This book offers a bold new perspective on how justice systems are evaluated. In an era where efficiency, transparency, and accountability are not just ideals but expectations, this book equips readers with the tools to move beyond opinion and into measurable reality.

Blending real-world data with cutting-edge technologies—such as generative AI and agent-based modeling—and a comparative approach across European jurisdictions, this volume redefines how we assess legal performance. From analyzing court delays and forecasting bottlenecks to simulating judicial processes through digital twins, it opens the door to smarter policymaking and legal innovation.

Whether you're a legal tech developer, policymaker, academic, or professional working in real estate foreclosures, credit risk, or legal system design, this book delivers actionable insights and models. Explore chapters on KPIs in civil justice, ESG-driven valuation in judicial sales, and predictive models for system stress under economic shocks.

What sets this book apart is its practical focus: it’s not just about theory—it’s about what can be done now. Supported by real case data, industry partnerships, and contributions from experts in law, AI, and finance, this book is a timely and essential read for anyone working at the intersection of justice and technology.

Marco Campennì is a computational social scientist and Senior Lecturer in Sustainable Business and Environment at University of Exeter in Cornwall, Business School, working as part of the Department of Management, Sustainable Futures unit. His work is centered on modelling the evolution of social behaviours, using different modeling techniques and tools (such as, agent-based modeling, social network analysis, evolutionary game theory). His research activity is focused on both theoretical and data informed models of social behaviors and dynamics of human beings from an evolutionary and cross-disciplinary perspective. Being an expert in modelling sustainable systems, his interested is in better understanding the internal and external dynamics of formal and informal social institutions.

Federico Cecconi is an Italian computer scientist with extensive experience in AI, data modeling, and simulation. As Chief Scientific Officer at QBT since 2012, he has led numerous AI-driven projects across sectors like real estate, finance, and legal tech. His work includes tools such as VisuTech for document analysis, Nemesi for legal NLP, and FLIGHTAI for drone risk assessment, often using generative AI and machine learning. He also manages software for credit risk, AML, and real estate evaluation. Cecconi has a strong academic background, conducting research at LABSS (ISTC-CNR) on norm diffusion and market simulations since 2007. He’s taught at LUMSA, GEMA, and L.U.de.S. University, developing courses on neural networks, computational models, and applied AI. His academic contributions include participation in European projects and a focus on socio-economic modeling through agent-based simulations.

His technical skills span machine learning, statistical modeling, text mining, and simulation. He’s proficient in Python, R, Java, MATLAB, and has worked on software for legal timing analysis, drone operations, and real estate data platforms. His experience includes both classic and NoSQL databases, as well as semantic AI and visual recognition. Cecconi has published over twenty academic papers in the past five years, covering topics from crowdfunding dynamics to disinformation analysis and neurophysiology. His recent work explores AI’s role in economic policy, including climate strategies using GPT-4, highlighting a multidisciplinary approach to digital innovation.