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How machine learning can improve pricing performance

#artificialintelligence

New analytical capabilities have the potential to transform the way banks and other payment providers price products and services. Obtaining fair compensation for complex payment products, such as corporate cards, merchant acquisitions, and treasury-management services, has long been a major challenge. This is primarily because these products tend to be intricate, offered in myriad forms, and implemented across diverse markets. Treasury services, for instance, might have 1,000 or more different fees, and prices are often embedded in private contracts not shared within the organization. Throughout the payment industry, these problems are further complicated by ever-changing payment methods and platforms created by the rapid evolution of payment technologies.


Derisking machine learning and artificial intelligence

#artificialintelligence

The added risk brought on by the complexity of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks. Machine learning and artificial intelligence are set to transform the banking industry, using vast amounts of data to build models that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate value of more than $250 billion in the banking industry.1 1.For the purposes of this article machine learning is broadly defined to include algorithms that learn from data without being explicitly programmed, including, for example, random forests, boosted decision trees, support-vector machines, deep learning, and reinforcement learning. The definition includes both supervised and unsupervised algorithms. For a full primer on the applications of artificial intelligence, we refer the reader to "An executive's guide to AI."


Derisking machine learning and artificial intelligence

#artificialintelligence

Machine learning and artificial intelligence are set to transform the banking industry, using vast amounts of data to build models that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate value of more than $250 billion in the banking industry.1 1.For the purposes of this article machine learning is broadly defined to include algorithms that learn from data without being explicitly programmed, including, for example, random forests, boosted decision trees, support-vector machines, deep learning, and reinforcement learning. The definition includes both supervised and unsupervised algorithms. For a full primer on the applications of artificial intelligence, we refer the reader to "An executive's guide to AI." But there is a downside, since machine-learning models amplify some elements of model risk.


Derisking AI by design: How to build risk management into AI development

#artificialintelligence

Artificial intelligence (AI) is poised to redefine how businesses work. Already it is unleashing the power of data across a range of crucial functions, such as customer service, marketing, training, pricing, security, and operations. To remain competitive, firms in nearly every industry will need to adopt AI and the agile development approaches that enable building it efficiently to keep pace with existing peers and digitally native market entrants. But they must do so while managing the new and varied risks posed by AI and its rapid development. The reports of AI models gone awry due to the COVID-19 crisis have only served as a reminder that using AI can create significant risks.


Investment management emerging stronger post-COVID

#artificialintelligence

Since February 2020, there has been a dramatic shift in the operating environment of financial markets, with increased volatility, repricing of assets, and transitions of favored asset classes. Uncertainty abounds for investment managers. According to one hypothetical stress scenario, individual managers may have seen assets under management fluctuate by up to one-third in the United States as outflows and valuation changes have affected many during the pandemic.1 Even before the emergence of COVID-19, the situation for investment managers appeared ripe for change. In 2019, most US equity managers were unable to generate excess returns, net of fees, relative to their benchmarks.