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Category: Events

Webinar: Geocoding Mortgage Data for ESG and Climate Risk Analysis

February 16th | 1:00 p.m. ET

Geocoding remains a particularly vexing challenge for the mortgage industry. Lenders, servicers, and loan/MSR investors know the addresses of the properties securing their mortgage assets. But most data pertaining to climate and other ESG considerations is available only by matching to a census tract or latitude/longitude.

And if you have ever tried mapping addresses, you know this exercise can be a lot harder than it looks. Fortunately, a growing body of geocoding tools and techniques is emerging to make the process more manageable than ever, even with less than perfect address data.

Join us on for a free RiskSpan webinar presenting a how-to guide on geocoding logic and its specific application to the mortgage space. You will learn a useful waterfall approach for linking census-tract-level, geo-specific data for climate risk and ESG to the property addresses in your portfolio.


 

Featured Speakers

Suhrud Dagli

Chief Innovation Officer, RiskSpan

Jason Huang

Manager, RiskSpan

Jason Lee

Software Engineer, RiskSpan


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

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? 

 

A growing body of evidence suggests that the high dimensionality of mortgage data spaces may actually make them ideal candidates for few-shot learning. 

 

Suhrud Dagli and Jing Liu present the latest installment in RiskSpan’s Data & Machine Learning Workshop series. Suhrud and Jing share examples of how they are using few-shot learning techniques in prepayment modeling and in automating quality control checks on uploaded mortgage data. 

 


Featured Speakers

Suhrud Dagli

Co-Founder and CIO, RiskSpan

Jing Liu

Senior Analyst, RiskSpan


Automating Compliance Risk Analytics

 Recorded: August 4th | 1:00 p.m. EDT

Completing the risk sections of Form PF, AIFMD, Open Protocol and other regulatory filings requires submitters to first compute an extensive battery of risk analytics, often across a wide spectrum of trading strategies and instrument types. This “pre-work” is both painstaking and prone to human error. Automating these upstream analytics greatly simplifies life downstream for those tasked with completing these filings.

RiskSpan’s Marty Kindler walks through a process for streamlining delta equivalent exposure, 10 year bond equivalent exposure, DV01/CS01, option greeks, stress scenario impacts and VaR in support not only of downstream regulatory filings but of an enhanced, overall risk management regime.


Featured Speaker

Martin Kindler

Managing Director, RiskSpan


Is Your Enterprise Risk Management Keeping Up with Recent Regulatory Changes?

Recorded: June 30th | 1:00 p.m. EDT

Nick Young, Head of RiskSpan’s Model Risk Management Practice, and his team of model validation analysts walk through the most important regulatory updates of the past 18 months from the Federal Reserve, OCC, and FDIC pertaining to enterprise risk management in general (and model risk management in particular).

Nick’s team present tips for ensuring that your policies and practices are keeping up with recent changes to AML and other regulatory requirements.


Featured Speakers

Nick Young

Head of Model Risk Management, RiskSpan


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 26 Webinar: Is Your Pricing Methodology Compliant With Rule 2a-5?

Recorded: May 26th | 1:00 p.m. ET

The SEC’s new Rule 2a-5 has important ramifications for anyone in the business of pricing hard-to-value instruments. It requires valuation practitioners to demonstrate good faith in implementing and following a defensible and transparent processes. But what does this mean as a practical matter?

On Wednesday, May 26th experts David Baum and Martin Dozier of Alston & Bird and Bill Moretti and Joe Sturtevant of RiskSpan explained and responded to your questions about:

  • What the new requirements are
  • Who is impacted and when
  • Implementation best practices
  • Potential issues with Rule 17a-7, and
  • Modeling considerations, including assumptions, back-testing, calibration, and data management.

Featured Speakers

William Moretti

Senior Managing Director, RiskSpan

David Baum

Partner, Investment Management, Trading and Markets Group, Alston & Bird LLP

Martin Dozier

Partner, Alston & Bird LLP

Joseph Sturtevant

Head of Valuation Services, RiskSpan


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

Suhrud Dagli

Co-founder and CIO, RiskSpan

Martin Kindler

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.

Suhrud Dagli

Co-founder and CIO, RiskSpan

Martin Kindler

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

Janet Jozwik

Managing Director, RiskSpan

Steven Sun

Director, RiskSpan



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