A Machine Learning-Based Predictive Risk Management Framework for Technology-Oriented Small and Medium Enterprises

Small and medium enterprises (SMEs) in technology
sectors face disproportionate exposure to cash-flow volatility,
supply chain disruption, and customer attrition, yet typically
lack access to the enterprise-grade risk analytics used by large
corporations. This paper proposes a lightweight, ensemble
based Predictive Risk Management (PRM) framework designed
for
the
fragmented, low-volume data environments
characteristic of tech-oriented SMEs. The architecture
combines a Random Forest classifier with a Gradient Boosted
Decision Tree (GBDT) model to synthesize financial, supply
chain, and customer-behavior indicators into a single Risk
Resilience Index (RRI) intended to support early, prescriptive
decision-making. A SHAP-based interpretability layer maps
each alert back to the operational indicators that produced it,
and a three-tier alerting scheme (low, medium, high) is
proposed to translate the index into an actionable operating
rhythm. Unlike prior enterprise risk platforms, the proposed
design emphasizes interpretability and low computational
overhead so that it can be deployed by organizations without
dedicated data science staff. This paper presents the conceptual
architecture, feature engineering strategy, and evaluation
methodology in detail; it is offered as a design and research
proposal rather than a report of completed empirical results,
and it outlines the validation study, including experimental
design and threats to validity, required before the framework's
performance claims could be substantiated.