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The Journal of Structured Finance

The Journal of Structured Finance

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Machine Learning for Structured Finance

Eric Kolchinsky
The Journal of Structured Finance Fall 2018, 24 (3) 7-25; DOI: https://doi.org/10.3905/jsf.2018.24.3.007
Eric Kolchinsky
is head of the National Association of Insurance Commissioners’ Structured Securities Group and Capital Markets Bureau in New York, NY. ekolchinsky@naic.org
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Abstract

Machine learning and artificial intelligence have evolved beyond simple hype and have integrated themselves in business and in popular conversation as an increasing number of smart applications profoundly transform the way we work and live. This article defines machine learning in terms of potential benefits and pitfalls for a nontechnical audience, and gives examples of popular and powerful machine learning algorithms: k-means clustering, principal component analysis, and artificial neural networks. Three important philosophical challenges of machine learning are introduced: the no free lunch theorem, the curse of dimensionality, and the bias–variance trade-off.

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The Journal of Structured Finance: 24 (3)
The Journal of Structured Finance
Vol. 24, Issue 3
Fall 2018
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Machine Learning for Structured Finance
Eric Kolchinsky
The Journal of Structured Finance Oct 2018, 24 (3) 7-25; DOI: 10.3905/jsf.2018.24.3.007

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Machine Learning for Structured Finance
Eric Kolchinsky
The Journal of Structured Finance Oct 2018, 24 (3) 7-25; DOI: 10.3905/jsf.2018.24.3.007
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  • Article
    • Abstract
    • INTRODUCTION
    • DEFINITION
    • NO FREE LUNCH
    • BASIC MACHINE LEARNING: k-MEANS CLUSTERING
    • THE CURSE OF DIMENSIONALITY
    • DIMENSIONALITY REDUCTION: PCA
    • BIAS AND VARIANCE TRADE-OFF
    • ARTIFICIAL NEURAL NETWORKS
    • AUTO-EPILOGUE
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
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