Artificial Intelligence for Integrating Environmental Risks into Financial Forecasts

Abstract

This paper introduces a novel methodological framework that integrates granular, dynamic environmental risk data into traditional financial forecasting models
using a hybrid artificial intelligence architecture. The research addresses a critical
gap in financial analysis, where environmental factors are often treated as static,
exogenous variables or are omitted entirely due to data complexity and temporal misalignment with financial cycles. Our approach diverges fundamentally from
prior work by conceptualizing environmental risk not as a set of discrete shocks but
as a continuous, multi-scale process that interacts with financial systems through
complex, non-linear pathways. We propose a two-tiered neural-symbolic AI system.
The first tier employs a modified Transformer architecture, trained on multi-modal
data streams including satellite imagery, sensor networks, and socio-economic indicators, to generate probabilistic forecasts of environmental stress at asset-specific
geolocations. The second tier consists of a symbolic reasoning layer that maps these
environmental forecasts onto financial statement line items and cash flow drivers
using a domain-specific ontology derived from fundamental analysis and corporate
disclosure principles. This mapping produces a dynamic ’environmental beta’ coefficient that modulates traditional financial growth rates and discount factors within
a modified discounted cash flow (DCF) model. We validate the framework using
a unique longitudinal dataset linking corporate financials to hyper-local environmental data for 500 global firms across extractive, agricultural, and manufacturing
sectors from 1995 to 2004. Results demonstrate that forecasts incorporating our
AI-derived environmental integration show a 22