Wednesday, July 27th | 1:00 p.m. EDT
Register for the next Live Demo of RiskSpan’s award-winning Edge Platform. Learn more and ask questions at our bi-weekly, 45-minute demo.
Historical Performance Tool: Slice and dice historical loan performance in the Agency and PLRMBS universe to find outperforming cohorts.
Predictive Loan-Level Pricing and Risk Analytics: Produce loan-level pricing and risk on loans, MSRs, and structured products in minutes – with behavioral models applied at the loan-level, and other assumptions applied conveniently to inform bids and hedging.
Loan Data Management: Let RiskSpan’s data scientists consolidate and enhance your data across origination and servicing platforms, make it analytics-ready, and maintain if for ongoing trend analysis.
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About RiskSpan:
RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.
Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.
Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.
Presenters
Director, RiskSpan
Sales Executive, RiskSpan
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).
[pardot-form height=”600 ” id=”9647″ title=”Webinar: How Much Will That MSR Portfolio Really Cost You?”]
Featured Speakers
SVP, Capital Markets, Ocwen
Senior Quantitative Analyst, New Residential Investment Corp.
Managing Director, RiskSpan
Director, RiskSpan
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.
[pardot-form height="520" id="9010" title="Industry Virtual Roundtable: The Intersection of Climate Risk Management with Mortgage Loan & MSR Investing"]
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
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.
[pardot-form height=”700 ” id=”8679″ title=”Webinar: Geocoding Mortgage Data for ESG and Climate Risk Analysis”]
Featured Speakers
Chief Innovation Officer, RiskSpan
Manager, RiskSpan
Software Engineer, RiskSpan
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.
[pardot-form width=”400″ height=”400″ id=”7546″ title=”Workshop: Applying Few-Shot Learning Techniques to Mortgage Data”]
Featured Speakers
Co-Founder and CIO, RiskSpan
Jing Liu
Senior Analyst, RiskSpan
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.
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Featured Speaker
Managing Director, RiskSpan
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.
[pardot-form height=”450 ” id=”6879″ title=”Webinar: Is Your Enterprise Risk Management Keeping Up with Recent Regulatory Changes?”]
Featured Speakers
Head of Model Risk Management, RiskSpan
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.
[pardot-form height=”700 ” id=”6683″ title=”May 26 Workshop Recording: Is Your Pricing Methodology Compliant With Rule 2a-5?”]
Featured Speakers
Senior Managing Director, RiskSpan
Partner, Investment Management, Trading and Markets Group, Alston & Bird LLP
Partner, Alston & Bird LLP
Head of Valuation Services, RiskSpan
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
[pardot-form height=”700 ” id=”6427″ title=”May 19 Workshop: Quality Control Using Anomaly Detection (Part 2)”]
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
[pardot-form height=”700 ” id=”6173″ title=”April 28 Workshop: Anomaly Detection”]