Risk is the constant

The continuous introduction of new AI technologies enables financial institutions to enhance their operating model and increase business performance. Through the sound use of AI, existing processes and ways of working can be redefined and improved. We help financial institutions in defining a path towards an operating model where AI capabilities are embedded within the organisation. By combining our sector and technological expertise, we help financial institutions identify valuable and realistic use cases; develop robust, secure, and future-proof solutions; and realise adoption, ensuring investments translate into sustained value.

Financial institutions rely on adequate risk models to meet regulatory requirements and manage financial risks. In practice, many banks and insurance companies struggle with ambiguity, data quality issues, resource scarcity, and long iteration cycles. We help our clients overcome their challenges in risk modelling by aligning stakeholders, adopting smart technologies, sharing experiences, and working iteratively to obtain adequate and compliant models in a timely manner. We support our clients throughout the full model life cycle — from improving data foundations, model (re)development, model validation, regulatory reviews, model implementation, and model maintenance.

Causal inference methods identify true cause-and-effect relationships to better understand the impact of decisions, enabling effectively influencing future outcomes. With causal inference methods, we can help setting credit limits, renewal prices, and bundle discounts that explicitly balance revenue, risk, and customer lifetime value; target promotions at customers who are truly persuaded rather than only likely to respond; and isolate the real effect of initiatives like loyalty programs from underlying trends and other factors that impact the system simultaneously.