May 19 Workshop: Quality Control Using Anomaly Detection (Part 2)
Recorded: May 19 | 1:00 p.m. ET
Last month, RiskSpan’s Suhrud Dagli and Martin Kindler outlined the principles underlying anomaly detection and its QC applications related to market data and market risk. You can view a recording of that workshop here.
On Wednesday, May 19th, Suhrud presented Part 2 of this workshop, which dove into mortgage loan QC and introduce coding examples and approaches for avoiding false negatives using open-source Python algorithms in the Anomaly Detection Toolkit (ADTK).
RiskSpan presents various types of detectors, including extreme studentized deviate (ESD), level shift, local outliers, seasonal detectors, and volatility shift in the context of identifying spike anomalies and other inconsistencies in mortgage data. Specifically:
- Coding examples for effective principal component analysis (PCA) loan data QC
- Use cases around loan performance and entity correction, and
- Novelty detection
Co-founder and CIO, RiskSpan
Managing Director, RiskSpan
April 28 Workshop: Anomaly Detection
Recorded: April 28 | 1:00 p.m. ET
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.
Suhrud and Martin explore unsupervised approaches for detecting anomalies.
Co-founder and CIO, RiskSpan
Managing Director, RiskSpan
April 21 Webinar: Automated Prepayment Model Calibration Using Machine Learning
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
March 31 Workshop: Advanced Forecasting Using Hierarchical Models
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
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
