Digital Transformation Solutions has published two companion works on building AI-augmented supervisory capacity for consumer and investor protection: a research report and an implementation manual, both co-authored by Simone di Castri and Matt Grasser under the Cambridge SupTech Lab and co-published with the Cambridge Centre for Alternative Finance (CCAF) at Cambridge Judge Business School, University of Cambridge.
The report, "SupTech-powered consumer and investor protection supervision: Architectural pathway from reactive oversight to outcome-driven institutional intelligence," draws on four years of longitudinal data covering 350 public authorities across 105 countries to diagnose the gap between suptech adoption and architectural integration. While 51% of authorities surveyed report active suptech applications in consumer and investor protection, only 37% of those with a formal mandate have deployed any application to a specific use case — and the report argues the constraint is not technology but institutional architecture. Most supervisory teams do not control their institution's data infrastructure; they inherit it. The report maps a pathway from isolated components — chatbots, dashboards, portals — to the integrated data pipelines that turn complaint and conduct signals into supervisory action, with a foreword by Magda Bianco, Director General for Consumer Protection and Financial Education at Banca d'Italia and Co-Chair of the G20 Global Partnership for Financial Inclusion.
The companion manual, "The SupTech Blueprint: DataStack architecture for consumer and investor protection supervision," translates the report's diagnostic framework into a layer-by-layer implementation playbook, including design methods, workbook templates, and sequencing guidance for authorities ready to move from diagnosis to build. It is accompanied by an interactive dashboard and live database available at govspace.io/cips-dashboard.
Read the report: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6959799
Read the manual: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6981658
