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Articles Tagged with: Feature Home

Webinar Recording: How Much Will That MSR Portfolio Really Cost You?

Recorded: June 8th | 1:00 p.m. ET

Accurately valuing a mortgage servicing rights portfolio requires accurately projecting MSR cash flows. And accurately projecting MSR cash flows requires a reliable forecast of servicing costs. Trouble is, servicing costs vary extensively from loan to loan. While the marginal cost of servicing a loan that always pays on time is next to nothing, seriously delinquent loans can easily cost hundreds, if not thousands, of dollars per year.

The best way to account for this is to forecast and assign servicing costs at the loan level – a once infeasible concept that cloud-native technology has now brought within reach. Our panelists present a novel, granular approach to servicing cost analytics and how to get to a truly loan-by-loan MSR valuation (without resorting to rep lines).

 

 

 

Featured Speakers

Venkat Mullur

SVP, Capital Markets, Ocwen

Paul Gross

Senior Quantitative Analyst, New Residential Investment Corp.

Dan Fleishman

Managing Director, RiskSpan

Joe Makepeace

Director, RiskSpan


Industry Virtual Roundtable: The Intersection of Climate Risk Management with Mortgage Loan & MSR Investing

April 14th | 2:00-3:15 p.m. ET

With both the public and private sectors increasingly making climate risk management a priority, attention in our industry is turning to what it means for mortgage loan and MSR investors.

Industry experts join RiskSpan and Housing Finance Strategies for a roundtable event where they engage in a discussion on the latest approaches and technology for mitigating climate risk management in mortgage portfolios.

The loan-level cash flows discussed in this webinar were generated using RiskSpan’s Edge Platform.

 

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Agenda (all times Eastern)

2:00-2:05 pm | WELCOME AND PROGRAM OVERVIEW 

Faith Schwartz, Founder & CEO, Housing Finance Strategies

2:05-2:20 pm | CLIMATE RISK’S IMPACT ON MORTGAGE FINANCE AND TOOLS TO MANAGE RISK

Janet Jozwik, Senior Managing Director and Head of Climate Risk, RiskSpan
Dan Raizman, Global Resilience Manager, Verisk Analytics

2:20-3:00 pm | PANEL DISCUSSION: CLIMATE RISK IN HOUSING FINANCE—RISK MANAGEMENT AND REGULATORY PERSPECTIVES

Faith Schwartz, Moderator
Mark Hanson, SVP, Freddie Mac
Kurt Johnson, CRO, Mr. Cooper
Sean Becketti, former Freddie Mac
Bernadette Kogler, CEO, RiskSpan

3:00-3:15 pm | QUESTIONS AND DISCUSSION OF POLLING RESULTS


Webinar: Geocoding Mortgage Data for ESG and Climate Risk Analysis

Recorded: 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.

Our panel presents 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


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


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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