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.
The fight against money laundering (ML) and terrorist financing contributes to global security, the integrity of the financial system and sustainable growth. In recent years, financial institutions have been under more regulatory pressure when it comes to the detection and prevention of financial crime. Financial institutions have therefore stepped up their measures against fraud, money laundering, terrorist financing and other forms of financial crime. In this fight, transaction monitoring is a crucial tool to detect unusual transactions and patterns.
The Financial Conduct Authority (FCA) ensured bank panels support LIBOR, and this is coming to a close at the end of 2021. Currently more than 80% of CHF loans are priced with the CHF LIBOR as a basis. The transition to a new reference rate poses a number of different challenges for the market. This fifth part of our article series presents the checklist of the Swiss National Working Group (NWG) and the Zanders IBOR Assessment.
Since the introduction of the Pillar 1 capital charge for market risk, banks must hold capital for Foreign Exchange (FX) risk, irrespective of whether the open FX position was held on the trading or the banking book. An exception was made for Structural Foreign Exchange Positions, where supervisory authorities were free to allow banks to maintain an open FX position to protect their capital adequacy ratio in this way.