A Multidisciplinary Framework for AI and Data-Driven Transformation in Taxation, Insurance, Mortgage Financing, and Financial Advisory: Integrating Cloud Computing, Deep Learning, and Agentic AI for Community-Centric Economic Development

Abstract

Taxation, insurance, mortgage financing, and financial advisory are examples of fields facing an increased demand for data-driven transformations. These transformations comprise digitisation, automation, and analytics. Artificial Intelligence (AI) has various applications in all these fields. It is presented as a structured problem for mapping both the fields and corresponding AI techniques together. Of the many techniques popular in AI research, a handful are systematically mapped against a proposed framework. The framework naturally leads to a heat-map view of technique popularity, showing relative dominance in each of the four different fields.
AI was once thought to have little relevance to practice, but has in fact been mainstream for some years. Tax debts have been audited with experimental models, savings have been extracted with various optimisation techniques, emotion detection has been used for financial risk, and AI is widely used against fraud in practice. More recently already well-established AI fields have shown promise in their first forays into the domain. For example, active learning methods have shown good early results in bankruptcy prediction. These efficacy studies also promise great reward for companies who successfully adopt the technologies. Privacy-preserving data mining confidentiality preserving approach has cut the necessary feature pool to a third of the normal pool with only a 15% cost on the true positive rate. The lower cost is especially significant as the false positive rate is up to five times lower.