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Enhancing a HELOC Lender’s Operations with RiskSpan’s Data as a Service (DaaS)

A leading fintech company specializing in home equity lines of credit (HELOCs), was seeking to optimize the management of its data operations. To accomplish this, the company turned to RiskSpan, a leader in data analytics and financial technology solutions. Through a tailored Data as a Service (DaaS) offering, RiskSpan helped the company improve its HELOC business operations by providing advanced data management and modeling capabilities.

Challenges

The company sought to enhance its HELOC operations in two critical areas:

  1. Data Management and Integration: The company was dealing with complex data sets from multiple sources, including credit bureaus, property data, and customer behavior insights. Integrating and managing this data effectively was crucial for making informed lending decisions.
  2. Risk Assessment and Modeling: Accurate and reliable risk assessment models were necessary for evaluating customer behavior and predicting loan performance. The company required a solution that could model draw behavior and other variables specific to HELOCs.

RiskSpan’s DaaS Solution

RiskSpan’s DaaS offering provided the company with a comprehensive solution tailored to address these challenges. The key components of the solution included:

  1. Advanced Data Integration: RiskSpan’s DaaS platform seamlessly integrated the company’s various data sources, enabling a more streamlined and efficient data management process. This integration allowed the company to better understand their borrowers and make more informed lending decisions.
  2. Enhanced Loan-Level HELOC Pricing and Projections: The client successfully loaded its historical loan performance data onto RiskSpan’s DaaS platform and established a monthly process within the platform’s flexible data warehouse. Using the embedded historical performance tool, the client analyzed loan-level behavior across its portfolio. This enabled the client to generate detailed collateral performance reports for investors and rating agencies, as well as leverage these insights to enhance future projections and loan-level pricing for new loans.
  3. Cost-Effective Data Services: RiskSpan also identified an opportunity to replace the client’s existing data services provider at a significantly reduced cost. By offering a more competitive pricing structure while maintaining high-quality data services, RiskSpan positioned the client to achieve substantial cost savings, making them more competitive in the HELOC market.

Outcomes and Benefits

Implementing RiskSpan’s DaaS solution brought several key benefits:

  • Improved Decision-Making: With better-integrated data and more accurate modeling of HELOC draw behavior, the client could make more informed lending decisions, ultimately reducing risk and enhancing profitability.
  • Operational Efficiency: The streamlined data management process allowed the client to operate more efficiently, freeing up resources to focus on core business activities.
  • Cost Savings: RiskSpan’s competitive pricing enabled the client to cut costs significantly, improving their bottom line and allowing them to reinvest in other areas of the business.

RiskSpan’s Data as a Service solution provided the clients with the tools it needed to optimize its HELOC business. By addressing its data integration challenges, improving risk assessment through advanced modeling, and offering a cost-effective alternative to existing data services, RiskSpan helped the client strengthen its market position and enhance overall business performance.

Preparing For Impact: How Will Non-QM Prepay Speeds React to Lower Rates?

In a recent post, we addressed some of the less obvious ways in which a lower interest rate environment is likely to impact an agency universe with such a large volume of loans that are still out-of-the-money to refinance. In this post, we turn our attention to non-QM loans, whose unique characteristics mean they will likely feel the coming rate cuts differently.

Understanding the Distinctive Prepayment Dynamics of Non-QM Loans

Non-QM loans cater to borrowers who do not meet the stringent criteria of traditional agency loans, often due to factors like non-standard income documentation, credit issues, or investment property financing. Non-QM loans generally carry higher interest rates, and, unlike their agency counterparts, many have prepayment penalties designed to protect lenders from early payoff risk. Non-QM loans are also more likely than agency loans to involve investment properties – and thus, the underlying mortgages are not subject to the same “ability to repay” constraints that apply to agency/QM loans.

All these factors play a role in forecasting prepay speeds.

As rates decline, the incentive for some non-QM borrowers to refinance should increase, but several unique factors will shape the extent to which borrowers respond to this incentive:

  1. Prepayment Penalties: Many non-QM loans, especially those structured as Debt Service Coverage Ratio (DSCR) loans for investment properties, include prepayment penalties that can deter refinancing despite a favorable rate environment. These penalties vary widely, from a fixed percentage over a set period to declining penalties over time. The economic calculus for borrowers will hinge on whether the potential savings from refinancing outweigh these penalties
  2. Diverse Loan Structures: The non-QM market includes a variety of loan products, such as 40-year terms, hybrid ARMs and loans with interest-only periods, reminiscent of the pre-2008 lending landscape. This diversity means that not all non-QM loans will see the same incentive to refinance and the slope of the mortgage curve will matter. For example, loans with higher rates are likely to exhibit a stronger refinance response, particularly as the shape of the mortgage rate curve plays a significant role, with hybrid ARMs resetting off short-term rates and 30-year fixed-rate mortgages being influenced by movements in the 10-year Treasury yield
  3. Interest Rate Spread Compression: Historically, the spread between non-QM and agency mortgage rates has varied significantly, ranging from 100 to 300 basis points. A narrowing of this spread, driven by falling rates, could heighten the refinance incentive for non-QM borrowers, leading to faster prepayment speeds. However, the extent of this spread compression is uncertain and will depend on broader market dynamics. Souring economic conditions, for example, would likely contribute to a widening of spreads.

Key Factors Influencing Non-QM Prepayment Speeds

Loan Characteristics and Documentation Types

Non-QM loans can vary significantly by documentation type, such as full documentation, bank statements, or DSCR. Historically, as illustrated in the following chart, full documentation loans have shown faster prepayment speeds, because these borrowers are closer to qualifying for agency refinancing options as rates drop.

S-Curves by Doc Type (Full vs. Alt. vs. Bank Statement vs. DSCR)

Unlike agency mortgages, which include a substantial volume of loans originated at much lower rates, the non-QM market predominantly consists of loans originated in the past few years when rates were already elevated. As a result, a larger portion of non-QM loans is closer to being “in the money” for refinancing. This distinction suggests that the non-QM sector may see a more pronounced increase in prepayment activity compared to agency loans, where the lock-in effect remains stronger.

S-Curve (line) vs UPB (bars) by Refi Incentive

Economic Sensitivity to Rate Moves

For many non-QM borrowers, the primary barrier to agency loan qualification—whether credit score, income documentation, or property type—remains static despite lower rates. Thus, while a rate cut could improve the appeal of refinancing into another non-QM product, it might not significantly shift these borrowers towards agency loans. However, as noted, those closer to the threshold of agency eligibility could still be enticed to refinance if the rate spread and penalty structures align favorably.

Conclusion

The coming interest rate cuts are poised to influence the non-QM market in unique ways, with prepayment speeds likely to increase as borrowers seek to capitalize on lower rates. However, the interplay of rate spreads, prepayment penalties, and diverse loan structures will create a complex landscape where not all non-QM loans will behave uniformly. For lenders and investors, understanding these nuances is crucial to accurately forecasting prepayment risk and managing portfolios in a changing rate environment.

As the market evolves, ongoing analysis and model updates will be essential to capturing the shifting dynamics within the non-QM space, ensuring that investors and traders are well-prepared for the impacts of the anticipated rate cuts. Contact us to learn how RiskSpan’s Edge Platform is helping a growing number of non-QM investors get loan-level insights like never before.

RiskSpan Expands Private Credit Solution to Include Residential Transition Loans

Arlington, VA – July 18, 2024 – RiskSpan, a leading technology provider of innovative risk management and data analytics for securities, loans and private credit, today announced the addition of Residential Transition Loans, to its award-winning Edge Platform. This enhancement enables loan and private credit investors to seamlessly upload, model, and analyze cash flow projections for fix/flip, ground-up construction, bridge and other loans with distinctive RTL features, further solidifying RiskSpan’s commitment to delivering comprehensive and versatile solutions to the private credit market.

The integration of RTLs into the Edge Platform offers investors  an unprecedented level of flexibility and precision in managing and evaluating complex loan portfolios. The new capability permits lenders to model several loan features characteristic of RTLs, including:

  • Draw Schedules on Undisbursed Loan Amounts: Investors can now account for staggered disbursement schedules, allowing for detailed modeling of cash flows based on actual loan drawdown patterns.
  • Extended Maturity Dates and Extension fees: The Platform accommodates assumptions around extension of maturity dates, ensuring investors and lenders can extend terms as necessary and model the impact on cash flows.
  • Interest-Only Contract Terms: The Platform supports loans with interest-only payment structures, providing the ability to model and project cash flows accurately.
  • “Dutch” Loan Features: RiskSpan now supports loans where interest is charged on both disbursed and undisbursed loan amounts, offering a comprehensive view of interest accruals and cash flow projections.

“By adding RTLs to the Platform, we are providing loan and private credit investors with powerful tools to navigate the complexities of these unique loan products,” said Bernadette Kogler, CEO of RiskSpan. “This enhancement aligns with our mission to equip our clients with the most advanced and flexible solutions for managing and analyzing their loan portfolios.”

These new capabilities are designed to meet the evolving needs specifically of loan and private credit investors, offering a seamless integration process and user-friendly interface. This latest addition underscores RiskSpan’s dedication to continuous innovation in this market.

For more information about RiskSpan’s Edge Platform and the new RTL functionality, please visit RiskSpan.com.

About RiskSpan

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions.  Learn more at www.riskspan.com.

MSR Tape to Bid in 6 Easy Steps

Creating an MSR bid using RiskSpan’s Edge Platform is designed to be easy.

How easy?

So easy that we challenged a user to create a storylane illustrating how to get from uploading a tape to generating a price in the fewest steps possible.

She was able to get to a bid in just six easy steps!

  1. Upload the CSV file
  2. Click once to map the necessary fields using the Platform’s AI-powered Smart Mapper
  3. Click again to view the transformed and fully mapped loan-level data
  4. Select a segmentation level (loan-level, aggregate, or somewhere in-between)
  5. Select the appropriate anchor, prepay, credit, loan model and MSR inputs
  6. Click run and get your bid. (If you don’t mind more than six steps, you can iterate your inputs and model assumptions through the Platform’s easy-to-use Scenario Library module.)

How is this possible? Ultimately, it boils down to using a platform that was purpose-built to facilitate the process. RiskSpan’s platform boasts:

  1. User-Friendly Interface: The Edge Platform features an intuitive interface that allows users to navigate through different modules and functions with ease. The design focuses on minimizing the learning curve for new users.
  2. Data Integration: The platform integrates seamlessly with various data sources, allowing users to import the necessary data quickly. This integration supports the efficient preparation and analysis of MSR bids.
  3. Automated Processes: Edge offers automation for several steps in the bid creation process. This includes automated data validation, pricing models, and risk assessment tools, which help streamline the workflow.
  4. Advanced Analytics: The platform provides powerful analytics and modeling tools to assess the value and risk of MSRs accurately. Users can leverage these tools to generate insights and make informed decisions.
  5. Collaboration Tools: Edge facilitates collaboration among team members, enabling multiple users to work on a bid simultaneously. This collaborative approach enhances efficiency and ensures all relevant expertise is applied to the bid.
  6. Support and Resources: RiskSpan offers comprehensive support and resources, including tutorials, documentation, and customer service, to help users navigate the platform and utilize its features effectively.
  7. Customization Options: Users can customize the platform to fit their specific needs, including setting up custom workflows, reports, and analytics. This flexibility ensures that the platform can adapt to different bidding strategies and requirements.
  8. Security and Compliance: The Edge Platform is built with robust security measures to protect sensitive data and ensure compliance with industry standards and regulations.

Contact us to try it yourself and see how easy it is to go from a CSV file of loans to a preliminary MSR bid in just minutes.

How RiskSpan and Snowflake Helped a Large Insurance Company Revolutionize Its Data Management

Background

Asset managers are increasingly turning to Snowflake’s cloud infrastructure to address the limitations of outdated databases. Migrating to Snowflake grants them access to a sustainable and secure platform that enables efficient data storage, processing, and analytics. This transition empowers asset managers to streamline operations, improve data accessibility, and reduce costs associated with maintaining on-premises infrastructure.

Client Challenge

A large insurance company’s asset management team was seeking to improve its approach to data management in response to its increasingly complex investment portfolio. The company recognized that transitioning to Snowflake would serve as a foundation for sustainable data analysis for years to come.

Desiring a partner to assist with the transition, the life insurer turned to RiskSpan – a preferred Snowflake partner with substantial experience in database architecture and management.

Specifically, the insurance company sought to achieve the following:

Systems Consolidation: Data stored across multiple transactional systems had contributed to data fragmentation and inefficiencies in data retrieval and analysis. The client sought to establish and maintain a consistent source of asset data for enterprise consumption and reporting.

Improved Reporting Capabilities: Quantifying full risk exposures in fast-moving situations proved challenging, leaving the institution vulnerable to unforeseen market fluctuations. Consequently, the client sought to improve its asset evaluation and risk assessment process by incorporating comprehensive look-through data and classification information. The need for various hierarchical classifications further complicated data access and reporting processes which required streamlining the process of producing ad-hoc exposure reports, which often required several weeks and involved teams of people.

Reduction of Manual Processes: The client needed more automated data extraction processes in order to create exposure reports across different asset classes in a more time-efficient manner with less risk of human error. 

Reduction of Infrastructure Constraints: On-premise infrastructure had defined capacity limitations, hindering scalability and agility in data processing and analysis.

RiskSpan’s Approach and Solutions Implemented

Collaborative Partnership: RiskSpan worked closely with the client’s IT, risk management, and analytics teams throughout the project lifecycle, fostering collaboration and ensuring alignment with organizational goals and objectives.

Comprehensive Assessment: Together, we conducted a thorough assessment of the client’s existing data infrastructure, analytics capabilities, and business requirements to identify pain points and opportunities for improvement.

Strategic Planning: Based on the assessment findings, the collective team developed a strategic roadmap outlining the migration plan to the unified data platform, encompassing asset data consolidation, portfolio analytics enhancement, and reporting automation.

Unified Data Platform: Leveraging modern technologies, including cloud-based solutions and advanced analytics tools, RiskSpan orchestrated the integration of various data sources and analytics capabilities. Together, we consolidated asset data from various transactional systems into a unified data platform, providing a single source of truth for comprehensive asset evaluation and risk assessment.

Data Lineage Tracking: The team employed dbt Labs tools to build, validate, and deploy flexible reporting solutions from the Snowflake cloud infrastructure.  This enabled the tracking of data lineage, adjustments, and ownership.

Daily Exposure Reporting: Leveraging automated analytic pipelines, we enabled real-time generation of exposure reports across different asset classes, enhancing the client’s ability to make timely and informed decisions.

Automated Data Extraction: We automated the data extraction processes, reducing manual intervention and streamlining data retrieval, cleansing, and transformation workflows.

Hierarchical Classification Framework: We implemented a hierarchical classification framework, providing standardized and consistent data hierarchies for improved data access and reporting capabilities.

Transformative Outcomes

Enhanced Decision-making: Implementing advanced analytics capabilities and exposure reporting empowered our client to make informed decisions more quickly, mitigating risks and capitalizing on market opportunities.

Operational Efficiency: Automation of data extraction, analytics modeling, and reporting processes resulted in significant operational efficiencies, reducing time-to-insight and enabling resource reallocation to strategic initiatives.

Scalability and Agility: The migration to a cloud-based infrastructure provides scalability and agility, allowing our client to adapt quickly to changing business needs and accommodate future growth without infrastructure constraints.

Data Governance and Compliance: The implementation of standardized hierarchical classifications strengthened data governance and compliance, ensuring data consistency, integrity, and regulatory adherence. By leveraging Snowflake’s scalable architecture and advanced features, this large asset manager is now positioned to maneuver both its current and future data landscapes. The implementation of Snowflake not only streamlined data management processes but also empowered the organization to extract valuable insights with unprecedented efficiency. As a result, the asset manager can make data-driven decisions confidently, enhance operational agility, and drive sustainable growth in a rapidly evolving market landscape.

RiskSpan Launches MBS Loan Level Historical Data on Snowflake Marketplace

ARLINGTON, Va., June 18, 2024 – RiskSpan, a leading provider of data analytics and risk management solutions for the mortgage industry, announced today that it has launched MBS Loan Level Historical Data on Snowflake Marketplace. RiskSpan’s MBS Loan Level Historical Data on Snowflake Marketplace enables joint customers to access RiskSpan’s normalized and enriched loan-level data for Fannie Mae, Freddie Mac, and Ginnie Mae mortgage-backed securities.

“We are thrilled to join the Snowflake Marketplace and offer our loan-level MBS data to a wider audience of Snowflake users,” said Janet Jozwik, Senior Managing Director at RiskSpan. “This is a first step in what we believe will ultimately become a cloud-based analytical hub for MBS investors everywhere.”

RiskSpan and Snowflake, the AI Data Cloud company, are working together to help joint customers inform business decisions and drive innovations by enabling them to query the data using SQL, join it with other data sources, and scale up or down as needed. RiskSpan also provides sample code and calculations to help users get started with common metrics such as CPR, aging curves, and S-curves.

“RiskSpan’s launch of a unique blend of enriched data onto Snowflake Marketplace represents a major opportunity for Snowflake customers to unlock new value through data on their business journey,” said Kieran Kennedy, Head of Marketplace at Snowflake. “We welcome RiskSpan to the ecosystem and look forward to exploring how we can support our customers as they look to leverage the breadth of the Snowflake platform more effectively.”

Joint customers can now leverage Loan-Level MBS Data on Snowflake Marketplace, allowing them to access RiskSpan data enhancements, including servicer normalization, refinements, mark-to-market LTV calculations, current coupon. These and other enhancements make it easier and faster for users to perform analysis and modeling.

Snowflake Marketplace is powered by Snowflake’s ground-breaking cross-cloud technology, Snowgrid, allowing companies direct access to raw data products and the ability to leverage data, data services, and applications quickly, securely, and cost-effectively. Snowflake Marketplace simplifies discovery, access, and the commercialization of data products, enabling companies to unlock entirely new revenue streams and extended insights across the AI Data Cloud. To learn more about Snowflake Marketplace and how to find, try and buy the data, data services, and applications needed for innovative business solutions, click here.

About RiskSpan, Inc. 

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions. Learn more at www.riskspan.com.

The newest, fastest and easiest way to access and analyze Agency MBS data

TL;DR Summary of Benefits

  • Data normalization and enhancement: RiskSpan’s MBS data on Snowflake normalizes Fannie, Freddie, and Ginnie loan-level data, consolidating everything into one set of field names. It also offers enhanced loan level-data fields, including current coupon, spec pool category, and mark-to-market LTV, which are not available in the raw data from the agencies. The data also includes pool-level factors like pool prefix and pool age, as well as full loan histories not available from the GSEs directly.
  • Data access and querying: Users access the data in Snowflake using SQL or Python connectors. Snowflake functions essentially as a cloud SQL server that allows for instantaneous data sharing across entities. In just a few clicks, users can start analyzing MBS data using their preferred coding language—no data, ETL, or IT Teams required.
  • Data merging and analytics: Users can merge the data in Snowflake with other available loan level or macroeconomic data, including interest rates, home prices, and unemployment, for advanced analytics. Users can also project performance, monitor portfolios, and create spec pools, among other features.
CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

The Problem

Even though Fannie, Freddie and Ginnie have been making MBS performance data publicly available for years, working with the raw data can be challenging for traders and back-office analysts.

Traders and analysts already have many of the tools they need to write powerful queries that can reveal hidden patterns and insights across different markets – patterns that can reveal lucrative trading opportunities based on prepayment analysis. But one big obstacle often stands in the way of getting the most out of these tools: the data from the agencies is large and unwieldy and is not formatted in a consistent way, making it hard to compare and combine.

What’s more, the Agencies do not maintain full history of published data on the websites for download. Only recent history is available.

The Solution: RiskSpan’s new MBS loan-level historical offering on Snowflake Marketplace

Using RiskSpan’s new MBS Loan-Level Historical Data Offering, MBS traders and analysts can now leverage the power of Snowflake, the leading cloud data platform, to perform complex queries and merge data from multiple sources like never before.

This comprehensive data offering provides a fully normalized view of the entire history of loan-level performance data across Agencies – allowing users to interact with the full $9T Agency MBS market in unprecedented ways.

A list of normalized Fannie and Freddie fields can be found at the end of this post.

In addition to being able to easily compare different segments of the market using a single set of standardized data fields, MBS traders and analysts also benefit from derived and enhanced data, such as current coupon, refinance incentive, current loan-to-value ratio, original specified pool designation, and normalized seller and servicer names.

The use cases are practically limitless.

MBS traders and analystscan track historical prepayment speeds, find trading opportunities that offer relative value, and build, improve, or calibrate prepayment models. They can see how prepayment rates vary by loan size, credit score, geographic location, or other factors. They can also identify pools that have faster or slower prepayments than expected and exploit the differences in price.

Loan originators can see how their loans perform compared to similar loans issued by other originators, servicers, or agencies, allowing them to showcase their ability to originate high-quality loans that command premium pricing.

Enhanced fields provide users with more comprehensive insights and analysis capabilities. They include a range of derived and enhanced data attributes beyond the standard dataset: derived fields useful for calculations, additional macroeconomic data, and normalized field names and enumerations. These fields give users the flexibility to customize their analyses by incorporating additional data elements tailored to their specific needs or research objectives.

Enhanced loan-level fields include:

  • Refi Incentive: The extent to which a borrower’s interest rate exceeds current prevailing market rates
  • Spread at Origination (SATO): a representation of the total opportunities for refinancing within a mortgage servicing portfolio. SATO encompasses all potential refinance candidates based on prevailing market conditions, borrower eligibility, and loan characteristics
  • Servicer Normalization: A standardization of servicer names to ensure consistency and accuracy in reporting and analysis
  • Scheduled Balance: A helper field necessary to easily calculate CPR and other performance metrics
  • Spec Pool Type: A designation of the type of spec story on the loan’s pool at origination
  • Current LTV: a walked forward LTV based on FHFA’s HPI and the current balance of the loan

Not available in the raw data from the agencies, these fields allow MBS traders and analysts to seamlessly project loan and pool performance, monitor portfolios, create and evaluate spec pools, and more.

Access the Data on Your Terms

Traders and analysts can access the data in Snowflake using SQL or Python connectors. Alternatively, they can also access the data through the Edge UI, our well-established product for ad hoc querying and visualization. RiskSpan’s Snowflake listing provides sample queries and a data dictionary for reference. Data can be merged with macroeconomic data from other sources – rates, HPI data, unemployment – for deeper insights and analytics.

The listing is available for a 15-day free trial and can be purchased on a monthly or annual basis. Users don’t need to have a Snowflake account to try it out. Learn more and get started at the Snowflake Marketplace or contact us to schedule a demo or discussion.

CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

Fannie/Freddie Normalized Fields

NAMETYPEDESCRIPTION
AGENumberLoan Age in Months
AGENCYVarcharFN [Fannie Mae], FH [Freddie Mac]
ALTDQRESOLUTIONVarcharPayment deferral type: CovidPaymentDeferral,DisasterPaymentDeferral,PaymentDeferral,Other/NA
BORROWERASSISTPLANVarcharType of Assistance: Forbearance, Repayment, TrialPeriod, OtherWorkOut, NoWorkOut, NotApplicable, NotAvailable
BUSINESSDAYSNumberBusiness Day in Factor Period
COMBINEDLTVFloatOriginal Combined LTV
CONTRIBUTIONFloatContribution of Loan to the Pool, to be used to correctly attribution Freddie Mirror Pools
COUPONFloatNet Coupon or NWAC in %
CURRBALANCEFloatCurrent Balance Amount
CURRENTCOUPONFloatPrimary rate in the market (PMMS)
CURRENTLTVFloatCurrent Loan to Value Ratio based on rolled-forward home value calculated by RiskSpan based on FHFA All-Transaction data
CURTAILAMOUNTFloatDollar amount curtailed in the period
DEFERRALAMOUNTFloatDollar amount deferred
DQSTRINGVarcharDelinquency History String, left most field in the current period
DTIFloatDebt to Income Ratio %
FACTORDATEDatePerformance Period
FICONumberBorrower FICO Score [300,850]
FIRSTTIMEBUYERVarcharFirst time home buyer flag Y,N,NA
ISSUEDATEDateLoan Origination Date
LOANPURPOSEVarcharLoan Purpose: REFI,PURCHASE,NA
LTVFloatOriginal Loan to Value Ratio in %
MATURITYDATEDateLoan Maturity Date
MICOVERAGEFloatMortgage Insurance Coverage %
MOSDELINQVarcharDelinquency Status: Current, DQ_30_Day, DQ_60_Day, DQ_90_Day, DQ_120_Day, DQ_150_Day, DQ_180_Day, DQ_210_Day, DQ_240_Day, DQ_270_Day, DQ_300_Day, DQ_330_Day, DQ_360_Day, DQ_390_Day, DQ_420_Day, DQ_450_Day, DQ_480_Day, DQ_510_Day, DQ_540_Day, DQ_570_Day, DQ_600_Day, DQ_630_Day, DQ_660_Day, DQ_690_Day, DQ_720pls_Day
MSAVarcharMetropolitian Statistical Area
NUMBEROFBORROWERSNumberNumber of Borrowers
NUMBEROFUNITSVarcharNumber of Units
OCCUPANCYTYPEVarcharOccupancy Type: NA,INVESTOR,OWNER,SECOND
ORIGBALANCEFloatOriginal Loan Balance
ORIGSPECPOOLTYPEVarcharSpec Story of the pool that the loan is a part of. Please see Spec Pool Logic in our linked documentation
PERCENTDEFERRALFloatPercentage of the loan balance that is deferred
PIWVarcharProperty Inspection Waiver Type: Appraisal,Waiver,OnsiteDataCollection, GSETargetedRefi, Other,NotAvailable
POOLAGENumberAge of the Pool
POOLIDVarcharPool ID

Transforming Loan Data Management Using Snowflake Secure Data Sharing

Presenters

Paul Gross

Senior Quantitative Analyst, Rithm Capital

Michael Cowley

Principal, Data Cloud Products, Snowflake

Bernadette Kogler

CEO, RiskSpan

Suhrud Dagli

CTO, RiskSpan

Wednesday, May 29th, 2024

1:00 ET

Hear from a distinguished panel including RiskSpan and Snowflake customers as they describe how Data Share has transformed their approach to mortgage investment. Specific topics to include:

  • High-speed data processing using Snowflake for easy delivery of risk analytics and diligence data
  • How Snowflake’s Data Sharing facilitates data access across and between organizations while maximizing computational performance and flexibility 
  • How Snowflake protects client data
  • The unique value of a central hub for all mortgage industry data and never having to FTP a file again

watch recording

Karthika Mani

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Our Feature Spotlight
Karthika Mani
Senior QA Lead

How long have you been with RiskSpan?
13 years
What does your role at RiskSpan entail?

Senior QA Lead. I work closely with the development team, and responsible for validating new feature enhancements, software upgrades, and bug fixes to ensure the highest quality standards are maintained across various development projects, mainly EDGE and PROSUP. Additionally, I take on the responsibility of delivering error-free client logins for new clients, ensuring a seamless onboarding experience. My role extends beyond traditional testing activities as I actively engage in testing new servers, environments, and APIs. Furthermore, I am documenting the modules and functionalities of EDGE, ensuring comprehensive coverage and clarity regarding the platform’s capabilities. I am responsible for coordinating the quarterly SOC process reports for Securities and Loans Allowance modules contributing to the overall security and compliance efforts of the organization. I also engage in other supporting tasks like interviews etc.

What 3 words best describe RiskSpan?
Collaborative, Progressive and Trustworthy
What are your favorite hobbies?
Music and Trekking/Hiking.
What is one thing on your Bucket List? 
Mount Kailash and Amsterdam.
What is your go-to or favorite snack? 
Buttered Corn and Bitter Gourd Chips.
What do you like about our company culture?
I like our company’s teamwork efforts, transparency, and upholding values among colleagues from diverse backgrounds. And the supportive work environment helps us with a healthy work-life balance, allowing us to make significant contribution towards our work.
Which company values resonate with you the most?
Transparency and Passion. I am deeply passionate and transparent about the work I do and I believe that it is essential for engaging and excellence. I felt my suggestions are always considered.
Describe how you’ve grown professionally since you started working for us?
Since joining Riskspan as a fresher, I got the opportunity to validate different projects spanning from Velocity till Edge which contributed to my growth in testing and quality assurance. I have expanded my technical skills through hands-on experience with various tools and methodologies. My understanding on the mortgage industry has deepened, and the validation process for structured products has helped me with insights to develop more strategic test scenarios according to the client needs. I have also gained knowledge of industry specific practices. I’ve been fortunate to have supportive seniors who’ve guided me along the way, and I’ve embraced the opportunity to pay it forward by mentoring juniors thereby promoting growth within the team.

Zeren Zhang

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zeren-zhang

Our Feature Spotlight
Zeren Zhang
Model Risk Analyst

How long have you been with RiskSpan?
About four and a half years.
What does your role at RiskSpan entail?

I work as an analyst in Model Risk Management team. My primary role is to validate the models and ensure the models can fulfill their purposes. I also contribute to management tasks, including designing test cases, tracking project status, and communicating with clients.

What 3 words best describe RiskSpan?
Supportive, Dynamic, Reliable
What are your favorite hobbies?
Traveling and cooking.
What is one thing on your Bucket List? 
Travel abroad to experience different cultures.
What is your go-to or favorite snack? 
Loacker chocolate wafer cookies.
What do you like about our company culture?
Supportive. RiskSpan fosters a culture of collaboration, where every member willingly offers support and assistance to their peers. Individuals are enthusiastic about sharing their expertise and abilities to contribute to the success of others.
Which company values resonate with you the most?
Inclusion. RiskSpan organizes monthly happy hours, town hall meeting and other activities to foster a sense of belonging and inclusivity among members.
Describe how you’ve grown professionally since you started working for us?
At RiskSpan, I have the opportunity to assess models constructed using various methodologies during validations, thereby enhancing my understanding of modeling processes in practice. Additionally, engaging in management tasks has contributed to the improvement of my communication skills. Through these experiences, I’ve gained valuable insights into both technical aspects of modeling and effective communication.

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