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Articles Tagged with: Innovation and Alternative Data

COVID-19 and the Cloud

COVID-19 creates a need for analytics in real time

Regarding the COVID-19 pandemic, Warren Buffet has observed that we haven’t faced anything that quite resembles this problem” and the fallout is “still hard to evaluate. 

The pandemic has created unprecedented shock to economies and asset performance. The recent unemployment  data, although encouraging , has only added to the uncertaintyFurthermore, impact and recovery are unevenoften varying considerably from county to county and city to city. Consider: 

  1. COVID-19 cases and fatalities were initially concentrated in just a few cities and counties resulting in almost a total shutdown of these regions. 
  2. Certain sectors, such as travel and leisure, have been affected worse than others while other sectors such as oil and gas have additional issues  Regions with exposure to these sectors have higher unemployment rates even with fewer COVID-19 cases 
  3. Timing of reopening and recoveries have also varied due to regional and political factors. 

Regional employment, business activity, consumer spending and several other macro factors are changing in real time. This information is available through several non-traditional data sources. 

Legacy models are not working, and several known correlations are broken. 

Determining value and risk in this environment is requiring unprecedented quantities of analytics and on-demand computational bandwidth. 

COVID-19 in the Cloud

Need for on-demand computation and storage across the organization 

I don’t need a hard disk in my computer if I can get to the server faster… carrying around these non-connected computers is byzantine by comparison.” ~ Steve Jobs 

Front office, risk management, quants and model risk management – every aspect of the analytics ecosystem requires the ability to run large number of scenarios quickly. 

Portfolio managers need to recalibrate asset valuation, manage hedges and answer questions from senior management, all while looking for opportunities to find cheap assets. Risk managers are working closely with quants and portfolio managers to better understand the impact of this unprecedented environment on assets. Quants must not only support existing risk and valuation processes but also be able to run new estimations and explain model behavior as data streams in from variety of sources. 

These activities require several processors and large storage units to be stood up on-demand. Even in normal times infrastructure teams require at least 10 to 12 weeks to procure and deploy additional hardware. With most of the financial services world now working remotely, this time lag is further exaggerated.  

No individual firm maintains enough excess capacity to accommodate such a large and urgent need for data and computation. 

The work-from-home model has proven that we have sufficient internet bandwidth to enable the fast access required to host and use data on the cloud. 

Cloud is about how you do computing

“Cloud is about how you do computing, not where you do computing.” ~ Paul Maritz, CEO of VMware 

Cloud computing is now part of everyday vocabulary and powers even the most common consumer devices. However, financial services firms are still in early stages of evaluating and transitioning to a cloud-based computing environment. 

Cloud is the only way to procure the level of surge capacity required today. At RiskSpan we are computing an average of half-million additional scenarios per client on demand. Users don’t have the luxury to wait for an overnight batch process to react to changing market conditions. End users fire off a new scenario assuming that the hardware will scale up automagically. 

When searching Google’s large dataset or using Salesforce to run analytics we expect the hardware scaling to be limitless. Unfortunately, valuation and risk management software are typically built to run on a pre-defined hardware configuration.  

Cloud native applications, in contrast, are designed and built to leverage the on-demand scaling of a cloud platform. Valuation and risk management products offered as SaaS scale on-demand, managing the integration with cloud platforms. 

Financial services firms don’t need to take on the burden of rewriting their software to work on the cloud. Platforms such as RS Edge enable clients to plug their existing data, assumptions and models into a cloudnative platform. This enables them to get all the analytics they’ve always had—just faster and cheaper.  

Serverless access can also help companies provide access to their quant groups without incurring additional IT resource expense. 

A recent survey from Flexera shows that 30% of enterprises have increased their cloud usage significantly due to COVID-19.

COVID-19 in the Cloud

Cloud is cost effective 

In 2000, when my partner Ben Horowitz was CEO of the first cloud computing company, Loudcloud, the cost of a customer running a basic Internet application was approximately $150,000 a month.”  ~ Marc Andreessen, Co-founder of Netscape, Board Member of Facebook 

Cloud hardware is cost effective, primarily due to the on-demand nature of the pricing model. $250B asset manager uses RS Edge to run millions of scenarios for a 45minute period every day. Analysis is performed over a thousand servers at a cost of $500 per month. The same hardware if deployed for 24 hours would cost $27,000 per month 

Cloud is not free and can be a two-edged sword. The same on-demand aspect thaenables end users to spin up servers as needed, if not monitoredcan cause the cost of such servers to accumulate to undesirable levelsOne of the benefits of a cloud-native platform is built-on procedures to drop unused servers, which minimizes the risk of paying for unused bandwidth. 

And yes, Mr. Andreeseen’s basic application can be hosted today for less than $100 per month 

The same survey from Flexera shows that organizations plan to increase public cloud spending by 47% over the next 12 months. 

COVID-19 in the Cloud

Alternate data analysis

“The temptation to form premature theories upon insufficient data is the bane of our profession.” ~ Sir Arthur Conan Doyle, Sherlock Holmes.

Alternate data sources are not always easily accessible and available within analytic applications. The effort and time required to integrate them can be wasted if the usefulness of the information cannot be determined upfront. Timing of analyzing and applying the data is key. 

Machine learning techniques offer quick and robust ways of analyzing data. Tools to run these algorithms are not readily available on a desktop computer.  

Every major cloud platform provides a wealth of tools, algorithms and pre-trained models to integrate and analyze large and messy alternate datasets. 

Join fintova’s Gary Maier and me at 1 p.m. EDT on June 24th as we discuss other important factors to consider when performing analytics in the cloud. Register now.

Chart of the Month: Tracking Mortgage Delinquency Against Non-traditional Economic Indicators by MSA

Tracking Mortgage Delinquency Against Non-traditional Economic Indicators by MSA 

Traditional economic indicators lack the timeliness and regional granularity necessary to track the impact of COVID-19 pandemic on communities across the country. Unemployment reports published by the Bureau of Labor Statistics, for example, tend to have latency issues and don’t cover all workers. As regional economies attempt to get back to a new “normal” RiskSpan has begun compiling non-traditional “alternative” data that can provide a more granular and real-time view of issues and trends. In past crises, traditional macro indicators such as home price indices and unemployment rates were sufficient to explain the trajectory of consumer credit. However, in the current crisis, mortgage delinquencies are deteriorating more rapidly with significant regional dispersion. Serious mortgage delinquencies in the New York metro region were around 1.1% by April 2009 vs 30 day delinquencies at 9.9% of UPB in April 2020.  

STACR loan–level data shows that nationwide 30–day delinquencies increased from 0.8% to 4.2% nationwide. In this chart we track the performance and state of employment of 5 large metros (MSA). 

May Chart of the Month

Indicators included in our Chart of the Month: 

Change in unemployment is the BLS measure computed from unemployment claims. Traditionally this indicator has been used to measure economic health of a region. BLS reporting typically lags by months and weeks. 

Air quality index is a measure we calculate using level PM2.5 reported by EPA’s AirNow database on a daily basis. This metric is a proxy of increased vehicular traffic in different regions. Using a nationwide network of monitoring sites, EPA has developed ambient air quality trends for particle pollution, also called Particulate Matter (PM). We compute the index as daily level of PM2.5 vs the average of the last 5 years.  For regions that are still under a shutdown air quality index should be less than 100 (e.g. New York at 75% vs Houston at 105%) 

Air pollution from traffic has increased in regions where businesses have opened in May ’20 (e.g. LA went up from 69% in April to 98% in May).  However, consumer spending has not always increased at the same level.  We look to proxies for hourly employment levels. 

New Daily COVID-19 Cases: This is a health crisis and managing the rate of new COVID-19 cases will drive decisions to open or close businesses. The chart reports the monthly peak in new cases using daily data from Opportunity Insight 

Hourly Employment and Hours Worked at small businesses is provided by Opportunity Insight using data from Homebase. Homebase is a company that provides virtual scheduling and time-tracking tools, focused on small businesses in sectors such as retail, restaurant, and leisure/accommodation. The chart shows change in level of hourly employment as compared to January 2020. We expect this is to be a leading indicator of employment levels for this sector of consumers. 

Sources of data: 

Freddie Mac’s (STACR) transaction database 

Opportunity Insight’s Recovery Tracker 

Bureau of Labor and Statistics (BLS)’ MSA level economic reports 

Environment Protection Agency (EPA)’s AirNow database. 

Webinar: 2020 — Entering The Decade of Data & Smart Analytics


2020 — Entering The Decade of Data & Smart Analytics

Prepare for the decade where data and analytics become the driving force behind successful investment management

For the first time in decades, Structured Finance is poised to join the rest of the financial sector in adopting new tech solutions. Deal cycles are shrinking from 3 weeks to as little as 2 days. Consequently, the market’s demand for granular collateral data has never been stronger. Accuracy and consistency are paramount. The new decade promises major advances in technology around data supporting investment best practices. Will you be ready to adopt them? 

Join industry veterans and RiskSpan executives Bill Moretti, Suhrud Dagli, and Bernadette Kogler for our latest webinar, 2020: Entering The Decade of Data & Smart Analytics.

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Key Topics:

  • Automation – How can you join asset managers aggressively seeking yield through automation? 
  • Prioritization – Which new tech will have the biggest impact on your workflow? 
  • Fit – How will you fit into the new Structured Finance ecosystem for the next decade?
  • Resistance – What has prevented the industry from adopting new tech in past decades? What has changed?

About The Hosts

Bernadette Kogler


Bernadette is co-founder, board member, and the Chief Executive Officer of RiskSpan. She is also co-founder of SmartLink Lab, RiskSpan’s innovation lab developing blockchain applications in lending and structured finance. Bernadette is an entrepreneurial leader focused on leveraging emerging technology for the advancement of data analytics and business process in the lending and structured finance markets. She leads the company’s long-term vision and strategy bringing deep expertise across the entire lending lifecycle. She is a seasoned executive and has spent most of her career focused on analytics, risk management and technology applications that provide strategic advantage to clients. Bernadette was previously with KPMG’s Mortgage and Structured Finance Practice and started her career with Prudential Insurance Company. She holds a BS in Finance from Villanova University and an MBA from Seton Hall University.  

Suhrud Dagli


Suhrud is a co-founder, board member, and the Chief Technology and Innovation Officer of RiskSpan. He is also co-founder of SmartLink Lab, an innovation lab developing blockchain applications for structured finance and the capital markets. Suhrud is one of the industry’s most respected experts in mortgage and structured product technology and leads RiskSpan’s technology strategy including the company’s SaaS offering, open source and API strategies. He is also responsible for RiskSpan’s advanced technology incubations including machine learning applications and applications developed with blockchain. Suhrud has spent his career developing solutions for the capital markets. Formerly he was Head of System & Analytics at Greenwich Capital and Head of Analytics and Model Development for UBS. Suhrud holds an MS in Computer Science from Pennsylvania State University and a BS in Electrical Engineering from VJTI, Bombay India.  

Bill Moretti, CFA, CPA, FRM

Senior Managing Director

Bill Moretti has over 30 years of experience identifying business opportunities and developing creative investment strategies & solutions for the Structured Finance industry on both the buy- and sell-sides. Bill’s expertise covers all sectors within structured finance including Agency Residential Mortgage Backed Securities (RMBS), Non-Agency RMBS, Asset Backed Securities (ABS), Collateralized Loan Obligations (CLO’s), and Commercial Mortgage Backed Securities (CMBS). In his new role as a Senior Managing Director and Lead of the Innovation Lab with RiskSpan, Bill with be focused on applications of machine learning, AI and Blockchain to improve efficiencies in lending, structured finance and the investment process. Bill holds a Bachelors of Business Administration (BBA) from Pace University in New York, NY, as well as CPA, CFA, and FRM designations.

Webinar: Machine Learning in Building a Prepayment Model


Machine Learning in Building a Prepayment Model

Join RiskSpan financial model experts Janet Jozwik, Fan Zhang, and Lei Zhao to discuss how machine learning can help simplify prepayment models. They will discuss

  • Data:  Preprocessing the data and determining which variables are important to include in prepayment models
  • Modeling Approach:  Evaluating machine learning approaches
  • Model Performance: Opening the black box and tuning the model to improve performance

About The Hosts

Janet Jozwik

Managing Director – RiskSpan

Janet Jozwik helps manage quantitative modeling and data analysis groups at RiskSpan. Janet has a background in mortgage credit modeling, loss forecasting, and data analysis. Since joining RiskSpan, Janet has focused on loss forecasting and mortgage portfolio analytics for a key client as well as building a credit model using GSE loan-level data. Prior to joining RiskSpan, Janet was a financial economist at Fannie Mae where she specialized in single family credit pricing. Her work directly impacted the national guarantee fee pricing scheme and government programs to support the housing market during and after the financial crisis. Janet has extensive experience in analyzing massive datasets, a deep understanding of the drivers of credit risk, and an expertise in modeling mortgage cash flows. Janet holds an MBA from the University Of Chicago Booth School Of Business and a BA in Economics from Johns Hopkins University. 

Fan Zhang

Director of Model Development

Fan Zhang has 12 years of quantitative finance experience specializing in behavioral modeling, fixed income analysis and, machine learning. At RiskSpan, Fan leads the quantitative modeling team where he is currently driving improvements to prepay modeling and application of cutting edge machine learning methods. Fan was a senior quantitative manager at Capital One where he worked on prepayment, deposit, MSR, auto, interest rate term structure, and economic capital modeling. He was also a senior financial engineer at Fannie Mae managing a team to validate model implementation and risk analytics. Fan holds an MBA from the University of Maryland and a BA in Economics from the University of Michigan.

Lei Zhao

Quantitative Modeling Analyst

Lei Zhao is a key member of the quantitative modeling team at RiskSpan. Lei has done extensive research on clustering methodologies and his postdoctoral research paper has been cited over a hundred times in scholarly publications. Lei holds a Master of Science degree in Financial Engineering from University of California, Los Angeles, and a PhD in Mechanical Engineering from Zhejiang University, China. 

Webinar: Using Machine Learning in Whole Loan Data Prep


Using Machine Learning in Whole Loan Data Prep

Tackle one of your biggest obstacles: Curating and normalizing multiple, disparate data sets.

Learn from RiskSpan experts:

  • How to leverage machine learning to help streamline whole loan data prep
  • Innovative ways to manage the differences in large data sets
  • How to automate ‘the boring stuff’

About The Hosts

LC Yarnelle

Director – RiskSpan

LC Yarnelle is a Director with experience in financial modeling, business operations, requirements gathering and process design. At RiskSpan, LC has worked on model validation and business process improvement/documentation projects. He also led the development of one of RiskSpan’s software offerings, and has led multiple development projects for clients, utilizing both Waterfall and Agile frameworks.  Prior to RiskSpan, LC was as an analyst at NVR Mortgage in the secondary marketing group in Reston, VA, where he was responsible for daily pricing, as well as on-going process improvement activities.  Before a career move into finance, LC was the director of operations and a minority owner of a small business in Fort Wayne, IN. He holds a BA from Wittenberg University, as well as an MBA from Ohio State University. 

Matt Steele

Senior Analyst – RiskSpan

LC Yarnelle is a Director with experience in financial modeling, business operations, requirements gathering and process design. At RiskSpan, LC has worked on model validation and business process improvement/documentation projects. He also led the development of one of RiskSpan’s software offerings, and has led multiple development projects for clients, utilizing both Waterfall and Agile frameworks.  Prior to RiskSpan, LC was as an analyst at NVR Mortgage in the secondary marketing group in Reston, VA, where he was responsible for daily pricing, as well as on-going process improvement activities.  Before a career move into finance, LC was the director of operations and a minority owner of a small business in Fort Wayne, IN. He holds a BA from Wittenberg University, as well as an MBA from Ohio State University. 

eBook: Machine Learning in Modeling Loan Data


Machine Learning in Modeling Loan Data

Understanding the challenges of implementing a machine learning solution is critical to yielding leverageable results. In this three part eBook, we cover the fundamentals of machine learning, a use case with modeling loan data as well as how machine learning can be used for data visualization.

Open Source and Mortgage Data Modeling

white paper

Open Source and Mortgage Data Modeling

Open source software is growing in popularity, but incorporating it into your organization can be daunting. Professionals are eager to apply it to their mortgage data analysis processes and reap its benefits, but are wary of its risks.

In this white paper, we share:

  • The benefits of open source that make it so popular
  • The risks inherent in open source and how to mitigate them
  • The application of open source to mortgage data analysis, including real examples from our own work

Imputation and Analysis with Machine Learning

white paper

Imputation and Analysis with Machine Learning

Despite industry-wide efforts to incorporate robust quality control programs, challenges with mortgage data persist. Fortunately, combining machine learning with cloud computing shows promise in addressing mortgage data gaps and producing more accurate results than traditional approaches. 

This white paper introduces two methods to impute missing values and understand the relationships between various features of a residential loan database.

eBook: Machine Learning in Model Risk Management


Machine Learning in Model Risk Management

In this eBook, we first address some of the ways in which machine learning techniques can be leveraged by model validators to assess models developed using conventional means. We then tackle several considerations that model validators should take into account when independently assessing machine learning models that appear in their inventories. 

In this eBook, you’ll learn:

  • Real-world examples illustrating how machine learning models can be used to solve financial problems
  • Procedures for validating machine learning models
  • Machine learning methods that can be applied during model validation to understand and mitigate model risk




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