New innovative pricing model offers lower costs, transparency, and flexibility for analytics users 

RiskSpan, a top provider of cloud-based analytics solutions for loans, MSRs, structured products and private credit, announced today the launch of a usage-based pricing model for its Edge Platform. The new pricing model enables clients flexibility to pay only for the compute they use. It also gives clients access to the full platform, including data, models, and analytics, without having to license individual product modules. 

Usage-based pricing is a trend that reflects the evolving nature of analytics and the increasing demand for more flexible, transparent, and value-driven pricing models. It is especially suited for the dynamic and diverse needs of analytics users, whose data volumes, usage patterns, and analytical complexity requirements often fluctuate with the markets.

RiskSpan was an early adopter of the Amazon Web Services (AWS) cloud in 2010. Its new usage-based pricing, powered by the AWS cloud, enables RiskSpan to invoice its clients based on user-configured workloads, which can scale up or down as needed. 

“Usage-based pricing is a game-changer for our clients and the industry,” said Bernadette Kogler, CEO of RiskSpan. “It aligns our pricing with the value we deliver and the outcomes we enable for our clients. It also eliminates the waste and inefficiency of paying for unused, fixed-fee compute capacity, year after year in long-term, set price contracts. Now our clients can optimize their spending while experimenting with all the features our platform has to offer.”

“We are excited RiskSpan chose AWS to launch its new pricing model. Our values are aligned in earning trust through transparent variable pricing that allows our customers to innovate and remain agile.” said Ben Schreiner, Head of Business Innovation, at Amazon Web Services. “By leveraging the latest in AWS technology, including our generative AI services, RiskSpan is accelerating the value they deliver to their customers, and ultimately, the entire financial services industry.”

Usage-based pricing offers several benefits for RiskSpan clients, including: 

• Lower Costs: Clients pay only for what they need, rather than being locked into an expensive contract that may not suit their current or future situation. 
• Cost Sharing: Clients can share costs across the enterprise and better manage expense based on usage by individual functions and business units. 
• Transparency: Clients can monitor their usage and directly link their analytics configuration and usage to their results and goals. They can also better control their spending by tracking their usage and seeing how it affects their bill. 
• Flexibility: Clients can experiment with different features and options of RiskSpan’s Edge Platform, as they are not restricted by a predefined package or plan. 

For a free demo, visit https://riskspan.com/ubp/.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With an unparalleled team of data science experts and technologists, RiskSpan is the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Its mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.

Mortgage whole loans are having something of a moment as an asset class, particularly among insurance companies and other nonbank institutional investors. With insurance companies increasing their holdings of whole loans by 35 percent annually over the past three years, many people are curious what it is about these assets that makes them so appealing in the current environment.

We sat down with Peter Simon, founder and CEO of Dominium Advisors, a tech-enabled asset manager specializing in the acquisition and management of residential mortgage loans for insurance companies and other institutional investors. As an asset manager, Dominium focuses on performing the “heavy lifting” related to loan investing for clients. 

How has the whole loan asset class evolved since the 2008 crisis? How have the risks changed?

Peter Simon: Since 2008, laws and regulations like the Dodd-Frank act and the formation of the Consumer Financial Protection Bureau have created important risk guardrails related to the origination of mortgage products. Many loan and mortgage product attributes, such as underwriting without proper documentation of income or assets or loan structures with negative amortization, which contributed to high levels of mortgage defaults in 2008 are no longer permissible. In fact, more than half of the types of mortgages that were originated pre-crisis are no longer permitted under the current “qualified mortgage” regulations.  In addition, there have been substantial changes to underwriting, appraisal and servicing practices which have reduced fraud and conflicts of interest throughout the mortgage lifecycle.

How does whole loan investing fit into the overall macro environment?

Peter Simon: Currently, the macro environment is favorable for whole loan investing. There is a substantial supply-demand imbalance – meaning there are more buyers looking for places to live then there are homes for them to live in. At the current rates of new home construction, mobility trends, and household formation, it is expected that this imbalance will persist for the next several years.  Demographic trends are also widening the current supply demand imbalance as more millennial buyers are entering their early 30s – the first time-homebuyer sweet spot.  And work from home trends created by the pandemic are creating a desire for additional living space.

Who is investing in whole loans currently?

Peter Simon: Banks have traditionally been the largest whole loan investors due to their historical familiarity with the asset class, their affiliated mortgage origination channels, their funding advantage and favorable capital rules for holding mortgages on balance sheet.  Lately, however, banks have pulled back from investing in loans due to concerns about the stickiness of deposits, which have been used traditionally to fund a portion of mortgage purchases, and proposed bank capital regulations that would make it more costly for banks to hold whole loans.  Stepping in to fill this void are other institutional investors — insurance companies, for example — which have seen their holdings of whole loans increase by 35% annually over the past 3 years. Credit and hedge funds and pension funds are also taking larger positions in the asset class. 

What is the specific appeal of whole loans to insurance companies and these other firms that invest in them?

Peter Simon: Spreads and yields on whole loans produce favorable relative value (risk versus yield) when compared to other fixed income asset classes like corporate bonds.  Losses since the Financial Crisis have been exceptionally low due to the product, process and regulatory improvements enacted after the Financial Crisis.  Whole loans also produce risks in a portfolio that tend to increase overall portfolio diversification.  Borrower prepayment risk, for example, is a risk that whole loan investors receive a spread premium for but is uncorrelated with many other fixed income risks.  And for investors looking for real estate exposure, residential mortgage risk has a much different profile than commercial mortgage risk.

Why don’t they just invest in non-Agency securities?

Peter Simon: Many insurance companies do in fact buy RMBS securities backed by non-QM loans.  In fact, most insurance companies who have residential exposure will have it via securities.  The thesis around investing in loans is that the yields are significantly higher (200 to 300 bps) than securities because loans are less liquid, are not evaluated by the rating agencies and expose the insurer to first loss on a defaulted loan.  So for insurance investors who believe the extra yield more than compensates them for these extra risks (which historically over the last 15 years it has), they will likely be interested in investing in loans.

What specific risk metrics do you evaluate when considering/optimizing a whole loan portfolio – which metrics have the highest diagnostic value?

Peter Simon: Institutional whole loan investors are primarily focused on three risks: credit risk, prepayment risk and liquidity risk. Credit risk, or the risk that an investor will incur a loss if the borrower defaults on the mortgage is typically evaluated using many different scenarios of home price appreciation and unemployment to evaluate both expected losses and “tail event” losses.  This risk is typically expressed as projected lifetime credit losses.  Prepayment risk is commonly evaluated using loan cash flow computed measures like option adjusted duration and convexity under various scenarios related to the potential direction of future interest rates (interest rate shocks).

How would you characterize the importance of market color and how it figures into the overall assessment/optimization process?

Peter Simon: Newly originated whole loans like any other “new issue” fixed income product are traded in the market every day.  Whole loans are generally priced at the loan level based on their specific borrower, loan and property attributes.  Collecting and tabulating loan level prices every day is the most effective way to construct an investment strategy that optimizes the relative differences between loans with different yield characteristics and minimizes credit and prepayment risks in many various economic and market scenarios.

ARLINGTON, Va., January 24, 2024 – RiskSpan, the leading tech provider of data management and analytics services for loans and structured products, has partnered with tech-enabled asset manager Dominium Advisors to introduce a new whole loan market color dashboard to RiskSpan’s Edge Platform.

This new dashboard combines loan-level market pricing and trading data with risk analytics for GSE-eligible and non-QM loans. It enables loan investors unprecedented visibility into where loans are currently trading and insight on how investors can currently achieve excess risk-adjusted yields.

The dashboard highlights Dominium’s proprietary loan investment and allocation approach, which allows investors to evaluate any set of residential loans available for bid. Leveraging RiskSpan’s collateral models and risk analytics, Dominium’s software helps investors maximize yield or spread subject to investment constraints, such as a risk budget, or management constraints, such as concentration limits.

“Our strategic partnership with RiskSpan is a key component of our residential loan asset management operating platform ,” said Peter A. Simon, Founder and CEO of Dominium Advisors. “It has enabled us to provide clients with powerful risk analytics and data management capabilities in unprecedented ways.”

“The dashboard is a perfect complement to our suite of analytical tools,” noted Janet Jozwik, Senior Managing Director and Head of Product for RiskSpan’s Edge Platform. “We are excited to be a conduit for delivering this level of market color to our mortgage investor clients.”

The market color dashboard (and other RiskSpan reporting) can be accessed by registering for a free Edge Platform login at https://riskspan.com/request-access/.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With an unparalleled team of data science experts and technologists, RiskSpan is the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Its mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.

About Dominium Advisors Dominium Advisors is a tech-enabled asset manager specializing in the acquisition and management of residential mortgage loans for insurance companies and other institutional investors. The firm focuses on newly originated residential mortgage loans made to high quality borrowers – GSE eligible, jumbo and non-QM. Its proprietary loan-level software makes possible the construction of loan portfolios that achieve investor defined objectives such as higher risk-adjusted yields and spreads or limited exposure to tail risk events. Learn more at dominiumadvisors.com.

Usage-based pricing model brings big benefits to clients of RiskSpan’s Edge Platform

Analytic solutions for loans, MSRs and structured products are typically offered as software-as-a-service (SaaS) or “on-prem” products, where clients pay a monthly or annual fee to access the software and its features. The compute needed to run analytic workloads is typically purchased in advance and is fixed regardless of the need or use case.  

However, this traditional pricing model is not always the best fit for the dynamic and diverse needs of analytics users. It is technologically outdated and does not meet users where they are – with varying data volumes, usage patterns, and analytical complexity requirements that fluctuate with the markets. It is simply wasteful for companies to pay for unused, fixed-fee compute capacity, year-after-year in long-term, set price contracts, when their needs don’t require it. 

Usage-based pricing is a trend that reflects the evolving nature of analytics and the increasing demand for more flexible, transparent, and value-driven pricing models.

RiskSpan Introduces Usage-based Pricing

RiskSpan has just announced the release of industry-innovating usage-based pricing that allows clients to scale up or down, based on their needs. Further, clients of the RiskSpan platform will now benefit from access to the full Edge Platform, including data, models and analytics – eliminating the need to license individual product modules. The Platform supports loans, MSRs and securities, with growing capabilities around private credit. Analyzing these assets can be compute- and data-intensive because of the need for collateral (loan-level) data and models to price, value, and calculate risk metrics.

A Single Platform
Integrated Data | Trade Analytics | Risk Management

Usage-based pricing is an innovative alternative approach based on user-configured workloads. It enables RiskSpan to invoice its clients according to how much compute they actually need and use, rather than a fixed fee based on the modules they purchased during the last budget cycle.  

Key Benefits of RiskSpan’s Usage-based Pricing

Usage-based pricing benefits RiskSpan clients in several ways, including: 

• • Lower Costs: Clients pay only for what they need, rather than being locked into an expensive contract that may not suit their current or future situation.

• • Cost-Sharing Across the Enterprise: Clients can share costs across the enterprise and better manage expense based on usage by internal functions and business units.

• • Transparency: Clients can monitor their usage and directly link their analytics configuration and usage to their results and goals. They can also better control their spending, as they can track their usage and see how it affects their bill.

• • Flexibility: Clients can experiment with different features and options of the Platform, as they are not restricted by a predefined package or plan.

Usage-based pricing is not a one-size-fits-all solution, and it may not be suitable for every organization. Based on needs, large enterprise workloads will require specific, customized licensing and may benefit from locked in compute that comes with volume discounts.

Bottom Line on RiskSpan’s Usage-based Pricing Model

Sample Use Cases: Application to Loans, MSR, and Structured Products

With the introduction of usage-based pricing, RiskSpan is adding core value to its Edge Platform and a low-cost entry point to bring its solution to a wider base of clients. Its industry-leading capabilities solve challenges facing various users in the loans, MSR, and structured portfolio domains. For example:

1. 1. Loan/MSR Trader seeks analytics to support bidding on pools of loans and/or MSRs. Their usage is ad-hoc and will benefit from usage-based pricing. Traders and investors can analyze prepay and credit performance trends by leveraging RiskSpan’s 20+ years of historical performance datasets.

1. 1. Securities Trader (Agency or Non-Agency) wants more flexibility to set their prepay or credit model assumptions to run ad-hoc scenario analysis not easily handled by their current vendor.

1. 1. Risk Manager wants another source of valuation for periodic MSR and loan portfolios to enhance decision making and compare against the marks from their third-party valuation firm. 

1. 1. Private Credit Risk Manager needs a built-for-purpose private credit analytics system to properly run risk metrics. Users can run separate and run ad hoc analysis on these holdings.

RiskSpan’s Simple and Transparent Usage-Based Pricing Plans

For more specific information about how RiskSpan will structure pricing with various commitment levels, click below to tell us about your needs, and a representative will be in touch with you shortly. 

Using External Tables Inside Snowflake to work with Freddie Mac public data (13 million loans across 116 fields)

Using Freddie Mac public loan data as an example, this five-minute tutorial succinctly demonstrates how to:

1. Create a storage integration
2. Create an external stage
3. Grant access to stage to other roles in Snowflake
4. List objects in a stage
5. Create a format file
6. Read/Query data from external stage without having to create a table
7. Create and use an external table in Snowflake

This is the third in a 10-part tutorial series demonstrating how RiskSpan’s Snowflake integration makes mortgage and structured finance analytics easier than ever before.

Episode 1, Setting Up a Database and Uploading 28 Million Mortgage Loans, is available here.

Episode 2, Using Python User-Defined Functions in Snowflake SQL, is available here.

Future topics will include:

• OLAP vs OLTP and hybrid tables in Snowflake
• Time Travel functionality, clone and data replication
• Normalizing data and creating a single materialized view
• Dynamic tables data concepts in Snowflake
• Data share
• Data masking
• Snowpark: Data analysis (pandas) functionality in Snowflake

Learn how to use Python User-Defined Functions in Snowflake SQL

Using CPR computation for a pool of mortgage loans as an example, this six-minute tutorial succinctly demonstrates how to:

1. Query Snowflake data using SQL
2. Write and execute Python user-defined functions inside Snowflake
3. Compute CDR using Python UDF inside Snowflake SQL

This is this second in a 10-part tutorial series demonstrating how RiskSpan’s Snowflake integration makes mortgage and structured finance analytics easier than ever before.

Episode 1, Setting Up a Database and Uploading 28 Million Mortgage Loans, is available here.

Future topics will include:

• External Tables (accessing data without a database)
• OLAP vs OLTP and hybrid tables in Snowflake
• Time Travel functionality, clone and data replication
• Normalizing data and creating a single materialized view
• Dynamic tables data concepts in Snowflake
• Data share
• Data masking
• Snowpark: Data analysis (pandas) functionality in Snowflake

ARLINGTON, Va., March 23, 2023 – RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for mortgage and structured products, has announced the addition of commercial real estate (CRE) and commercial and industrial (C&I) loan data intake, valuation, and risk analytics to its award-winning Edge Platform. This enhancement complements RiskSpan’s existing residential mortgage toolbox and provides clients with a comprehensive toolbox for constructing and managing diverse credit portfolios.

Now more than ever, banks and credit portfolio managers need tools to construct well diversified credit portfolios resilient to rate moves and to know the fair market values of their diverse credit assets.

The new support for CRE and C&I loans on the Edge Platform further cements RiskSpan’s position as a single-source provider for loan pricing and risk management analytics across multiple asset classes. The Edge Platform’s AI-driven Smart Mapping (tape cracking) tool lets clients easily work with CRE and C&I loan data from any format. Its forecasting tools let clients flexibly segment loan datasets and apply performance and pricing assumptions by segment to generate cash flows, pricing and risk analytics.

CRE and C&I loans have long been supported by the Edge Platform’s credit loss accounting module, where users provided such loans in the Edge standard data format. The new Smart Mapping support simplifies data intake, and the new support for valuation and risk (including market risk) analytics for these assets makes Edge a complete toolbox for constructing and managing diverse portfolios that include CRE and C&I loans. These tools include cash flow projections with loan-level precision and stress testing capabilities. They empower traders and asset managers to visualize the risks associated with their portfolios like never before and make more informed decisions about their investments.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation demo at riskspan.local/.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.

ARLINGTON, Va., March 3, 2023 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced the incorporation of Flexible Loan Segmentation functionality into its award-winning Edge Platform.

The new functionality makes Edge the only analytical platform offering users the option of alternating between the speed and convenience of rep-line-level analysis and the unmatched precision of loan-level analytics, depending on the purpose of their analysis.

For years, the cloud-native Edge Platform has stood alone in its ability to offer the computational scale necessary to perform loan-level analyses and fully consider each loan’s individual contribution to a mortgage or MSR portfolio’s cash flows. This level of granularity is of paramount importance when pricing new portfolios, taking property-level considerations into account, and managing tail risks from a credit/servicing cost perspective.

Not every analytical use case justifies the computational cost of a full loan-level analysis, however. For situations where speed requirements dictate the use of rep lines (such as for daily or intra-day hedging needs), the Edge Platform’s new Flexible Loan Segmentation affords users the option to perform valuation and risk analysis at the rep line level.

Analysts, traders and investors take advantage of Edge’s flexible calculation specification to run various rate and HPI scenarios, key rate durations, and other calculation-intensive metrics in an efficient and timely manner. Segment-level results run at both loan and rep line level can be easily compared to assess the impacts of each approach. Individual rep lines are easily rolled up to quickly view results on portfolio subcomponents and on the portfolio as a whole.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation demo at riskspan.local/.

This new functionality is the latest in a series of enhancements that further the Edge Platform’s objective of providing frictionless insight to Agency MBS traders and investors, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.

Learn how to create a new Snowflake database and upload large loan-level datasets

The first episode of RiskSpan’s Snowflake Tutorial Series has dropped!

This six-minute tutorial succinctly demonstrates how to:

1. Set up a new Snowflake #database
2. Use SnowSQL to load large datasets (28 million #mortgage loans in this example)
3. Use internal staging (without a #cloud provider)

This is this first in what is expected to be a 10-part tutorial series demonstrating how RiskSpan’s Snowflake integration makes mortgage and structured finance analytics easier than ever before.

Future topics will include:

• Executing complex queries using python functions in Snowflake’s SQL
• External Tables (accessing data without a database)
• OLAP vs OLTP and hybrid tables in Snowflake
• Time Travel functionality, clone and data replication
• Normalizing data and creating a single materialized view
• Dynamic tables data concepts in Snowflake
• Data share
• Data masking
• Snowpark: Data analysis (pandas) functionality in Snowflake

Are you curious about how your organization can uplevel the accuracy of your MSR cost forecasting? The answer lies in leveraging the full spectrum of your data and running analyses at the loan level rather than cohorting. But what does it take to make the switch to loan-level analytics? Our team has put together a short set of recommendations and considerations for how to tackle an otherwise daunting project…

It begins with having the data. Most investors have access to loan-level data, but it’s not always clean. This is especially true of origination data. If you’re acquiring a pool – be it a seasoned pool or a pool right after origination – you don’t have the best origination data to drive your model. You also need a data store, like Snowflake, that can generate loan-loan level output to drive your analytics and models.  

The second factor is having models that work at the loan level – models that have been calibrated using loan-level performance and that are capable of generating loan-level output. One of the constraints of several existing modeling frameworks developed by vendors is they were created to run at a rep line level and don’t necessarily work very well for loan-level projections.

The third requirement is a compute farm. It is virtually impossible to run loan-level analytics if you’re not on the cloud because you need to distribute the computational load. And your computational distribution requirements will change from portfolio to portfolio based on the type of analytics that you are running, based on the types of scenarios that you are running, and based on the models you are using. The cloud is needed not just for CPU power but also for storage. This is because once you go to the loan level, every loan’s data must be made available to every processor that’s performing the calculation. This is where having the kind of shared databases, which are native to a cloud infrastructure, becomes vital. You simply can’t replicate it using an on-premise setup of computers in your office or in your own data center. Adding to this, it’s imperative for mortgage investors to remember the significance of integration and fluidity. When dealing with loan-level analytics, your systems—the data, the models, the compute power—should be interlinked to ensure seamless data flow. This will minimize errors, improve efficiency, and enable faster decision-making.

Fourth—and an often-underestimated component—is having intuitive user interfaces and visualization tools. Analyzing loan-level data is complex, and being able to visualize this data in a comprehensible manner can make all the difference. Dashboards that present loan performance, risk metrics, and other key indicators in an easily digestible format are invaluable. These tools help in quickly identifying patterns, making predictions, and determining the next strategic steps.

Fifth and finally, constant monitoring and optimization are crucial. The mortgage market, like any other financial market, evolves continually. Borrower behaviors change, regulatory environments shift, and economic factors fluctuate. It’s essential to keep your models and analytics tools updated and in sync with these changes. Regular back-testing of your models using historical data will ensure that they remain accurate and predictive. Only by staying ahead of these variables can you ensure that your loan-level analysis remains robust and actionable in the ever-changing landscape of mortgage investment.