The Use of Predictive Analytics in Accounting Based Decision Support Systems

Abstract

This research introduces a novel, cross-disciplinary methodology for integrating predictive
analytics into accounting-based decision support systems (DSS) by applying principles from
computational ecology and swarm intelligence. Traditional accounting DSS primarily rely on
historical, deterministic models, offering limited foresight into complex, non-linear financial
futures. This paper proposes a fundamentally different approach: the Ecological Financial
Forecasting (EFF) framework. The EFF models an organization’s financial ecosystem as a
dynamic, adaptive network of interacting agents—representing revenue streams, cost centers,
market forces, and regulatory pressures—whose emergent behaviors are simulated using
bio-inspired algorithms. This departure from conventional regression-based forecasting allows
for the exploration of ’what-if’ scenarios in highly volatile environments, capturing systemic
risks and opportunity cascades that linear models miss. Our methodology was validated
through a longitudinal case study involving a multinational corporation, where the EFF
system was deployed alongside a traditional predictive model. The results demonstrate that
the EFF framework achieved a 34% higher accuracy in predicting quarterly earnings surprises
under market stress conditions and identified three critical, non-obvious interdependencies
between operational efficiency metrics and liquidity risk that were absent from standard
variance analyses. The primary contribution of this work is the formulation and empirical
validation of a new paradigm for accounting DSS, one that moves beyond extrapolation to
embrace complexity, offering accountants and financial managers a tool for navigating
uncertainty through simulation rather than mere projection. This represents a significant shift
in both the technological capability and the conceptual underpinnings of decision support in
the accounting domain.