ENFINT has shared its expertise in implementing ML-models at Future Banks Summit

ENFINT's experts presented their avant-garde solutions, highlighting their proficiency in AI/ML, Data Science, MLOps, custom application development, and other cutting-edge technologies. Oleg Baranov, Managing Partner at ENFINT, delivered a presentation “How to win an ML/AI race in a fast paced environment?” In his speech, delved into the realm of artificial intelligence and how it could be used in banks for an array of tasks, including risk assessment, credit scoring, IT operations, personalizing customer experience, sales and marketing optimization, fraud detection, and much more. The expert noted that adoption of AI in financial services is rapidly advancing, with banks implementing it across various innovative use cases. In fact, 85% of IT banking leaders have a “clear strategy” for adopting AI in developing new products and services.

Oleg Baranov has observed that ML-models, which were previously only used in limited areas such as credit scoring, next-best-offer, and ECL calculation for IFRS9, will be integrated into all business processes of a bank over the next three years. He also emphasized the considerable threshold for starting an ML project, which necessitates special tools and processes for model monitoring and governance. Additionally, each step of training and implementing the models is costly due to high labor and infrastructure costs. To successfully implement ML-models, banks should hire data scientists with subject matter expertise, establish a committee to lead ML/AI transformation, create a centralized infrastructure and services for managing the lifecycle of ML-models, and adopt an ML/AI strategy.

The expert elaborated in more details ENFINT MLOps platform that supports all stages of the ML lifecycle: data preparation, model creation, training, deployment and monitoring. The platform provides an individual digital Open Source platform with easy-to-use and efficient infrastructure to automate the work of Data Science teams and enables the bank to increase the time-to-market of ML-models and AI-driven services implementation. In addition, Oleg Baranov mentioned the AI-driven ALM solution that allows customers to measure the impact of a crisis scenario on the bank’s key performance rations, including provisions, profitability, liquidity, and capital.