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Articles Tagged with: Credit Analytics

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.


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.


Enriching Pre-Issue Intex CDI Files with [Actual, Good] Loan-Level Data

The way RMBS dealers communicate loan-level details to prospective investors today leaves a lot to be desired.

Any investor who has ever had to work with pre-issue Intex CDI files can attest to the problematic nature of the loan data they contain. Some are better than others, but virtually all of them lack information about any number of important loan features.

Investors can typically glean enough basic information about balances and average note rates from preliminary CDI files to run simple, static CPR/CDR scenarios. But information needed to run complex models — FICO scores, property characteristics and geography, and LTV ratios to name a few — is typically lacking. MBS investors who want to run to run more sophisticated prepayment and credit models – models that rely on more comprehensive loan-level datasets to run deeper analytics and scenarios – can be left holding the bag when these details are missing from the CDI file.

The loan-level detail exists – it’s just not in the CDI file. Loan-level detail often accompanies the CDI file in a separate spreadsheet (still quaintly referred to in the 21st Century as a “loan tape”). Having this data separate from the CDI file requires investors to run the loan tape through their various credit and prepayment models and then manually feed those results back into the Intex CDI file to fully visualize the deal structure and expected cash flows.

This convoluted, multi-step workaround adds both time and the potential for error to the pre-trade analytics process.

A Better Way

Investors using RiskSpan’s Edge Platform can streamline the process of evaluating a deal’s structure alongside the expected performance of its underlying mortgage loans into a single step.

EDGEPLATFORM

Here is how it works.

As illustrated above, when investors set up their analytical runs on Edge, RiskSpan’s proprietary credit and prepayment models automatically extract all the required loan-level data from the tape and then connect the modeling results to the appropriate corresponding deal tranche in the CDI file. This seamlessness reduces all the elements of the pre-trade analytics process down to a matter of just a few clicks.

Making all this possible is the Edge Platform’s Smart Mapper ETL solution, which allows it to read and process loan tapes in virtually any format. Using AI, the Platform recognizes every data element it needs to run the underlying analytics regardless of the order in which the data elements are arranged and irrespective of how (or even whether) column headers are used.

Contact us to learn more about how RMBS investors are reaping the benefits of consolidating all of their data analytics on a single cloud-native platform.


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.


RiskSpan, Dominium Advisors Announce Market Color Dashboard for Mortgage Loan Investors


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.

Dashboard

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.


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

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

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.

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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’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

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