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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.


Tutorial: Safe and Reliable Machine Learning

arXiv.org Artificial Intelligence

This document serves as a brief overview of the "Safe and Reliable Machine Learning" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). The talk slides can be found here: https://bit.ly/2Gfsukp, while a video of the talk is available here: https://youtu.be/FGLOCkC4KmE, and a complete list of references for the tutorial here: https://bit.ly/2GdLPme.


How is Machine Learning Different from Statistics and Why it Matters

#artificialintelligence

As noted in the paper Derisking ML and AI by McKinsey [4], ML algorithms are typically far more complex than their statistical counterparts and often require design decisions to be made before the training process begins. The benefits of ML include superior performance and accuracy but their complexity leads to an added layer of challenge of interpretation, bias and compliance for ML. This is not just a technical problem though. The paper rightly points out that the degree of interpretability required is a policy choice. Feature Engineering -- ML is more complex because of the inherent difficulty of feature engineering -- that is, which features to use? How sound is each feature? Is it consistent with policy?


Machine learning governance - Risk.net

#artificialintelligence

The ability of machine learning models to read great quantities of unstructured data, spot patterns and translate it into actionable information is driving a significant uptake in the technology. Today, there is great interest in harnessing machine learning to turn the massive volumes of data – including non-traditional data – into new insights and information. In contrast to traditional statistical models, which are limited in the number of dimensions they can effectively access, machine learning models overcome these limitations and can ingest vast amounts of unstructured data, identify patterns and translate them into actionable information. It is therefore no surprise that machine learning modelling is being eagerly adopted. A recent survey conducted by SAS and the Global Association of Risk Professionals found that, over the next three to five years, businesses expect to significantly increase adoption of artificial intelligence (AI) and machine learning models to support key risk business use cases (see figure 1).