This chapter analyses the modern credit data ecosystem, arguing that Big Data, artificial intelligence, and machine learning amplify the structural imbalances at the heart of consumer credit relationships. It details how lenders construct comprehensive digital profiles from granular datasets—including alternative data like rent and utility payments—to enhance profitability, raising critical challenges of opacity, bias, and coerced consent. The chapter critiques how tools promoted for financial inclusion can also be used for exploitation, masking financial hardship and enabling predatory lending. The analysis identifies three core dysfunctions of the current architecture: scoring systems that misrepresent people by optimising for profit rather than context; a one-sided demand for trust in which borrower conduct is intensely monitored while lender behaviour remains invisible; and the individualisation of hardship, which ignores systemic risks. In response, the chapter introduces ‘trust intelligence’ as a participatory, relational governance framework. This approach is built on three foundational shifts: from surveillance to engagement; from profit optimisation to responsiveness; and from unilateral authority to shared governance. Proposals include integrating life-event disclosures into lending decisions, making lender responses to forbearance requests visible on credit files, and establishing independent governance bodies with consumer representation to ensure fairness and mutual accountability.