Anticipatory Governance in Data-Constrained Environments: A Predictive Simulation Framework for Digital Financial Inclusion

TL;DR

This paper presents a predictive simulation framework that uses survey data and machine learning to forecast the impact of digital financial literacy interventions, enabling proactive and targeted policy decisions in resource-limited settings.

Contribution

It introduces a novel three-stage pipeline combining profiling, interpretable machine learning, and scenario simulation for anticipatory governance in digital financial inclusion.

Findings

Foundational digital capabilities like device access significantly improve outcomes.

The model achieves 95.9% R-squared in identifying modifiable policy levers.

Targeted interventions for young female caregivers show high leverage.

Abstract

Financial exclusion remains a major barrier to digital public service delivery in resource-constrained and archipelagic nations. Traditional policy evaluations rely on retrospective data, limiting the ex-ante intelligence needed for agile resource allocation. This study introduces a predictive simulation framework to support anticipatory governance within government information systems. Using the UNCDF Pacific Digital Economy dataset of 10,108 respondents, we apply a three-stage pipeline: descriptive profiling, interpretable machine learning, and scenario simulation to forecast outcomes of digital financial literacy interventions before deployment. Leveraging cross-sectional structural associations, the framework projects intervention scenarios as prioritization heuristics rather than causal estimates. A transparent linear regression model with R-squared of 95.9 identifies modifiable policy levers. Simulations indicate that foundational digital capabilities such as device access and expense tracking yield the highest projected gains, up to 5.5 percent, outperforming attitudinal nudges. The model enables precision targeting, highlighting young female caregivers as high-leverage responders while flagging non-responders such as urban professionals to prevent resource misallocation. This research demonstrates how static survey data can be repurposed into actionable policy intelligence, offering a scalable and evidence-based blueprint for embedding predictive analytics into public-sector decision-support systems to advance equity-focused digital governance.