Description du livre
This book examines the structural asymmetries embedded in contemporary consumer credit systems. Trust—essential to economic exchange—is increasingly withheld from borrowers while demanded of them. Lenders operate with minimal visibility or accountability; borrowers must navigate opaque infrastructures, absorb unilateral judgments, and bear the consequences of exclusion.
Over the past two decades, credit markets have expanded through algorithmic risk assessment and automated decision-making. Yet the cost of evaluating ‘creditworthiness’—and the entrenchment of risk-based pricing—has deepened exclusion for those experiencing financial difficulty. Surveillance-based scoring systems discipline borrower behaviour, obscure hardship, and produce reputational harm that reverberates across households and public institutions.
At the centre of the book is the Trust Intelligent framework: a seven-domain model for diagnosing and redesigning trust–power configurations in credit relationships. It operationalises trust to reduce systemic risk, improve decision accuracy, and restore borrower agency. Grounded in empirical evidence, the framework supports practical reforms—integrating contextual data into credit files, rendering lender conduct visible, and establishing multistakeholder oversight of algorithmic systems.
Rather than accepting the trajectory of deeper data extraction and one-sided surveillance, the book proposes a reciprocal model of information exchange—one that improves outcomes and embeds mutual accountability. It offers a blueprint for reform that is both conceptually rigorous and institutionally actionable.
This is a book for those who design, regulate, and critique credit systems: lenders, credit reference agencies, financial regulators, consumer advocates, and scholars of trust, governance, and institutional design. Drawing on economics, sociology, political science, and organisational studies, it presents a multidisciplinary account of how trust can be rebuilt—within and beyond consumer credit.