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Webinar: MSR Trading Insights

Webinar: Tuesday, March 25th | 1:00 ET 
MSR Bulk Trading Insights

Register Now
Join us for an update from MBA’s Chief Economist, Michael Fratantoni, on the current state of the MSR market.

Then, stick around for actionable strategies from RiskSpan’s Chris Kennedy and Dan Fleishman on how to gain a competitive edge, including:

– How to effectively leverage strategic bidding to maximize outcomes.
– The importance of on-the-fly, ad hoc analysis in responding to market dynamics.
– Best practices for MSR valuations and trading analytics to ensure precise decision-making.

Whether you’re scaling your MSR portfolio or seeking to optimize your trading processes, this webinar will equip you with the tools and insights to stay ahead in a competitive landscape.

Panelists
Michael Fratantoni, Chief Economist, Mortgage Bankers Association

Chris Kennedy, Director, RiskSpan

Dan Fleishman, Head of Client Success, RiskSpan

ReGISTER

RiskSpan Introduces Enhanced Non-QM Prepayment Model Leveraging Loan-Level Data

Arlington, VA – February 18, 2025 – RiskSpan, a leading provider of innovative trading, risk management and data analytics for loans, securities and private credit, has announced the release of its latest Non-QM Prepayment Model (Version 3.11), incorporating CoreLogic’s loan-level non-QM performance data. This update significantly enhances prepayment forecasting accuracy for non-QM loans and mortgage-backed securities by leveraging a robust, segmented modeling approach.

RiskSpan’s new non-QM prepayment model introduces a two-component framework that improves the precision of prepayment predictions:

  • The first component is a Unified Turnover Model, designed to capture base prepayment trends.
  • The second component, a Refinance Model Categorized by Documentation Type, is capable of distinguishing among and modeling behavioral characteristics specific to bank statement, debt service coverage ratio/investor, full documentation, and other documentation types

The model is built on loan performance data spanning October 2019 to March 2024 and intelligently incorporates long-term prepayment behavior with conventional loans, addressing the challenge of limited non-QM data history. Key enhancements include:

  • Sensitivity to SATO (Spread at Origination) and Burnout Effects, refining prepayment behavior projections.
  • DSCR-Specific Adjustments, incorporating prepayment penalty terms and amounts to refine refinance calculations.

By integrating granular loan-level insights from CoreLogic, this release enhances market participants’ ability to accurately assess non-QM prepayment risk, optimize portfolio strategies, and improve secondary market pricing.

“Our latest model delivers a more precise view of non-QM borrower behavior, equipping market participants with the insights needed to manage risk effectively,” said Divas Sanwal, Senior Managing Director and RiskSpan’s Head of Modeling. “By leveraging CoreLogic’s expansive dataset and an expansive GSE dataset, we’re enabling investors to better anticipate prepayment trends and make more informed decisions.” The new model is now available for integration into RiskSpan’s Platform.

The new model is now available for integration into RiskSpan’s Platform.

About RiskSpan

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions.   Learn more at www.riskspan.com.

RiskSpan Launches Comprehensive MSR Analytics Solution

Arlington, VA – January 25, 2025 – RiskSpan, a leading technology provider of innovative risk management and data analytics for loans, securities and private credit, today announced the launch of its state-of-the-art MSR Analytics Solution, available through RiskSpan’s Edge Platform. This integrated, end-to-end data and analytics solution revolutionizes how mortgage servicing rights (MSRs) are analyzed, managed, and priced.

The solution is uniquely positioned to serve the needs of MSR traders and investors, offering capabilities tailored to agency, non-QM, and jumbo loans. It combines granular loan-level historical performance analysis, advanced machine learning models for tape cracking, and customizable scenario testing, all on a secure, fast, and scalable, cloud-native platform.

Key Features of the MSR Analytics Solution

  1. Loan-Level Analysis and Insights:
    Users can interactively query and filter loan data, create customized cohort stratifications, and access detailed historical performance metrics such as prepayment, default, and recapture rates. Visual reports and data queries are seamlessly integrated into Snowflake for enhanced accessibility and efficiency​.
  2. Streamlined Data Mapping and Consolidation:
    The platform’s Smart Mapper technology simplifies the process of loading and mapping portfolios from multiple servicers, saving hours of manual work. RiskSpan’s advanced QC rules and machine learning models further enhance data precision and reliability​.
  3. Robust MSR Pricing Models:
    RiskSpan’s loan-level MSR pricing models significantly reduce pricing errors by offering granular cash flow forecasts, option-adjusted valuations, and segmentation capabilities. The in-house modeling team continuously updates the tools to ensure accuracy and reliability​.
  4. Advanced Risk Analysis and Scenario Testing:
    Users can run multiple interest rate and pricing scenarios to explore a range of potential MSR valuations. The platform’s customizable interface supports automated overnight analytics, integrates with enterprise risk systems, and enhances decision-making confidence for buy/sell strategies​.

A Game-Changer for the MSR Market

“RiskSpan’s MSR Analytics Solution represents a significant step forward in delivering actionable insights to MSR portfolio managers,” said Chris Kennedy, Director of Sales at RiskSpan. “This new technology allows clients to navigate the complexities of the MSR market with precision and confidence. As the only commercial-grade MSR cash flow model that leverages GSE historical performance data, it offers unmatched transparency into market CPR speeds, delivering a comprehensive view of portfolio performance over time. I consider this to be the ‘secret sauce’ of our MSR Platform.” 

This solution empowers servicers, MSR sellers, MSR investors, and other stakeholders to make data-driven decisions, optimize portfolio performance, and meet critical deadlines with improved accuracy and speed.

For more information about RiskSpan’s Edge Platform and the new RTL functionality, please visit RiskSpan.com.

About RiskSpan

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions.   Learn more at www.riskspan.com.

How RiskSpan Helped a Credit-Focused Investment Management Firm Transition to Snowflake

A leading investment management firm and recognized leader in structured credit, including asset-backed securities (ABS), mortgage-backed securities (MBS), and other fixed-income sectors, sought RiskSpan’s help transitioning key data processing functions from the data management platform 1010data to Snowflake.

The ability to share data with partners using the same system in which the analytics are performed made the combination of RiskSpan and Snowflake especially attractive. The shift provided significant operational and financial benefits to the client, marking another successful milestone in RiskSpan’s history of helping clients optimize their data management.

Converting Key Functionalities from 1010data to Snowflake

The company had been relying on 1010data for several critical timeseries-based calculations. However, the limitations of the platform—both in terms of speed and cost—prompted them to seek a more modern solution. RiskSpan worked closely with them to replicate and enhance key functionalities using Snowflake. Converted functionalities included:

  1. Timeseries-Based Calculations: We re-engineered these to operate efficiently within Snowflake’s cloud-native architecture, maintaining accuracy while enhancing processing speeds.
  2. fill_nearest: This function retrieves the nearest non-N/A value within a group. It was implemented seamlessly using Snowflake’s window functions, preserving data integrity while boosting performance.
  3. rolling_sum: Snowflake’s SQL capabilities were leveraged to implement the moving sum of valid (non-N/A) values within a window. This provided the company with more responsive and scalable time-series analysis capabilities.
  4. cumulative_run_length: The cumulative run length within a group was translated into Snowflake’s environment using efficient SQL queries, making the entire process faster and more robust.

Integration Capabilities

In addition to replicating 1010data’s core functionalities, the company sought to expand its data capabilities by integrating additional datasets such as Market Data and Home Price Indices (HPI). We showed them how to incorporate and analyze these datasets within Snowflake’s environment, further enhancing their decision-making capabilities.

This cross-functional integration was pivotal in showcasing Snowflake’s ability to streamline complex data workflows. By integrating third-party data directly into their ecosystem, our client can now generate more insightful reports and conduct deeper analysis across multiple datasets without leaving the Snowflake platform.

The Benefits of Transitioning to Snowflake

Our client experienced several immediate and impactful benefits by transitioning from 1010data to Snowflake were immediate and impactful. These included:

  • Complete Replacement of 1010data: With all critical functionalities successfully converted, the company now can fully discontinue their reliance on 1010data. This eliminates the need for maintaining multiple platforms and simplifies their technology stack.
  • Significant Cost Savings: Discontinuing 1010data relieved our client of the high costs associated with the platform’s licensing and maintenance fees. Snowflake’s cost-efficient pricing model has already resulted in substantial savings for the company.
  • Improved Processing Speeds: One of the most noticeable changes has been the drastic improvement in the company’s processing times. Snowflake’s optimized cloud infrastructure provides faster data processing and querying capabilities, significantly reducing time-to-insight.
  • Access to Full Snowflake Feature Set: Moving to Snowflake has enabled the company to take advantage of features such as data sharing, enhanced security, and elasticity. Snowflake’s built-in scalability ensures our client’s data infrastructure will continue to grow effortlessly as its data needs expand.
  • Speed and Cost Efficiency: The company has expressed particular satisfaction with both the speed and cost-efficiency of the Snowflake platform. The reduction in data processing time and cost per query has positively impacted its business operations.

Partnering with RiskSpan not only enabled the company to replace 1010data with a more modern and efficient platform, but it has also empowered them to take advantage of Snowflake’s newest, advanced features, including AI.

Contact us to learn how RiskSpan can help you unlock the full potential of your data by guiding you through complex transitions and helping you implement scalable, secure, and cost-effective solutions.

Private Credit Primer Series: Insights for Investors

We are delighted to announce the release of RiskSpan’s series of Private Credit Primers aimed at providing investors with essential knowledge about the diverse and growing landscape of the loan types that private credit investors are buying. These primers offer at-a-glance insights into the mechanics, performance expectations, and unique features of various asset classes, enabling investors to make informed decisions in this dynamic market.

The first three primers in the series are available now: They focus on Residential Transition Loans (RTLs), Personal Loans, and HELOCs — loan types that are becoming increasingly popular in the private credit space.

Residential Transition Loans (RTLs) (full primer here)

RTLs are short-term loans designed to help borrowers bridge financial gaps during transitional periods in residential real estate. Often used for construction, bridge, and relocation purposes, RTLs typically have terms ranging from 6 to 36 months and feature higher interest rates than traditional long-term financing. These loans play a crucial role for both homeowners and real estate investors, especially in markets where property values fluctuate or where short-term liquidity is needed.

  • Important Features: RTLs often involve draw functionality, allowing borrowers to access funds incrementally as projects progress. Another key aspect is the use of the “As-Repaired” Value (ARV) to calculate Loan-to-Value (LTV) ratios, based on the projected value of the property after repairs.
  • Performance Considerations: While RTLs have performed well during periods of stable or rising home prices, the primer cautions that these loans are more vulnerable during economic downturns or periods of home price decline​.

Personal Loans (full primer here)

Personal loans can be secured or unsecured and are used for a variety of purposes, including debt consolidation, medical expenses, and large purchases. They are repaid in fixed monthly installments over a predetermined period.

  • Important Features: The primer highlights key modeling considerations for personal loans, such as static default/prepayment assumptions, which rely on historical data to predict future loan performance based on factors like loan age and borrower profiles.
  • Performance Expectations: As of 2024, the delinquency rate for personal loans stands at around 3.38%, with average interest rates hovering around 12.42%, though they can vary widely depending on market conditions and borrower credit quality​.

HELOCs (full primer here)

The complexity of modeling HELOCs stems from their sharing characteristics of both a mortgage and a credit card. Reliable assumptions about borrower behavior both during the draw period (when balances can move in either direction at any time) and during the post-draw, repayment-only period are crucial to forecasting correct cash flows.

  • Important Features: Understanding regional variations and borrower characteristics can provide deeper insights into HELOC performance, helping to refine risk models and lending strategies.
  • Performance Expectations: The delinquency rate for HELOCs can vary based on factors such as economic conditions, borrower credit quality, and market trends. However, historically, the delinquency rate for HELOCs tends to be lower than for unsecured loans or credit cards.

What to Expect from the Series

Each primer in the series will not only break down the mechanics of the loan type but also provide performance insights and modeling considerations. With the ongoing volatility in the financial markets, these primers will explore how various asset classes perform under different economic conditions, such as rising interest rates, declining home prices, or increasing unemployment.

By offering practical, data-driven insights, the Private Credit Primer series will serve as an invaluable resource for private credit investors who are looking to deepen their understanding of these asset classes and navigate potential risks effectively.

Stay tuned for more primers in this series, as we continues to expand RiskSpan’s library of resources for private credit investors!

Enhancing a HELOC Lender’s Operations with RiskSpan’s Data as a Service (DaaS)

A leading fintech company specializing in home equity lines of credit (HELOCs), was seeking to optimize the management of its data operations. To accomplish this, the company turned to RiskSpan, a leader in data analytics and financial technology solutions. Through a tailored Data as a Service (DaaS) offering, RiskSpan helped the company improve its HELOC business operations by providing advanced data management and modeling capabilities.

Challenges

The company sought to enhance its HELOC operations in two critical areas:

  1. Data Management and Integration: The company was dealing with complex data sets from multiple sources, including credit bureaus, property data, and customer behavior insights. Integrating and managing this data effectively was crucial for making informed lending decisions.
  2. Risk Assessment and Modeling: Accurate and reliable risk assessment models were necessary for evaluating customer behavior and predicting loan performance. The company required a solution that could model draw behavior and other variables specific to HELOCs.

RiskSpan’s DaaS Solution

RiskSpan’s DaaS offering provided the company with a comprehensive solution tailored to address these challenges. The key components of the solution included:

  1. Advanced Data Integration: RiskSpan’s DaaS platform seamlessly integrated the company’s various data sources, enabling a more streamlined and efficient data management process. This integration allowed the company to better understand their borrowers and make more informed lending decisions.
  2. Enhanced Loan-Level HELOC Pricing and Projections: The client successfully loaded its historical loan performance data onto RiskSpan’s DaaS platform and established a monthly process within the platform’s flexible data warehouse. Using the embedded historical performance tool, the client analyzed loan-level behavior across its portfolio. This enabled the client to generate detailed collateral performance reports for investors and rating agencies, as well as leverage these insights to enhance future projections and loan-level pricing for new loans.
  3. Cost-Effective Data Services: RiskSpan also identified an opportunity to replace the client’s existing data services provider at a significantly reduced cost. By offering a more competitive pricing structure while maintaining high-quality data services, RiskSpan positioned the client to achieve substantial cost savings, making them more competitive in the HELOC market.

Outcomes and Benefits

Implementing RiskSpan’s DaaS solution brought several key benefits:

  • Improved Decision-Making: With better-integrated data and more accurate modeling of HELOC draw behavior, the client could make more informed lending decisions, ultimately reducing risk and enhancing profitability.
  • Operational Efficiency: The streamlined data management process allowed the client to operate more efficiently, freeing up resources to focus on core business activities.
  • Cost Savings: RiskSpan’s competitive pricing enabled the client to cut costs significantly, improving their bottom line and allowing them to reinvest in other areas of the business.

RiskSpan’s Data as a Service solution provided the clients with the tools it needed to optimize its HELOC business. By addressing its data integration challenges, improving risk assessment through advanced modeling, and offering a cost-effective alternative to existing data services, RiskSpan helped the client strengthen its market position and enhance overall business performance.

Preparing For Impact: How Will Non-QM Prepay Speeds React to Lower Rates?

In a recent post, we addressed some of the less obvious ways in which a lower interest rate environment is likely to impact an agency universe with such a large volume of loans that are still out-of-the-money to refinance. In this post, we turn our attention to non-QM loans, whose unique characteristics mean they will likely feel the coming rate cuts differently.

Understanding the Distinctive Prepayment Dynamics of Non-QM Loans

Non-QM loans cater to borrowers who do not meet the stringent criteria of traditional agency loans, often due to factors like non-standard income documentation, credit issues, or investment property financing. Non-QM loans generally carry higher interest rates, and, unlike their agency counterparts, many have prepayment penalties designed to protect lenders from early payoff risk. Non-QM loans are also more likely than agency loans to involve investment properties – and thus, the underlying mortgages are not subject to the same “ability to repay” constraints that apply to agency/QM loans.

All these factors play a role in forecasting prepay speeds.

As rates decline, the incentive for some non-QM borrowers to refinance should increase, but several unique factors will shape the extent to which borrowers respond to this incentive:

  1. Prepayment Penalties: Many non-QM loans, especially those structured as Debt Service Coverage Ratio (DSCR) loans for investment properties, include prepayment penalties that can deter refinancing despite a favorable rate environment. These penalties vary widely, from a fixed percentage over a set period to declining penalties over time. The economic calculus for borrowers will hinge on whether the potential savings from refinancing outweigh these penalties
  2. Diverse Loan Structures: The non-QM market includes a variety of loan products, such as 40-year terms, hybrid ARMs and loans with interest-only periods, reminiscent of the pre-2008 lending landscape. This diversity means that not all non-QM loans will see the same incentive to refinance and the slope of the mortgage curve will matter. For example, loans with higher rates are likely to exhibit a stronger refinance response, particularly as the shape of the mortgage rate curve plays a significant role, with hybrid ARMs resetting off short-term rates and 30-year fixed-rate mortgages being influenced by movements in the 10-year Treasury yield
  3. Interest Rate Spread Compression: Historically, the spread between non-QM and agency mortgage rates has varied significantly, ranging from 100 to 300 basis points. A narrowing of this spread, driven by falling rates, could heighten the refinance incentive for non-QM borrowers, leading to faster prepayment speeds. However, the extent of this spread compression is uncertain and will depend on broader market dynamics. Souring economic conditions, for example, would likely contribute to a widening of spreads.

Key Factors Influencing Non-QM Prepayment Speeds

Loan Characteristics and Documentation Types

Non-QM loans can vary significantly by documentation type, such as full documentation, bank statements, or DSCR. Historically, as illustrated in the following chart, full documentation loans have shown faster prepayment speeds, because these borrowers are closer to qualifying for agency refinancing options as rates drop.

S-Curves by Doc Type (Full vs. Alt. vs. Bank Statement vs. DSCR)

Unlike agency mortgages, which include a substantial volume of loans originated at much lower rates, the non-QM market predominantly consists of loans originated in the past few years when rates were already elevated. As a result, a larger portion of non-QM loans is closer to being “in the money” for refinancing. This distinction suggests that the non-QM sector may see a more pronounced increase in prepayment activity compared to agency loans, where the lock-in effect remains stronger.

S-Curve (line) vs UPB (bars) by Refi Incentive

Economic Sensitivity to Rate Moves

For many non-QM borrowers, the primary barrier to agency loan qualification—whether credit score, income documentation, or property type—remains static despite lower rates. Thus, while a rate cut could improve the appeal of refinancing into another non-QM product, it might not significantly shift these borrowers towards agency loans. However, as noted, those closer to the threshold of agency eligibility could still be enticed to refinance if the rate spread and penalty structures align favorably.

Conclusion

The coming interest rate cuts are poised to influence the non-QM market in unique ways, with prepayment speeds likely to increase as borrowers seek to capitalize on lower rates. However, the interplay of rate spreads, prepayment penalties, and diverse loan structures will create a complex landscape where not all non-QM loans will behave uniformly. For lenders and investors, understanding these nuances is crucial to accurately forecasting prepayment risk and managing portfolios in a changing rate environment.

As the market evolves, ongoing analysis and model updates will be essential to capturing the shifting dynamics within the non-QM space, ensuring that investors and traders are well-prepared for the impacts of the anticipated rate cuts. Contact us to learn how RiskSpan’s Edge Platform is helping a growing number of non-QM investors get loan-level insights like never before.

RiskSpan Expands Private Credit Solution to Include Residential Transition Loans

Arlington, VA – July 18, 2024 – RiskSpan, a leading technology provider of innovative risk management and data analytics for securities, loans and private credit, today announced the addition of Residential Transition Loans, to its award-winning Edge Platform. This enhancement enables loan and private credit investors to seamlessly upload, model, and analyze cash flow projections for fix/flip, ground-up construction, bridge and other loans with distinctive RTL features, further solidifying RiskSpan’s commitment to delivering comprehensive and versatile solutions to the private credit market.

The integration of RTLs into the Edge Platform offers investors  an unprecedented level of flexibility and precision in managing and evaluating complex loan portfolios. The new capability permits lenders to model several loan features characteristic of RTLs, including:

  • Draw Schedules on Undisbursed Loan Amounts: Investors can now account for staggered disbursement schedules, allowing for detailed modeling of cash flows based on actual loan drawdown patterns.
  • Extended Maturity Dates and Extension fees: The Platform accommodates assumptions around extension of maturity dates, ensuring investors and lenders can extend terms as necessary and model the impact on cash flows.
  • Interest-Only Contract Terms: The Platform supports loans with interest-only payment structures, providing the ability to model and project cash flows accurately.
  • “Dutch” Loan Features: RiskSpan now supports loans where interest is charged on both disbursed and undisbursed loan amounts, offering a comprehensive view of interest accruals and cash flow projections.

“By adding RTLs to the Platform, we are providing loan and private credit investors with powerful tools to navigate the complexities of these unique loan products,” said Bernadette Kogler, CEO of RiskSpan. “This enhancement aligns with our mission to equip our clients with the most advanced and flexible solutions for managing and analyzing their loan portfolios.”

These new capabilities are designed to meet the evolving needs specifically of loan and private credit investors, offering a seamless integration process and user-friendly interface. This latest addition underscores RiskSpan’s dedication to continuous innovation in this market.

For more information about RiskSpan’s Edge Platform and the new RTL functionality, please visit RiskSpan.com.

About RiskSpan

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions.  Learn more at www.riskspan.com.

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.

How RiskSpan and Snowflake Helped a Large Insurance Company Revolutionize Its Data Management

Background

Asset managers are increasingly turning to Snowflake’s cloud infrastructure to address the limitations of outdated databases. Migrating to Snowflake grants them access to a sustainable and secure platform that enables efficient data storage, processing, and analytics. This transition empowers asset managers to streamline operations, improve data accessibility, and reduce costs associated with maintaining on-premises infrastructure.

Client Challenge

A large insurance company’s asset management team was seeking to improve its approach to data management in response to its increasingly complex investment portfolio. The company recognized that transitioning to Snowflake would serve as a foundation for sustainable data analysis for years to come.

Desiring a partner to assist with the transition, the life insurer turned to RiskSpan – a preferred Snowflake partner with substantial experience in database architecture and management.

Specifically, the insurance company sought to achieve the following:

Systems Consolidation: Data stored across multiple transactional systems had contributed to data fragmentation and inefficiencies in data retrieval and analysis. The client sought to establish and maintain a consistent source of asset data for enterprise consumption and reporting.

Improved Reporting Capabilities: Quantifying full risk exposures in fast-moving situations proved challenging, leaving the institution vulnerable to unforeseen market fluctuations. Consequently, the client sought to improve its asset evaluation and risk assessment process by incorporating comprehensive look-through data and classification information. The need for various hierarchical classifications further complicated data access and reporting processes which required streamlining the process of producing ad-hoc exposure reports, which often required several weeks and involved teams of people.

Reduction of Manual Processes: The client needed more automated data extraction processes in order to create exposure reports across different asset classes in a more time-efficient manner with less risk of human error. 

Reduction of Infrastructure Constraints: On-premise infrastructure had defined capacity limitations, hindering scalability and agility in data processing and analysis.

RiskSpan’s Approach and Solutions Implemented

Collaborative Partnership: RiskSpan worked closely with the client’s IT, risk management, and analytics teams throughout the project lifecycle, fostering collaboration and ensuring alignment with organizational goals and objectives.

Comprehensive Assessment: Together, we conducted a thorough assessment of the client’s existing data infrastructure, analytics capabilities, and business requirements to identify pain points and opportunities for improvement.

Strategic Planning: Based on the assessment findings, the collective team developed a strategic roadmap outlining the migration plan to the unified data platform, encompassing asset data consolidation, portfolio analytics enhancement, and reporting automation.

Unified Data Platform: Leveraging modern technologies, including cloud-based solutions and advanced analytics tools, RiskSpan orchestrated the integration of various data sources and analytics capabilities. Together, we consolidated asset data from various transactional systems into a unified data platform, providing a single source of truth for comprehensive asset evaluation and risk assessment.

Data Lineage Tracking: The team employed dbt Labs tools to build, validate, and deploy flexible reporting solutions from the Snowflake cloud infrastructure.  This enabled the tracking of data lineage, adjustments, and ownership.

Daily Exposure Reporting: Leveraging automated analytic pipelines, we enabled real-time generation of exposure reports across different asset classes, enhancing the client’s ability to make timely and informed decisions.

Automated Data Extraction: We automated the data extraction processes, reducing manual intervention and streamlining data retrieval, cleansing, and transformation workflows.

Hierarchical Classification Framework: We implemented a hierarchical classification framework, providing standardized and consistent data hierarchies for improved data access and reporting capabilities.

Transformative Outcomes

Enhanced Decision-making: Implementing advanced analytics capabilities and exposure reporting empowered our client to make informed decisions more quickly, mitigating risks and capitalizing on market opportunities.

Operational Efficiency: Automation of data extraction, analytics modeling, and reporting processes resulted in significant operational efficiencies, reducing time-to-insight and enabling resource reallocation to strategic initiatives.

Scalability and Agility: The migration to a cloud-based infrastructure provides scalability and agility, allowing our client to adapt quickly to changing business needs and accommodate future growth without infrastructure constraints.

Data Governance and Compliance: The implementation of standardized hierarchical classifications strengthened data governance and compliance, ensuring data consistency, integrity, and regulatory adherence. By leveraging Snowflake’s scalable architecture and advanced features, this large asset manager is now positioned to maneuver both its current and future data landscapes. The implementation of Snowflake not only streamlined data management processes but also empowered the organization to extract valuable insights with unprecedented efficiency. As a result, the asset manager can make data-driven decisions confidently, enhance operational agility, and drive sustainable growth in a rapidly evolving market landscape.

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