Linkedin    Twitter   Facebook

Get Started
Log In

Linkedin

Blog Archives

Modeling Credit and the Impact of COVID-19

Notwithstanding action taken at every level of government (including emergency measures taken by the Federal Reserve) to attempt to limit the economic fallout of the COVID-19 pandemic, markets remain highly volatileHow loans and structured credit are modeled needs to be modified (and quickly) to reflect the emerging expectations, moral hazard, and risks from the current crisis.  In the consumer finance market, existing models and data for the primary, secondary, and tertiary markets are strong, but these have a high probability of performing poorly as they’re based on historic data that doesn’t reflect the current crisis. To address this issue, RiskSpan has created a topdown framework that incorporates data from select historical events as well as a user-defined view of macro-economic forecasts. 

A Framework for Modeling Mortgage Credit in COVID-19

Compile data from past catastrophes 

The basis of our approach continues to be data-driven as historic events can serve as data points to inform analysis for the current crisis. Relevant catastrophes to look upon include: 

  • Natural disasters, including Hurricane Katrina and the impact on regional economies 
  • The Great Recession and its impact on certain borrowers 
  • The federal government’s response to the Great Recession 
  • The Great Depression  
  • Federal Reserve Board stress tests  

These events can inform some part of the modeling framework for key performance drivers, including: 1) unemployment and short-term delinquencies, 2) government relief programs, 3) default and foreclosure, and 4) home price changes and losses to investors. 

Unemployment and Mortgage Delinquencies  

The impact of unemployment on mortgage delinquencies will be severe. As the graph below showsthe relationship between unemployment and delinquencies is highly correlatednearly 1:1 relationship.  

UNEMPLOYMENT AND MORTGAGE DELINQUENCIES

We further expect that unemployment and subsequent delinquencies will correlate to regional, state, or business sector unemployment. Certain industries are more susceptible to COVID-19 related disruption stemming from the decline of consumer demand, state and federal orders, and international government actions that affect tourism. Further, statelevel executive orders and COVID-19 responses have been inconsistentsome state orders are more severe than others. This will lead to a corresponding impact at the state versus federal level and highlightthe importance of taking geo-specific macroeconomic factors into account 

Economic forecasts of unemployment related to the current crisis vary widely so we look to past levels to inform possible boundaries. During the Great Recessionnearly 9 million people lost their jobs within one year leading to an unemployment rate of 10%, according to the BLS. In contrastnew unemployment claims spiked on March 26, 2020, to 3.28 million according to the Labor Department, and then to an astounding 6.6 million today. These figures far exceed the previous high of 665,000 claims during the Great Recession. The Great Depression can also serve as a benchmark when unemployment peaked at 24.9% in 1933. This can be particularly relevant for certain geographies or industries. The Federal Reserve 2020 Severely Adverse Scenario, with unemployment peaking at 10% in 2021, suddenly looks more akin to a base or optimistic scenario.  

Government Relief and Delinquencies  

The national scope of COVID-19 is forcing governments and mortgage guarantors into nationwide mortgage forbearance, foreclosure moratoriums, and government relief programs. Regardless of type or reason, mortgage non-payment will result in a peak in delinquencies which may remain elevated through a forbearance period. 

We can look to natural disasters, like New Orleans and Puerto Rico hurricanes and the Houston floods, to find dramatic and immediate spikes in delinquencies. However, these natural disasters did not result in a corresponding spike in serious mortgages delinquencies or defaults. In these examples, forbearance and moratorium programs provided relief to borrowers until insurance companies paid claims

GOVERNMENT RELIEF AND DELINQUENCIES

Seriously Delinquent and Default  

As the table below shows, the Great Recession produced multiyear elevated delinquency and default rates with delinquencies spiking at near 10% in 2010. Peak default rates for some private investor programs exceeded 40%. 

SERIOUSLY DELINQUENT AND DEFAULT

However, mortgage defaults from the Great Recession included the impact of aggressively expanded underwriting with rampant and unfettered fraud in the form of subprime and NINA (No Income No Assets) mortgage programs. The historical trend of RiskSpan’s Vintage Quality Index reflects the degree to which underwriting guidelines have generally tightened and steadied over the past decade.  

Vintage Quality Index

Efforts to reduce default rates during the 2008 financial crisis were further hampered by initially slow government responses and uncoordinated efforts between investors and federal and state agencies. In the current crisis, we can expect government responses to COVID-19 to be immediate, aggressive, and coordinated. The U.S. federal government has already enacted relief legislation, recognizing that forbearance, loan modifications, and moratoriums are all proven tools to reduce mortgage delinquencies and severities. Unlike in 2008, however, the mortgages impacted by the current crisis are primarily federally insured and likely skewed towards lowincome and lowFICO borrowersBecause federally insured mortgages tend to find their way into Ginnie Mae MBS, emerging issues relating to advances on those securities may require new and unproven programs. 

  

Home Prices

The residential real estate market was strong prior to the pandemic. Home sales in February rose 6.5% to 5.77 million, according to the National Association of Realtors, and median home prices rose 8.0% year-over-year. As COVID-19 spreads, new sales activity is already coming to a halt, yet the impact on HPA is uncertain. A review of natural disasters, such as the Houston and Louisiana hurricanes, shows little negative longterm impact on HPA after the events. By comparison, the Great Recession produced nationwide declines that did not begin to rebound until 2012 (see below) 

HOME PRICES

Supporting the argument of a short-term impact on HPA are the recent strength of the U.S. economy and continued discipline in credit lending standards. Further, there is also strong generational demand for housing during a nationwide housing shortageArguments for a less optimistic view are based on the potential for a longer-thanexpected national economic shutdown and structural impacts to the economy, employment, and industries even after the pandemic ends.  

 

Summary 

The aphorism “things work until they don’t” is commonly used to explain financial markets and behaviorsThe COVID-19 crisis is simply the latest manifestation of this realityRisk managers and finance executives have to decide whether to rely on current models built on historical events and data – the models that got you here – or to start rethinking and retesting hypotheses and assumptions to manage and quantify new risks.   Email us at info@riskspan.com to talk about how RiskSpan can help and click here to see RS Edge in action.


Managing Operational and Credit Risk in Mortgage Servicing Portfolios During the COVID-19 Crisis

Tomorrow (April 1st) is the due date of the first significant wave of mortgage payments since the Coronavirus began disrupting the economy. The operational impact of COVID-19 on mortgage bankers—and servicers in particular—has been swift and dramatic. It will not soon subside. Its financial impact remains on the horizon but is likely to be felt over a more extended period. 

Whereas borrower inquiries related to the Coronavirus accounted for zero percent of servicer call volume as recently as March 16th, within a week they have spiked to more than 25 percent of inquiries at one servicer. Another servicer reported receiving over 20,000 calls relating to forbearance relief during the same period. 

We are officially in a new world. The next several months appear to hold chaos, disruption, and potentially devastating losses for mortgage servicers. When delinquencies associated with April 1st payments start to hit, the financial impact—felt primarily through P&I, T&I, and corporate advances, additional collection and compliance costs, and the loss of servicing fee income simply because fewer payments are being made—has the potential to linger considerably longer than the liquidity and funding crisis currently rocking financial markets.    

Having a roadmap for navigating impending financial, credit, and operational dilemmas has never been more important.   

Market dislocations created by the speed and seriousness of COVID-19 are constraining (and will continue to constrain) servicers’ tools for responding to and resolving a forthcoming tsunami of delinquencies, foreclosures and REOs. The ability of servicers to manage through this will be further complicated by external factors that will dictate when and how servicers will be able to manage their businesses. These are likely to include various forms of government intervention, such as payment holidays, mandatory forbearance, foreclosure moratoriums, and modification programs. While protecting borrowers, these programs will also add layers of complexity into servicer compliance operations. 

In addition to introducing new sets of moral hazard issues for the servicing of mortgages, increases in delinquencies and illiquidity of trading markets will seize the trading markets for servicing portfolios, limiting mortgage bankers’ access to cash. Investors, guarantors, and insurers will increase their oversight into servicer operations to minimize their losses.  

One Solution 

RiskSpan has been working with its mortgage banking clients to construct a modeling framework for assessing, quantifying, and managing COVID-19 risk to servicing operations and income statements. The framework covers the full lifecycle of a servicing asset and is designed to forecast each of the following under several defined stress scenarios: 

  • Principal and interest advances
  • Escrow (T&I) advances 
  • Corporate advances to cover foreclosure, liquidations and REO expenditures 
  • Financing and capital implications of delinquent and defaulted loans 
  • Repurchases, denials, and rescissions  
  • Compensatory fees and curtailments 

In addition to projecting these financial costs, the modeling framework forecasts the incremental operational costs associated with servicing a portfolio with increasing shares of delinquencies, defaults, bankruptcies, liquidations, and REOs—including all the incremental personnel, compliance and other costs associated with servicing a portfolio that was prime at acquisition but is suddenly beginning to take on subprime characteristics.  

Contact us to talk about how RiskSpan’s operational risk assessment tool can be customized to your servicing portfolio. 


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

webinar

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.

RiskSpan, Inc. needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at anytime. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our Privacy Policy. Your privacy is important to us and your information will be kept confidential.

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

CEO

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

CTO

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: Applying Model Validation Principles to Anti-Money Laundering Tools

webinar

Applying Model Validation Principles to Anti-Money Laundering Tools

This webinar will explore some of the more efficient ways we have encountered for applying model validation principles to AML tools, including:

  • Ensuring that the rationale supporting rules and thresholds is sufficiently documented 
  • Applying above-the-line and below-the-line testing to an effective benchmarking regime 
  • Assessing the relevance of rules that are seldom triggered or frequently overridden 


About The Hosts

Timothy Willis

Managing Director – RiskSpan

Timothy Willis is head of RiskSpan’s Governance and Controls Practice, with a particular focus on model risk management. He is an experienced engagement manager, financial model validator and mortgage industry analyst who regularly authors and oversees the delivery of technical reports tailored to executive management and regulatory audiences.

Tim has directed projects validating virtually every type of model used by banks. He has also developed business requirements and improved processes for commercial banks of all sizes, mortgage banks, mortgage servicers, Federal Home Loan Banks, rating agencies, Fannie Mae, Freddie Mac, and U.S. Government agencies.

Susan Devine, Cams, CPA

Senior Consultant – Third Pillar Consulting

Susan has more than twenty years of experience as an independent consultant providing business analysis, financial model validations, anti-money laundering reviews in compliance with the Bank Secrecy Act, and technical writing to government and commercial entities. Experience includes developing and documenting business processes, business requirements, security requirements, computer systems, networks, systems development lifecycle activities, and financial models. Experience related to business processes includes business process reviews, security plans in compliance with NIST and GISRA, Sarbanes Oxley compliance documents, Dodd-Frank Annual Stress Testing, functional and technical requirements for application development projects, policies, standards, and operating procedures for business and technology processes.

Chris Marsten

Financial and Data Analyst – RiskSpan

Chris is a financial and data analyst at RiskSpan where he develops automated analytics and reporting for client loan portfolios and provides data analysis in support of model validation projects. He also possesses extensive experience writing ETL code and automating manual processes. Prior to coming to RiskSpan, he developed and managed models for detecting money laundering and terrorist activity for Capital One Financial Corporation, where he also forecasted high-risk customer volumes and created an alert investigation tool for identifying suspicious customers and transactions.


Webinar: Basics of the Reference Rate Transition

webinar

Basics of the Reference Rate Transition

In June 2017, the ARRC announced the Secure Overnight Financing Rate (SOFR) as its recommended alternative rate, replacing LIBOR by the end of 2021.

Learn from RiskSpan experts Tom Pappalardo and Pat Greene the current industry standard for LIBOR, the possible challenges with SOFR, and how to mitigate your risk.


About The Hosts

Tom Pappalardo

Managing Director

Thomas Pappalardo is head of RiskSpan’s Data, Modeling and Analytics Consulting Practice and has 20+ years of broad experience in mortgage technology, finance and operations and retail banking industries. He is an experienced engagement manager, data and business requirements lead, business process and internal controls analyst and financial model validator. At RiskSpan, Tom has led multiple client engagements supporting the development of analytical applications, reengineering of business processes, validation of financial models and development of model risk management policies for the GSE’s (Fannie Mae, Freddie Mac, Federal Home Loan Banks), commercial banks, mortgage banks and non-bank servicers.

Patrick Greene

Senior Managing Director

Patrick Greene currently supports consulting and advisory services provided by RiskSpan for clients implementing securitization activities. In addition, he has delivered technology solutions and provided financial model validation support to multiple RiskSpan clients whose business practices rely on credit models, interest-rate models, prepayment models, income simulation models, counter-party risk models, whole loan valuation models, and bond redemption forecasting models. Pat is an experienced executive who has been responsible for the management of a leading asset securitization program for a national financial institution.


Webinar: Building and Running an Efficient Model Governance Program

webinar

Building and Running an Efficient Model Governance Program

Join RiskSpan Model Governance Expert Tim Willis for a webinar about running an efficient program. This webinar will cover essential elements of a model risk management policy including how to devise policies for open-source models and other applications not easily categorized. They’ll discuss best practices for building and maintaining a model inventory, tips for assigning appropriate risk ratings to models and determining validation frequency.


About The Host

Timothy Willis

Managing Director – RiskSpan

Timothy Willis is head of RiskSpan’s Governance and Controls Practice, with a particular focus on model risk management. He is an experienced engagement manager, financial model validator and mortgage industry analyst who regularly authors and oversees the delivery of technical reports tailored to executive management and regulatory audiences.


Webinar: CECL – The Requirements & Options For Credit Unions

webinar

CECL – The Requirements & Your Options For Credit Unions

In this webinar, learn from the new current expected credit losses methodology (CECL) experts, David Andrukonis, from RiskSpan, and Graham Dyer, from Grant Thornton about considerations specific to credit unions.

They will cover:

  • Accounting requirements and recent updates from the Financial Accounting Standards Board (FASB) Transition Resource Group
  • Proxy data options with specific data sources for each asset class 
  • Proxy data options with specific data sources for each asset class 


About The Hosts

Dave Andrukonis

Manager – RiskSpan

David Andrukonis has technical and managerial experience in banking, credit risk, and valuation. David leads the development of RiskSpan’s CECL Application, covering a variety of asset classes and model types. He has also led the development of specialized credit risk models such as structural credit risk models for shipping finance. He has performed non-traditional ABS valuations and validated a wide range of financial forecasting models, including models that estimate return on equity, asset/liability valuations under varying market interest rate scenarios, and loan losses.

Prior to joining RiskSpan, he managed the credit risk department at WashingtonFirst Bank, where he developed underwriting methodologies and stress tolerance models for diverse private firms and commercial real estate.

Graham Dyer

Partner – Grant Thornton, Member – FASB’S CECL Transition Resource Group (TRG)

Graham currently consults with Grant Thornton’s clients and audit teams regarding technical accounting and auditing matters, with a focus on issues impacting financial services entities.

His background includes the National Professional Standards Group at Grant Thornton and serving as a Professional Accounting Fellow in the Office of the Chief Accountant at the OCC. Graham is also a member of the FASB’s CECL Transition Resource Group (TRG) and the IASB’s IFRS 9 Impairment Transition Group (ITG).


Webinar: Credit Stress Testing Portfolio Exposure to COVID-19

webinar

Credit Stress Testing Portfolio Exposure to COVID-19

Learn how experienced portfolio managers apply stress scenarios to unprecedented events.

Very few models are built to contemplate the impact of a 20-percent unemployment rate. And those that are don’t have enough data to be trustworthy. 

Building, selecting, and applying appropriate stress scenarios to a portfolio is challenging under the best of circumstances. It becomes even more perilous when people begin applying superlatives like unprecedented, unparalleled, and uncharted. Are we in 2008 again? 2001? The Great Depression? Some combination of all three? No one can really know for sure. 

And so what is a portfolio manager to do? 

Hear Bill Moretti, Faith Schwartz, Scott Carnahan, Amy Crews Cutts, and Bernadette Kogler as they discuss “Stress Testing Portfolio Exposure to COVID-19.” 

Key Topics:  

  • General principles for assessing portfolio risk during a crisis 
  • Identifying an appropriate set of stress scenarios 
  • Concentration and sector-specific risks 


About The Hosts

Bill Moretti

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. Bill’s expertise covers all sectors within structured finance including RMBS, Non-Agency RMBS, ABS, CLOs, and CMBS. He is now director and Lead of the SmartLink Innovation Lab.

Amy Crews Cutts

President, AC Cutts and Associates

Amy is President of AC Cutts and Associates, which advises clients on economic trends, public policy, and strategy relating to consumer credit, housing policy, auto lending and mortgage markets. She was formerly Senior Vice President at Equifax, where she was responsible for analytics and research relating micro and macro factors affecting the consumer. Amy has been widely published and quoted both during her time in academia and the private sector.

Faith Schwartz

President, Housing Finace Strategies

Faith Schwartz is the President of Housing Finance Strategies, a strategic advisory services firm. She is active on many industry boards and is known for having developed and led the HOPE NOW Alliance to unite the industry throughout the housing crisis.

Scott Carnahan

Senior Managing Director, FTI Consulting

Scott Carnahan is currently a Senior Managing Director in the Forensic & Litigation Consulting segment at FTI Consulting in Los Angeles. Scott has held leadership positions with Impac Mortgage and KPMG’s accounting, audit, and advisory practice.

Bernadette Kogler

CEO

Bernadette is co-founder, board member, and CEO of RiskSpan. Bernadette is focused on leveraging emerging technology for the advancement of data analytics and business process in the lending and structured finance markets. She is a seasoned executive and has spent most of her career focused on analytics, risk management and technology applications. Bernadette was previously with KPMG’s Mortgage and Structured Finance Practice and started her career with Prudential Insurance Company.


Webinar: How Peers are Tackling CECL for Held-to-Maturity Securities

webinar

How Peers are Tackling CECL for Held-to-Maturity Securities

Join experts from RiskSpan and Grant Thornton to learn about the new current expected credit loss standard (CECL) and it’s implications for held-to-maturity securities.

In this webinar, they will:

  • Identify defining major classes in the debt securities universe including structured-finance, corporate bonds and MUNI bonds.
  • Introduce CECL approaches for these classes, looking at both advanced and simpler approaches
  • Apply the general CECL model to debt securities and look at the impact on pooling and zero credit losses


About The Hosts

Dave Andrukonis

Director – RiskSpan

David Andrukonis, CFA leads RiskSpan’s banking line of business, which helps lending institutions efficiently measure, optimize, and report the risk in their portfolios. Formerly, David managed the credit risk analyst group at WashingtonFirst Bank, covering CRE, construction, C&I and residential portfolios. David has published three technical papers in the RMA Journal and is a CFA Charterholder.

Graham Dyer

Partner – Grant Thornton, Member – FASB’S CECL Transition Resource Group (TRG)

Graham Dyer currently consults with Grant Thornton’s clients and audit teams regarding technical accounting and auditing matters, with a focus on issues impacting financial services entities. His background includes the National Professional Standards Group at Grant Thornton and serving as a Professional Accounting Fellow in the Office of the Chief Accountant at the OCC. Graham is also a member of the FASB’s CECL Transition Resource Group (TRG) and the IASB’s IFRS 9 Impairment Transition Group (ITG).

Varum Agaewal

Director, Strategic Risk and Operations Practice, Financial Services

Varun Agarwal provides advisory services to Banking and Capital Markets clients in risk and regulatory compliance management space in the areas of Enterprise Risk, Credit Risk, Market Risk, Liquidity Risk, Operational Risk and Model Risk management services along with Risk Governance, Risk Data Management and Reporting services.


Webinar: Machine Learning in Building a Prepayment Model

webinar

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. 


Get Started
Log in

Linkedin   

risktech2024