How to Apply Machine Learning in Your Lending Business

[vc_row][vc_column][vc_custom_heading text=”How to Apply Machine Learning in Your Lending Business” use_theme_fonts=”yes”][vc_custom_heading text=”(and explain the outcomes to your regulator)” font_container=”tag:h3|text_align:left” google_fonts=”font_family:Roboto%3A100%2C100italic%2C300%2C300italic%2Cregular%2Citalic%2C500%2C500italic%2C700%2C700italic%2C900%2C900italic|font_style:300%20light%20italic%3A300%3Aitalic”][vc_column_text]Machine learning-based underwriting can help lenders approve more borrowers and significantly reduce defaults. Yet only a small vanguard of lenders are using machine learning in their credit business. The complexity of machine learning models is one reason that lenders hesitate to become early adopters. AI’s notorious “black box” problem, which makes it hard to explain machine learning-generated results to the regulators, is another.[/vc_column_text][/vc_column][/vc_row][vc_row equal_height=”yes” content_placement=”top”][vc_column width=”1/3″][vc_custom_heading text=”Sign Up For The Discussion” font_container=”tag:h4|text_align:left” use_theme_fonts=”yes”][vc_separator color=”black” css=”.vc_custom_1493733523939{padding-bottom: 10px !important;}”][vc_column_text]

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    [/vc_column_text][/vc_column][vc_column width=”2/3″][vc_custom_heading text=”The Details” font_container=”tag:h4|text_align:left” use_theme_fonts=”yes”][vc_separator color=”black” css=”.vc_custom_1493733523939{padding-bottom: 10px !important;}”][vc_row_inner][vc_column_inner width=”3/4″][vc_column_text]Date & Time: April 24, 2018 at 1:00pm EST

    Host: Karen Webster, CEO, PYMNTS

    Guest: Douglas Merrill, Founder of ZestFinance

    In this Webinar, Karen Webster and Douglas Merrill, founder of ZestFinance and former Chief Information Officer at Google, will discuss how lenders can overcome these obstacles and use machine learning to dramatically improve the economics of their credit businesses. Specifically, they will cover:

    • The basics of machine learning, including common misconceptions
    • The opportunities for machine learning in credit underwriting
    • The “black box” problem in machine learning
    • The interpretability and auditability of machine learning methods
    • How to implement machine learning in your organization

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