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Articles Tagged with: Innovation

April 28 Workshop: Anomaly Detection

April 28 | 1:00 p.m. EDT

Outliers and anomalies refer to various types of occurrences in a time series. Spike of value, shift in level or volatility or a change in seasonal pattern are common examples. Anomaly detection depends on specific context. 

In this month’s installment in our Data and Machine Learning Workshop Series, RiskSpan Co-Founder & CIO Suhrud Dagli is joined by Martin Kindler, a market risk practitioner who has spent decades dealing with outliers.

Join Suhrud and Martin on Wednesday, April 28th, as they explore unsupervised approaches for detecting anomalies.

Suhrud Dagli

Co-founder and CIO, RiskSpan

Martin Kindler

Managing Director, RiskSpan



April 21 Webinar: Automated Prepayment Model Calibration Using Machine Learning

April 21 | 1:00 p.m. EDT

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.

Join RiskSpan’s Janet Jozwik and Steven Sun on Wednesday, April 21st, for a free webinar. They will 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

Janet Jozwik

Managing Director, RiskSpan

Steven Sun

Director, RiskSpan



March 31 Workshop: Advanced Forecasting Using Hierarchical Models

Recorded: March 31 | 1:00 p.m. EDT

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

Suhrud Dagli

Co-founder and Chief Innovation Officer, RiskSpan

Jing Liu

Model Developer, RiskSpan



Edge Enhancements: Spotlight AGENCY EDGE

2021 is off to a great start, but the Edge Team is not resting on its laurels.

On the heels of a year that saw more than a 30 percent increase in Edge subscribers, including a doubling of Agency Module users, we continue to add more of the Ginnie and GSE data you need.

Edge’s enhanced datasets make customizing S-curves even easier.

For example:

Loans with a principal deferral pay more slowly than loans without them when faced with the same refinancing incentive.

But how much more slowly?

Edge lets you quantify the difference, so you can adjust your models accordingly.

7 of the 10 largest U.S. broker/dealers use Edge to analyze Agency prepays.
Find out why.


January 13 Workshop: Pattern Recognition in Time Series Data

Recorded: January 13, 2021 | 1:00 p.m. EST

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! 



December 2 Workshop: Structured Data Extraction from Image with Google Document AI

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

Steven Sun

Director, RiskSpan


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