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The future of analytics pricing is RiskSpan’s Usage-based delivery model

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 It is technologically outdated and does not meet users where they are – with varying data volumes, usage patterns, and analytical complexity requirements that fluctuate with the markets.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.

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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

CONS of Traditional Fixed Fee Pricing PROS of Usage-Based Pricing
Flat-fee pricing models force customers to pay for unused capacity​. Lower Costs — Pay only for what you use, not the wasted capacity of a dedicated cluster
Unused capacity cannot be shared across the enterprise, which translates into wasted resources and higher costs. Cost Sharing — Costs can be shared across the enterprise to better manage expense based on usage by your internal functions and business units
Fixed pricing models make it difficult for customers to scale up or down as needed. Transparency — Transparent pricing that fits your specific analytics workload (size, complexity, performance)
Traditional “product module-based” purchasing runs the risk of over-buying on features that will not be used. Flexibility — Scale up and scale down your use as new and in-place features become useful to you under different market conditions

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. 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. 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. 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. 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. 

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Whole Loans

A Complete System for the Loan Investment Life Cycle

A comprehensive system to handle your data and analytic needs for whole loan investments. RiskSpan’s Edge Platform leverages modern cloud technology to enable mortgage investors, aggregators, and conduits to more effectively and accurately manage acquisitions, asset management, risk exposures, and total return.

Get a free Trial or Demo

Get a Free Trial or Demo

Outsource the Heavy Lifting of Consolidating and Mapping Servicer Data

Powered by Smart Mapping and Optimized QC rules, RiskSpan automates data ingestion across multiple servicers and data sources:

  • Machine learning data mapper trained on servicer data that handles acquisition and monthly feeds

  • QC rules and backup values that have been optimized for the top servicer data feeds

  • Automated QC audit reports showing mapping choices and exceptions

Dynamic Query/Filter Loan Data and Historical Performance Metrics

Analyze loan data using query/filter and custom composition reports

  • Report and filter on historical loan performance including prepayment, default, loss severity and recapture rates

Generate customized data visualization reports using Tableau

  • Leverage the speed and scalability of Snowflake for data access and queries

Loan Bid Analysis Trading Quality Risk Models, Loan-Level Valuations

RiskSpan has purpose-built tools and models to support active buyers/sellers of whole loans  

  • Access to RiskSpan’s proprietary, loan-level prepayment/credit models

  • Full set of collateral types covered including GSE, FHA, VA, Jumbo, and Non-QM

  • Valuation and risk metrics include detailed cash flows, OAS, OAD, and multi-factor risk/scenarios

  • Flexible interfaces enables custom risk model tuning

Portfolio Risk Management Powerful Scalability for Daily Analytics

  • Leverage Edge’s cloud architecture and scalability to generate granular loan-level valuations/risk analysis

  • Automate daily and customized risk calculations

  • Results can be viewed across various existing or custom loan cohorts

Outputs accessed through custom Tableau-based reports

Contact us to learn more, get a free demo, or request a free trial

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Our team of quants and data
scientists is available on demand
to provide custom support.

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Snowflake Tutorial Series: Episode 3

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
TALK TO AN EXPERT

RiskSpan’s Snowflake Tutorial Series: Ep. 2

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
TALK TO AN EXPERT

RiskSpan Adds CRE, C&I Loan Analytics to Edge Platform

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.com.

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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.

RiskSpan Incorporates Flexible Loan Segmentation into Edge Platform

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.com.

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.

###

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.

RiskSpan’s Snowflake Tutorial Series: Ep. 1

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
TALK TO AN EXPERT

Case Study: How a leading loan and MSR investor reduced costs with a loan-level approach

Learn more about how one whole loan and MSR investor (a large mortgage REIT) successfully overhauled its analytics computational processing with RiskSpan. The investor migrated from a daily pricing and risk process that relied on tens of thousands of rep lines to one capable of evaluating each of the portfolio’s more than three-and-a-half million loans individually (and how they actually saved money in the process). 

The Situation 

One of the industry’s largest mortgage REITs sought a more forward-thinking way of managing its extensive investment portfolio of mortgage servicing rights (MSR) assets, residential loans and securities. The REIT runs a battery of sophisticated risk management analytics that rely on stochastic modeling. Option-adjusted spread, duration, convexity, and key rate durations are calculated based on more than 200 interest rate simulations.

The investor used rep lines for one main reason: it needed a way to manage computational loads on the server and improve calculation speeds. Secondarily, organizing the loans in this way simplified the reporting and accounting requirements to a degree (loans financed by the same facility were grouped into the same rep line).  

This approach had some significant downsides. Pooling loans by finance facility was sometimes causing loans with different balances, LTVs, credit scores, etc., to get grouped into the same rep line. This resulted in prepayment and default assumptions getting applied to every loan in a rep line that differed from the assumptions that likely would have been applied if the loans were being evaluated individually. 

The Challenge 

The main challenge was the investor’s MSR portfolio—specifically, the volume of loans needing to be run. Having close to 4 million loans spread across nine different servicers presented two related but separate sets of challenges. 

The first set of challenges stemmed from needing to consume data from different servicers whose file formats not only differed from one another but also often lacked internal consistency. Even the file formats from a single given servicer tended to change from time to time. This required RiskSpan to continuously update its data mappings and (because the servicer reporting data is not always clean) modify QC rules to keep up with evolving file formats.  

The second challenge related to the sheer volume of compute power necessary to run stochastic paths of Monte Carlo rate simulations on 4 million individual loans and then discount the resulting cash flows based on option adjusted yield across multiple scenarios. 

And so there were 4 million loans times multiple paths times one basic cash flow, one basic option-adjusted case, one up case, and one down case—it’s evident how quickly the workload adds up. And all this needed to happen on a daily basis. 

To help minimize the computing workload, the innovative REIT had devised a way of running all these daily analytics at a rep-line level—stratifying and condensing everything down to between 70,000 and 75,000 rep lines. This alleviated the computing burden but at the cost of decreased accuracy because they could not look at the loans individually.

The Solution 

The analytics computational processing RiskSpan implemented ignores the rep line concept entirely and just runs the loans. The scalability of our cloud-native infrastructure enables us to take the nearly four million loans and bucket them equally for computation purposes. We run a hundred loans on each processor and get back loan-level cash flows and then generate the output separately, which brings the processing time down considerably. 

For each individual servicer, RiskSpan leveraged its Smart Mapper technology and Configurable QC feature in its Edge Platform to create a set of optimized loan files that can be read and rendered “analytics-ready” very quickly. This enables the loan-level data to be quickly consumed and immediately used for analytics without having to read all the loan tapes and convert them into a format that an analytics engine can understand. Because RiskSpan has “pre-processed” all this loan information, it is immediately available in a format that the engine can easily digest and run analytics on. 

What this means for you

An investor in any mortgage asset benefits from the ability to look at and evaluate loan characteristics individually. The results may need to be rolled up and grouped for reporting purposes. But being able to run the cash flows at the loan level ultimately makes the aggregated results vastly more meaningful and reliable. A loan-level framework also affords whole-loan and securities investors the ability to be sure they are capturing the most important loan characteristics and are staying on top of how the composition of the portfolio evolves with each day’s payoffs. 

CLICK HERE to talk to us and learn how a loan-level approach can help you

5 foundational steps for investors to move towards loan-level analyses

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.

How Rithm Capital leverages RiskSpan’s expertise and Edge Platform to enhance data management and achieve economies of scale

 

BACKGROUND

 

One of the nation’s largest mortgage loan and MSR investors was hampered by a complex data ingestion process as well as slow and cumbersome on-prem software for pricing and market risk.

A complicated data wrangling process was taking up significant time and led to delays in data processing. Further, month-end risk and financial reporting processes were manual and time-pressured. The data and risk teams were consumed with maintaining the day-to-day with little time available to address longer-term data strategies and enhance risk and modeling processes.

 

OBJECTIVES

  1. Modernize Rithm’s mortgage loan and MSR data intake from servicers — improve overall quality of data through automated processes and development of a data QC framework that would bring more confidence in the data and associated use cases, such as for calculating historical performance.

  2. Streamline portfolio valuation and risk analytics while enhancing granularity and flexibility through loan-level valuation/risk.

  3. Ensure data availability for accounting, finance and other downstream processes.

  4. Bring scalability and internal consistency to all of the processes above.

THE SOLUTION

THE EDGE WE PROVIDED

By adopting RiskSpan’s cloud-native data management, managed risk, and SaaS solutions, Rithm Capital saved time and money by streamlining its processes

Adopting Edge has enabled Rithm to access enhanced and timely data for better performance tracking and risk management by:

  • Managing data on 5.5 million loans, including source information and monthly updates from loan servicers (with ability in the future to move to daily updates)​
  • Ingesting, validating and normalizing all data for consistency across servicers and assets
  • Implementing automated data QC processes​
  • Performing granular, loan-level analysis​

 

With more than 5 million mortgage loans spread across nine servicers, Rithm needed a way to consume data from different sources whose file formats varied from one another and also often lacked internal consistency. Data mapping and QC rules constantly had to be modified to keep up with evolving file formats. 

Once the data was onboarded Rithm required an extraordinary amount of compute power to run stochastic paths of Monte Carlo rate simulations on all 4 million of those loans individually and then discount the resulting cash flows based on option adjusted yield across multiple scenarios.

To help minimize the computing workload, Rithm had been running all these daily analytics at a rep-line level—stratifying and condensing everything down to between 70,000 and 75,000 rep lines. This alleviated the computing burden but at the cost of decreased accuracy and limited reporting flexibility because results were not at the loan-level.

Enter RiskSpan’s Edge Platform.

Combining the strength of RiskSpan’s subject matter experts, quantitative analysts, and technologists together with the power of the Edge platform, RiskSpan has helped Rithm achieve its objectives across the following areas: 

Data management and performance reporting

  • Data intake and quality control for 9 servicers across loan and MSR portfolios
  • Servicer data enrichment
  • Automated data loads leading to reduced processing time for rolling tapes
  • Ongoing data management support and resolution
  • Historical performance review and analysis (portfolio and universe)

Valuation and risk

  • Daily reporting of MSR, mortgage loan and security valuation and risk analytics based on customized Tableau reports
  • MSR and whole loan valuation/risk calculated based at the loan-level leveraging the scalability of the cloud-native infrastructure
  • Additional scenario analysis and other requirements needed for official accounting and valuation purposes

Interactive tools for portfolio management

  • Fast and accurate tape cracking for purchase/sale decision support
  • Ad-hoc scenario analyses based on customized dials and user-settings

The implementation of these enhanced data and analytics processes and increased ability to scale these processes has allowed Rithm to spend less time on day-to-day data wrangling and focus more on higher-level data analysis and portfolio management. The quality of data has also improved, which has led to more confidence in the data that is used across many parts of the organization.

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Models + Data management = End-to-end Managed Process

”The economies of scale we have achieved by being able to consolidate all of our portfolio risk, interactive analytics, and data warehousing onto a single platform are substantial. RiskSpan’s experience with servicer data and MSR analytics have been particularly valuable to us.“​

          — Head of Analytics

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