2026 may initiate a cyber threat landscape less defined by the dramatic, ‘growing’ as a transformative force . . . ~

Artificial intelligence (AI) has become a transformative force across critical sectors such as manufacturing, healthcare, and finance, largely due to its ability to deliver highly [seemingly] accurate predictions from complex data. In finance, advanced machine learning (ML) models have demonstrated strong performance in areas such as credit scoring, portfolio optimisation, and investment decision-making. However, these gains in accuracy come with a significant limitation: most high-performing ML models operate as “black boxes,” offering little transparency into how decisions are made. This lack of interpretability is particularly problematic in financial contexts, where explainability is essential for trust, governance, and regulatory compliance.

The emergence of eXplainable Artificial Intelligence (XAI) addresses this challenge by bridging the gap between accuracy and interpretability. Model-agnostic techniques, especially those based on Shapley values, allow practitioners to explain individual predictions made by complex ML models without [seemingly] sacrificing performance. This is especially valuable in investment analysis, where understanding and demonstrating the drivers of both risk and return is as important as the prediction itself.

Building on this paradigm, recent research applies XAI to investment decisions in small and medium enterprises (SMEs), a domain characterised by limited and imperfect information. Using a five-step framework, the approach first estimates SMEs’ probability of default (PD) through a highly accurate XGBoost model, then employs Shapley values to identify the most influential risk factors. After filtering out default-prone datasets and datapoints, the model estimates expected returns for non-defaulted SMEs and again uses Shapley values to explain the key drivers of profitability. This dual-focus on explainable risk and return represents one of the first comprehensive XAI-based investment frameworks for SMEs, extending traditional credit scoring concepts to a broader investment decision-making context; and also provides a wider spinoff into non-financial applications, including heavily regulated fields.

At the same time, developments in the regulatory landscape - particularly evident in 2025 - underscore why explainability and data coherence matter more than ever. Regulators have shifted toward firm-level, cross-asset scrutiny, emphasising consistency, accuracy, and alignment of data across systems and business-lines. New and enhanced rules have exposed the limitations of manual reviews and siloed workflows, accelerating the adoption of centralised validation frameworks, intelligent automation, and auditable data infrastructures. There have been many front-page breaches in finance, manufacturing, government and retail sectors during 2025.

Taken together, these trends point to a converging reality: high-performing AI models must be explainable, auditable, and integrated within scalable data architectures. XAI techniques not only enhance trust in AI-driven investment decisions - particularly for SMEs, but across all sectors - but also align naturally with the growing regulatory demand for transparency, consistency, and accountability across the ecosystem.