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Loans and MSRs LP

Unlock Loan and MSR Value

Easy loan-level analysis for better identification of retention opportunities and smarter portfolio management

 

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Advanced Loan-Level MSR Cash Flow Engine and Analytics

  • Industry-leading cash flow engine allows for full transparency and control of income and expense assumptions at a granular level including recapture rates

  • Full/excess MSR cash flow engines and prepay/credit models run at loan level across Conventional/Ginnie/Private-label MSRs

  • Forecasts include detailed MSR component cash flows, option-adjusted valuations, and risk/scenario metrics

  • Results can be viewed across various pre-canned or custom segmentations

Interactively Query/Filter Loan Data and Historical Performance Metrics

(Direct Data Access via Snowflake Also Available)

Access interactive Edge Loans module: 

    • Create Composition reports
    • Query/filter loan data
    • Extract loan data for external constituents

Report on historical performance and filter performance metrics across dimensions:

    • Prepayment
    • Default
    • Recapture rates

Leverage suite of customized data visualization reports (via Tableau)

MSR Bid Analysis – Fast Tape Cracking for Loan-Level Forecasting

Powered by Smart Mapping tools and Configurable QC, RiskSpan streamlines and automates MSR data ingestion across multiple servicers and data sources:

  • Quickly load and map portfolios from different counterparties

  • Apply machine learning model that accounts for past experience

  • Leverage RiskSpan’s recommended QC rules and backup values

  • Access QC audit reports showing mapping choices and exceptions

  • View MOM data QC visualization reports

MSR Portfolio Risk Managment (Risk as a Service)
Comprehensive Solution for Daily MSR and Hedge Valuation/Analytics

Leverage modern technology and a managed service approach for reliable and timely daily MSR valuation and risk analytics 

  • Automated, overnight run process allows for on-time delivery of daily analytics run at the loan-level

  • Incorporates QC checks to ensure high quality of results/reports

  • Flexible approach to incorporating custom model dials and MSR assumptions

  • Options available to run at a repline-level to facilitate extensive scenario analyses in fastest time possible

Leverage modern technology and a managed service approach for reliable and timely daily MSR valuation and risk analytics 

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Leveraging Pool-Specific Performance and Recapture Analysis: A Game Changer for MSR Investors

Successfully forecasting MSR cash flows demands a level of precision and granularity in data analysis that few other asset classes require. This is especially true for investors seeking to estimate how much prepayment runoff they can reasonably expect to recapture, which is key to the performance of the asset. And often investors need to measure that performance by the specific pools of MSRs they purchase — as each pool may have its own unique recapture arrangements.

RiskSpan’s Edge Platform has incorporated a robust framework for managing MSR investment performance by enabling investors to track pool-specific performance and recapture analyses, thus obtaining a more nuanced understanding of their portfolios. In this post, we delve into some of the specific challenges MSRs pose, the benefits of transaction-specific segmentation, and the unique capabilities of RiskSpan’s Edge Platform.

Understanding Pool-Specific Performance

Owning MSRs requires investors to track the performance of various loan pools over time. For example, an investor may purchase an MSR pool and rely on a sub-servicer to service the loans as well as make efforts to recapture borrowers that are looking to refinance. It is important for the investor to understand and track the returns on that pool which may be largely driven by recapture efficiency.  

While performance needs to be monitored on a pool-level, the modeling of the underlying loans is dependent on the distinct characteristics of the loans within a pool and will be more accurate if the models are run at the loan-level (or at granular rep lines determined by smart rep line logic).  The ability to capture and analyze these pool-specific cash flows based on granular loan-level modeling is crucial for several reasons:

  1. Valuation Accuracy: Each loan can be valued more accurately by considering its unique attributes, such as the original loan terms, interest rates, and borrower profiles (e.g., FICO, LTV); at the same time, pools can be valued based on pool-specific assumptions such as recapture rates and prepayment penalties.
  2. Risk Management: Understanding the performance of individual pools helps in identifying which pools are more prone to prepayments or defaults, enabling more focused efforts on recapture and other risk management activities.
  3. Performance Tracking: Investors can track historical returns, CPRs, CDRs, Recapture and other historical performance metrics for each pool, facilitating more informed decision-making.

Supporting this functionality is RiskSpan’s ability to share and integrate data on Snowflake’s data cloud. RiskSpan’s Snowflake integration enhances the data management and analytics capabilities available to clients. Investors can easily share transaction-specific data through Snowflake, which is then seamlessly integrated into the Edge platform. The platform can then handle the large datasets (tens of millions of loans in some instances), providing real-time analytics and insights.

Recapture Analysis: Enhancing Portfolio Performance

Recapture analysis is a critical component for MSR portfolio risk management. When borrowers refinance or otherwise pay off their loans, the servicer’s cash flows usually vanish entirely. However, if, in the case of refinance, the investor retains the rights to service the loan replacing the refinanced loan, then the new loan can be considered as a recapture. RiskSpan’s Edge platform excels in tracking these recaptures, offering several advantages:

  1. Detailed Tracking: The platform allows for the separation and detailed tracking of original loans and their recaptures, maintaining the distinction between the two. Recaptures should have better performance (i.e., lower CPRs) than original loans.
  2. Performance Comparison: By comparing the performance of original loans and recaptures, investors can gauge the effectiveness of their recapture strategies.
  3. Granular Assumptions: Edge supports highly granular recapture rate assumptions used for projecting cash flows, which can be tailored to specific pools or deals, enhancing the precision of valuation.

A Case Study: Supporting a Large Mortage REIT’s MSR Portfolio Management Regime

A practical example of these capabilities involves a mortgage REIT, which relies on RiskSpan’s platform to manage a large MSR portfolio. Specifically, the Edge platform has enabled the REIT investor to accomplish the following:

  • Capture Transaction-Specific Data: the investor can track and analyze data at the transaction level, maintaining detailed records of each pool’s performance and its recaptures. This allows, for example, investors to review performance with sub-servicers and evaluate whether certain changes can be made to enhance performance either on the existing pool or on future pools.
  • Custom Assumption Setting: The platform allows for custom segmentation and assumption setting for valuation purposes, such as different recapture rates based on prepayment projections or loan age. This provides an ability to more accurately measure future projected cash flows and factor that into valuation of owned MSRs as well as potential purchases.

RiskSpan’s Edge platform offers MSR investors a robust toolset for managing their portfolios with precision not available anywhere else. By enabling pool-specific performance and detailed recapture analysis, Edge helps investors optimize their strategies and enhance portfolio performance. The ability to capture and analyze nuanced data points sets RiskSpan apart, making it a valuable ally in the complex landscape of MSR investments.

MSR investors, contact us to discover how tailored analytics and granular data management can transform your investment strategy and give you a competitive edge.

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.

Loans & MSRs: Managing model assumptions and tuners the easy way

One of the things that makes modeling loan and MSR cash flows hard is appropriately applying assumptions to individual loans. Creating appropriate assumptions for each loan or MSR segment is crucial to estimating realistic performance scenarios, stress testing, hedging, and valuation. However, manually creating and maintaining such assumptions can be time-consuming, error-prone, and inconsistent across different segments and portfolios.

Fortunately, hidden among some of the Edge Platform’s better-known features is a powerful and flexible way of running loan-level analytics on a portfolio using the Platform’s segment builder and loan model assumptions features.

These sometimes-overlooked features allow users to create and apply granular and customized modeling assumptions to a particular loan portfolio, based on its various, unique loan characteristics. Assumptions can be saved and reused for future analysis on different loans tapes.  This feature allows clients to effectively build and manage a complex system of models adjustment and tuners for granular sub-segments.

Applying the segment builder and loan model assumptions features, loan investors can:

    • Decouple how they run and aggregate results from how they assign modeling assumptions, and seamlessly assign different assumptions to various segments of the portfolio, based on user-defined criteria and preferences. For example, investors can assign different prepayment, default, and severity assumptions to loans based on their state, LTV, UPB, occupancy, purpose, delinquency status, loan type, collateral features, or virtually any other loan characteristic.

 

    • Choose from a variety of models and inputs, including RiskSpan models and vector inputs for things like CPR and CDR. Investors can define their own vector inputs as an aging curves by loan age or based on the forecast month, and apply them to different segments of the portfolio. For example, they can define their own CDR and CPR curves for consumer or C&I loans, based on the age of the loans.

    • Set up and save modeling assumptions one time, and then reference them over and over again whenever new loan tapes are uploaded. This saves time and effort and ensures consistency and accuracy in the analysis.

This hidden feature enables investors to customize their analysis and projections for different asset classes and scenarios, and to leverage the Edge Platform’s embedded cash flow, prepayment and credit models without compromising the granularity and accuracy of the results. Users can create and save multiple sets of loan model assumptions that include either static inputs, aging curves, or RiskSpan models, and apply them to any loan tape they upload and run in the forecasting UI.

Contact us and request a free demo or trial to learn more about how to use these and other exciting hidden (and non-hidden) features and how they can enhance your loan analytics.

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How an MSR Analytical Solution Can Boost Your Mortgage Banking Business

And why it’s probably less expensive than you think

Mortgage servicing rights (MSRs) are complex and volatile assets that require careful management and analysis. Inherent in MSR risk management is the need to monitor portfolio performance, assess risks and opportunities, evaluate and implement risk-reducing strategies such as recapture and interest rate hedging, and effectively communicate all this to investors and regulators. Handling all this has traditionally required an enormous budget for data, software, and consultants. Many mortgage banks are left with either using outdated and inflexible internal systems or outsourcing their analytics to third parties that lack full transparency and bill clients for every request. 

Not anymore.

The answer is a cloud-native MSR analytical solution that includes slice-and-dice-able Agency loan performance data as well as the models necessary to produce valuations, risk analytics and cash flows across both MSRs and associated derivative hedges, where applicable.

By integrating data, models, and reports, this combined solution enables mortgage banks to:

  • Generate internal metrics to compare with those received from third party brokers and consultants
  • Measure the fair value and cash flows of their MSRs under different scenarios and assumptions including a variety of recapture assumptions
  • Analyze the sensitivity of their MSRs (and associated hedges) to changes in interest rates, prepayment speeds, defaults, home prices and other factors
  • Compare their portfolio’s performance and characteristics with the market and industry peers
  • Generate customized reports and dashboards to share with investors, auditors, and regulators

More specifically, RiskSpan’s comprehensive data and analytics solution enables you to do the following:

1. Check assumptions used by outside analysts to run credit and prepayment analytics

Even in cases where the analytics are provided by a third party, mortgage banks frequently benefit from having their own analytical solution. Few things are more frustrating than analytics generated by a black box with no/limited visibility into assumptions or methodology. RiskSpan’s MSR tool provides mortgage banks with an affordable means of checking the assumptions and methodologies used by outside analysts to run credit and prepayment analytics on their portfolio.

Different analysts use different assumptions and models to run credit and prepayment analytics, often leading to inconsistent results that are difficult to explain. Some analysts use historical data while others rely on forward-looking projections. Some analysts simple models while others turn to complex one. Some analysts are content with industry averages while others dig into portfolio-specific data.

Having access to a fully transparent MSR analytical solution of their own allows mortgage banks to check the assumptions and models used by outside analysts for reasonableness and consistency. In addition to helping with results validation and identification of discrepancies or errors, it also facilitates communication of the rationale and logic behind assumptions and models to investors and regulators.  Lastly, the ability for a mortgage bank to internally generate MSR valuations and cash flows allows for a greater understanding of the economic value (vs. accounting value) of the asset they hold.

2. Understand how your portfolio’s prepayment performance stacks up against the market

Prepayment risk is one of the main drivers of MSR value and volatility. Mortgage banks need to know how their portfolio’s prepayment performance compares with the market and their peers. Knowing this helps mortgage banks field questions from investors, who may be concerned about the impact of prepayments on profitability and liquidity. It also helps identify areas of improvement and opportunity for the portfolio.

RiskSpan’s MSR analytical solution helps track and benchmark portfolio prepayment performance using various metrics, including CPR and SMM. It also helps analysts understand the drivers and trends of prepayments, such as interest rates, loan age, loan type, credit score, and geographic distribution. RiskSpan’s MSR analytical solution combined with its historical performance data provides a deeper understanding of how a portfolio’s prepayment performance stacks up against the market and what factors affect it.

And it’s less expensive than you might think

You may think that deploying an MSR analytical solution is too costly and complex, as it requires a lot of data, software, and expertise. However, this is not necessarily true.

Bundling RiskSpan’s MSR analytical solution with RiskSpan’s Agency historical performance tool actually winds up saving clients money by helping them optimize their portfolios and avoid costly mistakes. The solution:

  • Reduces the need for external data, software, and consultants because all the information and tools needed are in one platform
  • Maximizes portfolio performance and profitability by helping to identify and capture opportunities and mitigate risks, including through recapture analysis and active hedging
  • Enhances reputation and credibility by improving transparency to investors and regulators

RiskSpan’s solution is affordable and easy to use, with flexible pricing and deployment options, as well as user-friendly features and support, including intuitive interfaces, interactive dashboards, and comprehensive training and guidance. Its cloud-native, usage-based pricing structure means users pay only for the compute they need (in addition to a nominal licensing fee).

Contact us to learn more about how RiskSpan’s Edge Platform can help you understand how your MSR portfolio’s performance stacks up against the market, check assumptions used by outside analysts to run credit and prepayment analytics, and, most important, save money and time.

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What Do 2024 Origination Trends Mean for MSRs?

While mortgage rates remain stubbornly high by recent historical standards, accurately forecasting MSR performance and valuations requires a thoughtful evaluation of loan characteristics that go beyond the standard “refi incentive” measure.

As we pointed out in 2023, these characteristics are particularly important when it comes to predicting involuntary prepayments.

This post updates our mortgage origination trends for the first quarter of 2024 and takes a look at what they could be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned and stayed near the averages observed during the latter half of the 2010s.

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period.

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Consequently, in 2023 and 2024, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Between rates and home prices, ​​borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, continues to trend higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward? 

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends. 

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level. 

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

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RiskSpan to Launch Usage-based Pricing for its Edge Platform at SFVegas 2024 

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.

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.

A Single Platform
Integrated Data | Trade Analytics | Risk Management

Core Engine

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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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
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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

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