Kirill Stroganov is a seasoned Machine Learning Engineer and Manager at Bank Russian Standard, where he leverages over a decade of engineering experience and four years of dedicated work in data science to drive innovation within the Risk Department. His expertise lies in data modeling,...
Kirill Stroganov is a seasoned Machine Learning Engineer and Manager at Bank Russian Standard, where he leverages over a decade of engineering experience and four years of dedicated work in data science to drive innovation within the Risk Department. His expertise lies in data modeling, machine learning, and advanced analytics, which he applies to enhance the bank's risk assessment and management processes.
In his current role, Kirill has made significant strides in optimizing model development through the creation of an auto-scoring library. This innovative solution has drastically reduced the time required to build and deploy new models from a month to just one day, enabling the bank to respond more swiftly to changing market conditions and customer needs. By eliminating reliance on costly third-party software, Kirill has not only improved operational efficiency but also fostered a culture of in-house innovation. His work can be explored further on GitHub, where he shares the WOE Scoring library (github.com/kiraplenkin/woe_scoring), showcasing his commitment to open-source solutions in the financial sector.
Additionally, Kirill has developed a service for recovering credit histories, which processes credit reports to fill in any gaps in data. This initiative not only enhances the accuracy of credit assessments but also supports the bank's commitment to responsible lending practices. With a robust skill set that includes Python, data analysis, machine learning, and natural language processing, Kirill is at the forefront of integrating artificial intelligence into banking operations, ensuring that Bank Russian Standard remains competitive in an increasingly data-driven landscape. His contributions are pivotal in shaping the future of risk management in the financial industry.
