AI Fairness Assessment

Nemanja Djajić is a Managing Consultant with more than 12 years of experience in credit risk modeling and data science area. He gained his experience through multiple roles in banking industry including the position of data science department director in one of the biggest banks in Eastern Europe. Nemanja’s main area of expertise lies within the development and validation of risk and business related models, using traditional or machine learning methodologies.

Marino is a Senior Quantitative Risk & Machine Learning consultant at Finalyse. He is a qualified actuary with 7 years of experience. He has successfully delivered several projects for banks and insurance companies including a conversational chatbot, AI-powered price optimisation engine, Deep Learning time-series forecast models, challenger software for Credit Risk, AML, and PEP models for traditional and fully digital banking. His expertise extends to non-life insurance pricing,  risk modelling, and regulatory frameworks such as IFRS and Solvency II. He is proficient in Python, Linux, Docker, Flask, GCP,  Azure, AWS, and various other tech stacks.

Maciej is a Managing Consultant with 7 years of experience in model development and validation, specialising in machine learning and regulatory credit risk. He has hands-on expertise in building and assessing ML models for applications such as customer segmentation and credit scoring, including the use of alternative data sources as well as Pillar 1 and Pillar 2 modelling experience which he gained on projects completed for clients across Europe and the Middle East.

Gergely Tréfa is a Senior Consultant at Finalyse with more than 5 years experience in credit risk model development, validation and data science. He has extensive experience in developing and validating PD, LGD, EAD models under IRB and IFRS frameworks. Gergely holds a Master's in Quantitative Finance and is proficient in R, Python, and SQL. In addition to regulatory modeling, he has developed non-regulatory machine learning models, such as collection and prepayment models, and has applied NLP techniques to automate risk reporting processes.