Lorenzo Giurintano

Profile

Lorenzo Giurintano builds the data foundations that analytics and AI depend on. Across years in banking and engineering, he has designed data structures and models for large, complex datasets. His thesis applied unsupervised learning to banking transactions to aggregate the behaviour of potentially malicious actors, with explainability built in so regulators and developers can audit the system.

Areas of expertise

• 01Unsupervised learning and anomaly detection
• 02Explainable, auditable machine learning
• 03Data structures for multi-property analysis
• 04Columnar storage and high-volume pipelines

Selected engagements & achievements

1. 01

   Developed unsupervised behavioural aggregation of banking transactions to identify potentially malicious actors, with explainability methods for regulatory audit.

2. 02

   Designed data structures enabling high-volume cross-influence analysis of chemical and physical properties of liquids.

3. 03

   Built a large-scale knowledge graph powering conversational AI for banking customer service on every inbound channel, independent of modality (voice, text, image). The system detects gaps in its own knowledge and routes to the right human support, achieving significant deflection rates and improved service quality.