-objective optimization of credibility, ambition, and financial risk. Using a
unique, hand-collected global dataset of 450 SLFI issuances from 2000 to 2004, we train our
models to predict the likelihood of KPI achievement, the potential magnitude of coupon
adjustments, and the instrument’s overall impact integrity—a novel metric we define. Our
results demonstrate that the proposed framework significantly outperforms baseline models
using traditional ESG scores, achieving a 22% higher accuracy in predicting KPI breaches
and providing superior explanatory power through learned relational graphs of sustainability factors. The findings offer a new, data-driven paradigm for investors and regulators,
enhancing transparency, reducing greenwashing risks, and promoting capital allocation to
genuinely impactful sustainability projects. This work represents a foundational step towards algorithmic, real-time accountability in the sustainable finance market.