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Articles Tagged with: Data Management

Connect with us at SFVegas 2024

Click Here to book a time to connect

RiskSpan is delighted to be sponsoring SFVegas 2024!

Connect with our team there to learn how we can help you move off your legacy systems, streamline workflows and transform your data.

Click Here to book a time to connect

Don’t miss these RiskSpan presenters at SFVegas 2024

Bernadette Kogler

Housing Policy:
What’s Ahead
Mon, Feb 26th, 1:00 PM

Tom Pappalardo

Future of Fintech
Wed, Feb 28th, 9:15 AM

Divas Sanwal

Big Data & Machine Learning: Impacts on Origination
Wed, Feb 28th, 11:05 AM

Can’t make the panels?

Click here to make an appointment to connect. Or just stop by Booth 13 in the exhibit hall!


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

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.


Case Study: Using Snowflake to Create Single Family Credit Risk Grids for a Federal Agency

The Client

Government Sponsored Enterprise (GSE)

The Problem

The client sought to transition its ERCF spot capital reporting process from legacy systems and processes to a new, fully integrated system with automated processes. 

This required the re-creation and automation in Snowflake of a legacy report for FHFA consisting of 30 credit risk and risk factor grids rolled up from the loan level.

The Solution

RiskSpan led a cross-functional effort including the data and reporting teams to implement a fully automated report using data and SQL in Snowflake.

The Deliverables

  • Loan attributes re-mapped from legacy data to Snowflake data
  • Reverse-engineered logic mapping attribute values to grid cohorts​
  • Complex and efficient SQL developed in Snowflake to transform loan-level spot capital data into cohorts for credit risk grids​
  • Conversion of 13 million loan records into more than 2,200 grid cells in less than 3 minutes​
  • Design and execution UAT​ in cooperation with the business team
  • Fully automated FHFA credit risk report populated by calling SQL

Case Study: Hadoop to Snowflake Migration

The Client

Government Sponsored Enterprise (GSE)

The Problem

The client sought to improve the performance and forecasting capabilities of its loan valuation and forecast engine. As part of this strategic initiative, the client planned to migrate the underlying platform from Hadoop to the Snowflake Data Cloud to achieve an increase in data loading and querying speeds and an overall optimization of system performance.​

RiskSpan identified a need for project management and implementation planning, as well as data pipeline and ETL migration analysis to ensure a successful integration of the Snowflake data cloud into the loan valuation and forecast engine.​​

The Solution

RiskSpan led the data migration effort for the loan valuation engine and integrated its pipelines from multiple data sources. The RiskSpan team also executed planning, testing, and overall project management of the implementation effort to ensure a high quality, on-schedule delivery.

The Deliverables

  • An integrated project plan with transition from current state to target state and production parallel
  • A system and data flow comparing existing state to target state
  • SQL code to efficiently compare 13 million records and more than 100 attributes loaded to Snowflake with legacy data in just 2 minutes.
  • Review of target state database ETL patterns
  • Review of loan valuation engine output using data in Snowflake
  • Comprehensive report presented to Senior Management

RiskSpan Unveils New “Reverse ETL” Mortgage Data Mapping and Extract Functionality

ARLINGTON, Va., October 19, 2022 – Subscribers to RiskSpan’s Mortgage Data Management product can now not only leverage machine learning to streamline the intake of loan data from any format, but also define any target format for data extraction and sharing.

A recent enhancement to RiskSpan’s award-winning Edge Platform enables users to take in unformatted datasets from mortgage servicers, sellers and other counterparties and convert them into their preferred data format on the fly for sharing with accounting, client, and other downstream systems.

Analysts, traders, and portfolio managers have long used Edge to take in and store datasets, enabling them to analyze historical performance of custom cohorts using limitless combinations of mortgage loan characteristics and run predictive analytics on segments defined on the fly. With Edge’s novel “Reverse ETL” data extract functionality, these Platform users can now also easily and fully design an export format for exporting their data, creating the functional equivalent of a full integration node for sharing data with literally any system on or off the Edge Platform.   

Market participants tout the revolutionary technology as the end of having to share cumbersome and unformatted CSV files with counterparties. Now, the same smart mapping technology that for years has facilitated the ingestion of mortgage data onto the Edge Platform makes extracting and sharing mortgage data with downstream users just as easy.   

Comprehensive details of this and other new capabilities using RiskSpan’s Edge Platform are available by requesting a no-obligation live demo at riskspan.com.

SCHEDULE A FREE DEMO

This new functionality is the latest in a series of enhancements that is making the Edge Platform’s Data as a Service increasingly indispensable for mortgage loan and MSR traders and investors.

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About RiskSpan, Inc. 

RiskSpan is a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products. The company 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.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Media contact: Timothy Willis

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