Managing interest rate and liquidity risk on savings and current accounts is a hot topic for banks in 2021. Risk, ALM, and treasury managers have to navigate changing regulatory requirements, changing withdrawal behavior and deposit pricing strategies due to COVID-19, and decreasing market rates.
Currently, for many organizations, operational resilience is at the top of the agenda of the Board and senior management. The COVID-19 pandemic clearly showed how vulnerable societies and organizations can be to unexpected and unforeseen events.
Climate and environmental changes are viewed among the most important risks in society at present. As the financial sector is key for the transition towards a low-carbon and more circular economy, financial institutions have to deal with climate-related and environmental financial risks (C&E risks). At the same time, the increased importance of these C&E risks also presents new business opportunities for the financial sector. Therefore, to support banks in their self-assessment and action plans, Zanders developed a Scan & Plan Solution on C&E risks.
Machine learning (ML) models have already been around for decades. The exponential growth in computing power and data availability, however, has resulted in many new opportunities for ML models. One possible application is to use them in financial institutions’ risk management. This article gives a brief introduction of ML models, followed by the most promising opportunities for using ML models in financial risk management.
The low interest rate environment has faced banks with structural changes in customer behavior and converging products such as savings and current accounts. ING, one of Europe’s largest players in the savings market and a long-term client of Zanders, has positioned itself as one of the frontrunners in this environment. We sat down with Tom Tschirner (head of market risk at ING Germany) and Maarten Hummel (financial risk officer at ING Group) to gather their view on modeling and balance sheet management after these structural shifts.