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Machine learning in risk management

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.

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“It all depends on expert opinion, data, and common sense”

Two ING experts share their views on deposit modelling

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.

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Anti-money laundering and transaction monitoring in times of a pandemic

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.

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Liquidity Risk Management for Insurers

Latest insurance sector regulatory developments with increased attention on liquidity risk management

Supervision of insurers has so far focused mainly on solvency. There has been less focus on liquidity risk, which is generally not seen as a material risk for insurers, given the characteristics of traditional (life) insurers.

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IFRS 17 – EU ignoring annual cohorts?

Potential inconsistency between group and local reporting for international insurers

IFRS 17 has been a long time in the making. One of its most controversial aspects discussed this year is the annual cohorts requirement. On the one hand, the IASB hopes to shed light onto possible onerous insurance contract generations. On the other hand, most of the insurance industry argues that the requirement is against the basic principle of sharing the risk among contracts and essentially their business model. Meanwhile, the European Financial Reporting Advisory Group (EFRAG) is divided in forming an opinion on this matter.

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