Recorded: April 21 | 1:00 p.m. ET
Manually tuning MBS prepayment models is messy. In what amounts to an elaborate trial-and-error exercise, modelers must frequently resort to subjectively selecting sub-populations to calibrate, running back-testing to see where and how the model is off, and then tweaking knobs and re-running the back-test to see the impacts. Rinse and repeat.
RiskSpan’s Janet Jozwik and Steven Sun present an approach for running a set of back-tests on MBS pools that automatically solves for the right set of tuners to align model results to actuals. Learn how, by automatically covering every feasible combination of model knobs possible, you can visualize for every pool the impact each knob combination has on:
- Modeled prepay vs. actuals
- Model error
- Refi incentive and other pool features
Managing Director, RiskSpan
Director, RiskSpan
[pardot-form height=”700 ” id=”6171″ title=”April 21 Webinar Recording: Automated Prepayment Model Calibration Using Machine Learning”]
Recorded: March 31 | 1:00 p.m. ET
Traditional statistical models apply a single set of coefficients by pooling a large dataset or for specific cohorts.
Hierarchical models learn from feature behavior across dimensions or timeframes.
Suhrud Dagli and Jing Liu host an informative workshop applying hierarchical models to a variety of mortgage and structured finance use cases, including:
- Changes in beta and covariance of portfolios across time
- Loan performance across geographies and history – e.g., combining credit performance data from 2008 with unemployment-driven credit issues in 2020.
- Issuer-level prepayment performance
Co-founder and Chief Innovation Officer, RiskSpan
Jing Liu
Model Developer, RiskSpan
[pardot-form height="700 " id="6052" title="March 31 Workshop: Advanced Forecasting Using Hierarchical Models"]
Recorded: January 13, 2021 | 1:00 p.m. ET
Traders and investors rely on time series patterns generated by asset performance to inform and guide their trading and asset allocation decisions. Economists take advantage of analogous patterns in macroeconomic and market data to forecast recessions and other market events.
But you need to be able to spot these patterns in order to use them.
Catch the latest in RiskSpan’s series of machine learning and data workshops as Chirag Soni and Jing Liu, two of RiskSpan’s experts working at the intersection of data science and capital markets, demonstrate how advanced machine learning techniques such as Dynamic Time Warping and KShape can be applied to automate time series analysis and effectively detect patterns hiding in your data.
Chirag and Jing will discuss specific applications, explain popular algorithms, and walk through code examples.
Join us on Wednesday, January 13th!
[pardot-form height=”700″ id=”4972″ title=”January 13 Workshop: Pattern Recognition in Time Series Data”]
Recorded: Dec. 2nd | 1:00 p.m. EDT
RiskSpan Director Steven Sun shares a procedural approach to tackling the difficulties of efficiently extracting structured data from images, scanned documents, and handwritten documents using Google’s latest Document AI Solution. This approach greatly improves:
- Effectiveness and accuracy of extracting data which will be otherwise difficult or impossible, and
- Automating and streamlining the process of feeding extracted data into a data analytic framework
[pardot-form height=”700″ id=”4909″ title=”Webinar Recording: December 2 Workshop: Measuring and Visualizing Feature Impact & Machine Learning Model Materiality”]
Director, RiskSpan
Watch RiskSpan CEO Bernadette Kogler’s interview with Acting Comptroller of the Currency Brian Brooks.
They discuss many topics include the OCC’s Project REACh, machine learning models to expand the credit box, blockchain’s role in housing finance, and the expanding definition of a chartered institution.
Recorded: Oct. 28th | 1:00 p.m. EDT
RiskSpan CIO Suhrud Dagli, who discussed how ML is being incorporated into model risk management during our Sep. 30 webinar: Machine Learning in Model Validation, demonstrates in greater detail how machine learning can be used:
- In input data validations,
- To measure feature impact, and
- To visualize how multiple features interact with each other
[pardot-form height=”700″ id=”4705″ title=”Webinar Recording: Measuring and Visualizing Feature Impact & Machine Learning Model Materiality”]
Co-Founder & Fintech Lead, RiskSpan
Recorded:
Sep. 17th | 1:00 p.m. EDT
RiskSpan’s Bernadette Kogler led a panel of industry experts in a review of the U.S. economy and how mortgage companies can employ alternative data to responsibly extend mortgage credit more broadly to current and potential homeowners.
Participants include
- Bernadette Kogler, Co-Founder & CEO, RiskSpan
- Amy Crews Cutts, President, AC Cutts and Associates
- Janet Jozwik, Managing Director, RiskSpan
- Laurie Goodman, Director, Housing Finance Policy Center, The Urban Institute
Recorded: September 30th | 1:00 p.m. EDT
Join our panel of experts as they share their latest work using machine learning to identify and validate model inputs.
- Suhrud Dagli, Co-Founder & Fintech Lead, RiskSpan
- Jacob Kosoff, Head of Model Risk Management & Validation, Regions Bank
- Nick Young, Head of Model Validation, RiskSpan
- Sanjukta Dhar, Consulting Partner, Risk and Regulatory Compliance Strategic Initiative, TCS Canada
[pardot-form height=”700″ id=”4149″ title=”Webinar Recording: Machine Learning in Model Validation”]
Featured Speakers
Co-Founder & Fintech Lead, RiskSpan
Head of Model Risk Management & Validation, Regions Bank
Head of Model Validation, RiskSpan
Consulting Partner, Risk and Regulatory Compliance Strategic Initiative, Tata Consulting
Recorded: August 12th | 1:00 p.m. EDT
Business-as usual macroeconomic scenarios that seemed sensible a few months ago are now obviously incorrect. Off-the-shelf models likely need enhancements. How can institutions adapt?
Credit modelers don’t need to predict the future. They just need to forecast how borrowers are likely to respond to changing economic conditions. This requires robust datasets and insightful scenario building.
Let our panel of experts walk you through how they approach scenario building, including:
- How mortgage delinquencies have traditionally tracked unemployment and how these assumptions may need to be altered when unemployment is concentrated in non-homeowning population segments.
- The likely impacts of home purchases and HPI on credit performance.
- Techniques for translating macroeconomic scenarios into prepayment and default vectors.
[pardot-form height=”700″ id=”3744″ title=”Webinar Recording: Good Models, Bad Scenarios? Delinquency, Forbearance, and COVID”]
Featured Speakers
VP of Loss Forecast and Allowance, Fannie Mae
Managing Director, RiskSpan
Co-founder and CIO, RiskSpan
Senior Research Associate, The Urban Institute