Date: October 21, 2020 | Time: 11:00 AM - 12:00 PM EST
When it comes to personal loans, failure is an option. For many financial organizations, this is a serious cost of doing business. According to the Wall Street Journal, private default rates rose in the last quarter to a worrisome 8.1%. Lending today is high-risk, high-reward and only those with the best insights will be able to weather the storm.
Thanks to modern AI, default expenses can be reduced by improved loan risk analysis to predict the likelihood a loan will default. This is better than just using a credit history that evaluates the individual and not the loan.
Given the history of other loans, what's the likelihood of a given potential loan going bad?
Join us on October 21 when we discuss the trends in the financial sector and showcase a machine learning model of loan risk. We will use loan data from the peer-to-peer lending company LendingClub and model this data using Azure Databricks to understand how an institution can maximize profits in a world of uncertainty.
Showcase a predictive model in Azure Databricks about loan risk analysis.
Estimate the probability of default for individual loans.
Use real-world lending data to identify which features make a loan most at-risk.
Specialize your machine learning training to minimize losses or maximize financial gains.
This webinar is for both a technical and non-technical audience in finance. A portion of the talk will demo our analysis, so a basic understanding of Python is helpful but not necessary.
Jon Gore, Finance Services Account Lead
Jon leads all Financial Services and Insurance accounts at BlueGranite. With extensive project management, solution development, and solution design experience, Jon works with clients to envision success and improve their day to day operations. His Microsoft oriented background and knowledge in the BI industry help him to bring a unique and valued perspective to his role.
Tom Weinandy, Data Scientist
Tom is a Data Scientist and Business Economist specializing in data analytics and machine learning for industrial settings. His background experience is in nonprofits and education. Tom holds a bachelors in Social Entrepreneurship from John Carroll U., an M.B.A. from Wheeling Jesuit U., and is finishing a Ph.D. in Applied Economics from Western Michigan U. with his dissertation entitled "Applied Microeconomics and Business Intelligence in the Digital Age."
Colby Ford, AI Architect
Dr. Colby Ford is an AI Architect at BlueGranite. Coming from a background in mathematics, statistics, and computational biology, he combines this expertise to bring AI to everyone. Using R and Python, he puts Machine Learning to work to gain insight from data. Outside of BlueGranite, Colby is an avid machine learning researcher. Check out Colby’s website at www.colbyford.com.