Non-Linear Paths to Leadership: RiskSpan to Join Structured Finance Association WiS NextGen Panel
On Tuesday, November 16th RiskSpan CEO Bernadette Kogler joined fellow Women in Securitization NextGen panelists Beth O’Brien, Adama Kah, and Libby Cantrill, CFA to discuss Seizing Opportunites at Every Stage of Your Career, moderated by Structured Finance Association President Kristi Leo.
Watch here: https://structuredfinance.org/women-in-securitization/ 
Topics included:
- Why it’s essential to take risks in your career
- How to seize opportunities and take on challenges
- Leveraging an entrepreneurial spirit when exploring possibilities that don’t align with a preset career path – and taking that leap
Data & Machine Learning Workshop Series
RiskSpan’s Edge Platform is supported by a dynamic team of professionals who live and breathe mortgage and structured finance data. They know firsthand the challenges this type of data presents and are always experimenting with new approaches for extracting maximum value from it.
In this series of complimentary workshops our team applies machine learning and other innovative techniques to data that asset managers, broker-dealers and mortgage bankers care about.
Check out our recorded workshops
Measuring and Visualizing Feature Impact & Machine Learning Model Materiality
RiskSpan CIO Suhrud Dagli 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.
Structured Data Extraction from Images Using Google Document AI
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.
Pattern Recognition in Time Series Data
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.
Advanced Forecasting Using Hierarchical Models
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. This informative workshop applies hierarchical models to a variety of mortgage and structured finance use cases.
Quality Control with Anomaly Detection (Part I)
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. RiskSpan Co-Founder & CIO Suhrud Dagli is joined by Martin Kindler, a market risk practitioner who has spent decades dealing with outliers.
Quality Control with Anomaly Detection (Part 2)
Suhrud Dagli presents 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).
Applying Few-Shot Learning Techniques to Mortgage Data
Few-shot and one-shot learning models continue to gain traction in a growing number of industries – particularly those in which large training and testing samples are hard to come by. But what about mortgages? Is there a place for few-shot learning where datasets are seemingly so robust and plentiful?
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
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