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Articles Tagged with: Feature Home

Applying Few-Shot Learning Techniques to Mortgage Data

Recorded: September 22nd | 1:00 p.m. EDT

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



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

Suhrud Dagli

Co-founder and Chief Innovation Officer, RiskSpan

Jing Liu

Model Developer, RiskSpan



January 13 Workshop: Pattern Recognition in Time Series Data

Recorded: January 13, 2021 | 1:00 p.m. ET

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.

Catch the latest in RiskSpan’s series of machine learning and data workshops as Chirag Soni and Jing Liu, two of RiskSpan’s experts working at the intersection of data science and capital markets, demonstrate how advanced machine learning techniques such as Dynamic Time Warping and KShape can be applied to automate time series analysis and effectively detect patterns hiding in your data.

Chirag and Jing will discuss specific applications, explain popular algorithms, and walk through code examples.

Join us on Wednesday, January 13th! 



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

Steven Sun

Director, RiskSpan


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