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Articles Tagged with: Valuation

The Why and How of a Successful SAS-to-Python Model Migration

A growing number of financial institutions are migrating their modeling codebases from SAS to Python. There are many reasons for this, some of which may be unique to the organization in question, but many apply universally. Because of our familiarity not only with both coding languages but with the financial models they power, my colleagues and I have had occasion to help several clients with this transition.

Here are some things we’ve learned from this experience and what we believe is driving this change.

Python Popularity

The popularity of Python has skyrocketed in recent years. Its intuitive syntax and a wide array of packages available to aid in development make it one of the most user-friendly programming languages in use today. This accessibility allows users who may not have a coding background to use Python as a gateway into the world of software development and expand their toolbox of professional qualifications.

Companies appreciate this as well. As an open-source language with tons of resources and low overhead costs, Python is also attractive from an expense perspective. A cost-conscious option that resonates with developers and analysts is a win-win when deciding on a codebase.

Note: R is another popular and powerful open-source language for data analytics. Unlike R, however, which is specifically used for statistical analysis, Python can be used for a wider range of uses, including UI design, web development, business applications, and others. This flexibility makes Python attractive to companies seeking synchronicity — the ability for developers to transition seamlessly among teams. R remains popular in academic circles where a powerful, easy-to-understand tool is needed to perform statistical analysis, but additional flexibility is not necessarily required. Hence, we are limiting our discussion here to Python.

Python is not without its drawbacks. As an open-source language, less oversight governs newly added features and packages. Consequently, while updates may be quicker, they are also more prone to error than SAS’s, which are always thoroughly tested prior to release.


Visualization Capabilities

While both codebases support data visualization, Python’s packages are generally viewed more favorably than SAS’s, which tend to be on the more basic side. More advanced visuals are available from SAS, but they require the SAS Visual Analytics platform, which comes at an added cost.

Python’s popular visualization packages — matplotlib, plotly, and seaborn, among others — can be leveraged to create powerful and detailed visualizations by simply importing the libraries into the existing codebase.


SAS is a command-driven software package used for statistical analysis and data visualization. Though available only for Windows operating systems, it remains one of the most widely used statistical software packages in both industry and academia.

It’s not hard to see why. For financial institutions with large amounts of data, SAS has been an extremely valuable tool. It is a well-documented language, with many online resources and is relatively intuitive to pick up and understand – especially when users have prior experience with SQL. SAS is also one of the few tools with a customer support line.

SAS, however, is a paid service, and at a standalone level, the costs can be quite prohibitive, particularly for smaller companies and start-ups. Complete access to the full breadth of SAS and its supporting tools tends to be available only to larger and more established organizations. These costs are likely fueling its recent drop-off in popularity. New users simply cannot access it as easily as they can Python. While an academic/university version of the software is available free of charge for individual use, its feature set is limited. Therefore, for new users and start-up companies, SAS may not be the best choice, despite being a powerful tool. Additionally, with the expansion and maturity of the variety of packages that Python offers, many of the analytical abilities of Python now rival those of SAS, making it an attractive, cost-effective option even for very large firms.

Future of tech

Many of the expected advances in data analytics and tech in general are clearly pointing toward deep learning, machine learning, and artificial intelligence in general. These are especially attractive to companies dealing with large amounts of data.

While the technology to analyze data with complete independence is still emerging, Python is better situated to support companies that have begun laying the groundwork for these developments. Python’s rapidly expanding libraries for artificial intelligence and machine learning will likely make future transitions to deep learning algorithms more seamless.

While SAS has made some strides toward adding machine learning and deep learning functionalities to its repertoire, Python remains ahead and consistently ranks as the best language for deep learning and machine learning projects. This creates a symbiotic relationship between the language and its users. Developers use Python to develop ML projects since it is currently best suited for the job, which in turn expands Python’s ML capabilities — a cycle which practically cements Python’s position as the best language for future development in the AI sphere.

Overcoming the Challenges of a SAS-to-Python Migration

SAS-to-Python migrations bring a unique set of challenges that need to be considered. These include the following.

Memory overhead

Server space is getting cheaper but it’s not free. Although Python’s data analytics capabilities rival SAS’s, Python requires more memory overhead. Companies working with extremely large datasets will likely need to factor in the cost of extra server space. These costs are not likely to alter the decision to migrate, but they also should not be overlooked.

The SAS server

All SAS commands are run on SAS’s own server. This tightly controlled ecosystem makes SAS much faster than Python, which does not have the same infrastructure out of the box. Therefore, optimizing Python code can be a significant challenge during SAS-to-Python migrations, particularly when tackling it for the first time.

SAS packages vs Python packages

Calculations performed using SAS packages vs. Python packages can result in differences, which, while generally minuscule, cannot always be ignored. Depending on the type of data, this can pose an issue. And getting an exact match between values calculated in SAS and values calculated in Python may be difficult.

For example, the true value of “0” as a float datatype in SAS is approximated to 3.552714E-150, while in Python float “0” is approximated to 3602879701896397/255. These values do not create noticeable differences in most calculations. But some financial models demand more precision than others. And over the course of multiple calculations which build upon each other, they can create differences in fractional values. These differences must be reconciled and accounted for.

Comparing large datasets

One of the most common functions when working with large datasets involves evaluating how they change over time. SAS has a built-in function (proccompare) which compares datasets swiftly and easily as required. Python has packages for this as well; however, these packages are not as robust as their SAS counterparts. 


In most cases, the benefits of migrating from SAS to Python outweigh the challenges associated with going through the process. The envisioned savings can sometimes be attractive enough to cause firms to trivialize the transition costs. This should be avoided. A successful migration requires taking full account of the obstacles and making plans to mitigate them. Involving the right people from the outset — analysts well versed in both languages who have encountered and worked through the pitfalls — is key.

Chart of the Month: Not Just the Economy — Asset Demand Drives Prices

Within weeks of the March 11th declaration of COVID-19 as a global pandemic by the World Health Organization, rating agencies were downgrading businesses across virtually every sector of the economy. Not surprisingly, these downgrades were felt most acutely by businesses that one would reasonably expect to be directly harmed by the ensuing shutdowns, including travel and hospitality firms and retail stores. But the downgrades also hit food companies and other areas of the economy that tend to be more recession resistant. 

An accompanying spike in credit spreads was even quicker to materialize. Royal Caribbean’s and Marriott’s credit spreads tripled essentially overnight, while those of other large companies increased by twofold or more. 

But then something interesting happened. Almost as quickly as they had risen, most of these spreads began retreating to more normal levels. By mid-June, most spreads were at or lower than where they were prior to the pandemic declaration. 

What business reason could plausibly explain this? The pandemic is ongoing and aggregate demand for these companies’ products does not appear to have rebounded in any material way. People are not suddenly flocking back to Marriott’s hotels or Wynn’s resorts.    

The story is indeed one of increased demand. But rather than demand for the companies’ productswe’re seeing an upswing in demand for these companies’ debt. What could be driving this demand? 

Enter the Federal Reserve. On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase. 

And poof! Instant demand. And instant price stabilization. All the Fed had to do was announce that it would begin buying bonds (it hasn’t actually started buying yet) for demand to rush back in, push prices up and drive credit spreads down.  

To illustrate how quickly spreads reacted to the Fed’s announcement, we tracked seven of the top 20 companies listed by S&P across different industries from early March through mid-June. The chart below plots swap spreads for a single bond (with approximately five years to maturity) from each of the following companies: 

  • Royal Caribbean Cruises (RCL)
  • BMW 
  • The TJX Companies (which includes discount retailers TJ Maxx, Marshalls, and HomeGoods, among others) 
  • Marriott 
  • Wynn Resorts 
  • Kraft Foods 
  • Ford Motor Company

We sourced the underlying data for these charts from two RiskSpan partners: S&P, which provided the timing of the downgrades, and Refinitiv, which provided time-series spread data.  

The companies we selected don’t cover every industry, of course, but they cover a decent breadth. Incredibly, with the lone exception of Royal Caribbean, swap spreads for every one of these companies are either better than or at the same level as where they were pre-pandemic. 

As alluded to above, this recovery cannot be attributed to some miraculous improvement in the underlying economic environment. Literally the only thing that changed was the Fed’s announcement that it would start buying bonds. The fact that Royal Caribbean’s spreads have not fully recovered seems to suggest that the perceived weakness in demand for cruises in the foreseeable future remains strong enough to overwhelm any buoying effect of the impending SMCCF investment. For all the remaining companies, the Fed’s announcement appears to be doing the trick. 

We view this as clear and compelling evidence that the Federal Reserve in achieving its intended result of stabilizing asset prices, which in turn should help ease corporate credit.

April Chart of the Month: COVID-19 Impact on Junior Bond Spreads

Our chart of the month presents data illustrating what has already been acutely felt by mezzanine and other subordinate bond investors – a sharp rise in spreads across all sectors coinciding with the imposition of pandemic-related lockdowns in the United States and around the world. 

Spreads on aircraft leases had already begun widening by the start of March as travel was slowing dramatically well before widespread government-imposed shutdowns began hitting other parts of the economy. Spreads on aircraft bonds soared to 1,800 basis points at the end of March and 2,400 basis points on April 25th. 

Aircraft differed from most other sectors in its spreads continued to widen throughout April. Spreads in most other sectors began reverting closer to normal in April after experiencing the March market shock. Another notable exception to this pattern were timeshare spreads, which also continued widening during April, reaching a level on April 25th four times where they were on March 2nd.  

It is not surprising to see bonds associated with the travel sector of the economy react in this way. Other sectors that did not rebound during April included student, equipment, floor plan and commercial loans. 

Widening spreads naturally correspond with price declines over the same period. 

The spreads in this chart were computed using TRACE data enhanced by RiskSpan’s Market Color application.  

Market dislocations like these are compelling an increasing number of portfolio managers to begin marking their portfolios to model rather than to market. Join us on Thursday at 1:00 p.m. for the webinar, “Valuing Hard-to-Value Bonds” for a lively discussion on some of the ramifications of this change. 

Webinar: 2020 — Entering The Decade of Data & Smart Analytics


2020 — Entering The Decade of Data & Smart Analytics

Prepare for the decade where data and analytics become the driving force behind successful investment management

For the first time in decades, Structured Finance is poised to join the rest of the financial sector in adopting new tech solutions. Deal cycles are shrinking from 3 weeks to as little as 2 days. Consequently, the market’s demand for granular collateral data has never been stronger. Accuracy and consistency are paramount. The new decade promises major advances in technology around data supporting investment best practices. Will you be ready to adopt them? 

Join industry veterans and RiskSpan executives Bill Moretti, Suhrud Dagli, and Bernadette Kogler for our latest webinar, 2020: Entering The Decade of Data & Smart Analytics.

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Key Topics:

  • Automation – How can you join asset managers aggressively seeking yield through automation? 
  • Prioritization – Which new tech will have the biggest impact on your workflow? 
  • Fit – How will you fit into the new Structured Finance ecosystem for the next decade?
  • Resistance – What has prevented the industry from adopting new tech in past decades? What has changed?

About The Hosts

Bernadette Kogler


Bernadette is co-founder, board member, and the Chief Executive Officer of RiskSpan. She is also co-founder of SmartLink Lab, RiskSpan’s innovation lab developing blockchain applications in lending and structured finance. Bernadette is an entrepreneurial leader focused on leveraging emerging technology for the advancement of data analytics and business process in the lending and structured finance markets. She leads the company’s long-term vision and strategy bringing deep expertise across the entire lending lifecycle. She is a seasoned executive and has spent most of her career focused on analytics, risk management and technology applications that provide strategic advantage to clients. Bernadette was previously with KPMG’s Mortgage and Structured Finance Practice and started her career with Prudential Insurance Company. She holds a BS in Finance from Villanova University and an MBA from Seton Hall University.  

Suhrud Dagli


Suhrud is a co-founder, board member, and the Chief Technology and Innovation Officer of RiskSpan. He is also co-founder of SmartLink Lab, an innovation lab developing blockchain applications for structured finance and the capital markets. Suhrud is one of the industry’s most respected experts in mortgage and structured product technology and leads RiskSpan’s technology strategy including the company’s SaaS offering, open source and API strategies. He is also responsible for RiskSpan’s advanced technology incubations including machine learning applications and applications developed with blockchain. Suhrud has spent his career developing solutions for the capital markets. Formerly he was Head of System & Analytics at Greenwich Capital and Head of Analytics and Model Development for UBS. Suhrud holds an MS in Computer Science from Pennsylvania State University and a BS in Electrical Engineering from VJTI, Bombay India.  

Bill Moretti, CFA, CPA, FRM

Senior Managing Director

Bill Moretti has over 30 years of experience identifying business opportunities and developing creative investment strategies & solutions for the Structured Finance industry on both the buy- and sell-sides. Bill’s expertise covers all sectors within structured finance including Agency Residential Mortgage Backed Securities (RMBS), Non-Agency RMBS, Asset Backed Securities (ABS), Collateralized Loan Obligations (CLO’s), and Commercial Mortgage Backed Securities (CMBS). In his new role as a Senior Managing Director and Lead of the Innovation Lab with RiskSpan, Bill with be focused on applications of machine learning, AI and Blockchain to improve efficiencies in lending, structured finance and the investment process. Bill holds a Bachelors of Business Administration (BBA) from Pace University in New York, NY, as well as CPA, CFA, and FRM designations.

Webinar: Modeling Techniques for Hard-to-Value Bonds


Modeling Techniques for Hard-to-Value Bonds

Learn from leading practitioners as they discuss how to model bonds whose market values do not reflect their underlying fundamentals.

The market continues to punish every category of structured finance product. Even the highest-rated securities are not immune, but the further a bond moves down the ratings scale, the greater the uncertainty around what its real valuation is. 

Mark-to-model is fast becoming the new normal as the Covid-19 crisis is causing investors to become less and less comfortable relying on normal pricing service output. But transitioning from Level 1 to Level 2 (and even sometimes Level 3) assets brings with it a host of internal compliance and other challenges.  

Modelers must be able to demonstrate that their assumptions are defensible and their techniques are sound. 

Join Bill Moretti, Scott Carnahan, and Joe Sturtevant as they discuss “Modeling Techniques for Hard-to-Value Bonds” 

Key Topics:  

  • Overview of recent cross-sector performance 
  • Considerations when having to adapt from a market-based approach to a model-based one 
  • Example illustration of how to value a CLO security using mark-to-model. 

Visualizing a CMBS Portfolio’s Exposure to COVID-19

he economic impact of the Coronavirus outbreak is all but certain to be felt by CMBS investors. The only real uncertainty surrounds when missed rent payments will begin, what industries are likely to feel them most acutely, and—more to the point—how your portfolio aligns with these eventualities.

The dashboard below—created using RS Edge and Tableau—displays a stylized example compiling small random excerpts from several CMBS portfolios. While business disruptions have not (yet) lasted long enough to be reflected in CMBS default rates, visualizing portfolios in this way provides a powerful tool for zeroing in on where problems are most likely to emerge.

The maps at the top of the dashboard juxtapose the portfolio’s geographic concentration with states where COVID-19 prevalence is highest. Investors are able to drill down not only into individual states but into individual NAICS-defined industries that the loans in their deals cover.

At each level of analysis (overall, by state, or by industry) the dashboard not only reports total exposure in UPB but also important risk metrics around the portfolio’s DSCR and LTV, thus enabling investors to quickly visualize how much cushion the underlying loans have to absorb missed rent payments before the deals begin to experience losses.


The real value of visualizations like these, of course, is the limitlessness of their flexibility and their applicability to any market sector.

We sincerely desire to be helpful during these unprecedented market conditions. Our teams are actively helping clients to manage through them. Whether you are looking for historical context, market analysis or just a conversation with folks who have been through several market cycles, we are here to provide support. Please contact us to talk about what we can do for you.

Managing Coronavirus-Related Risks in Aircraft Lease ABS

Last week, UK airline Flybe grounded its planes, stranded passengers and filed for bankruptcy protection, as the struggling carrier was buffeted by a Coronavirus-related slowdown in demand. Flybe’s demise is a trenchant example of the implications of Coronavirus for investors in the aircraft sector, including aircraft lease ABS investors whose cash flow depends on continued lease payments from various global carriers. Of course, the impacts of Coronavirus will vary, with some countries, servicers, credit-rating sectors and deal structures worse off than others. Using RS Edge, aircraft lease ABS investors can drill down into collateral to see country, carrier and aircraft exposures, stress test deals and learn potential fault lines for deals as Coronavirus uncertainty looms. 

The snapshot below illustrates how clients can benefit from RiskSpan and Intex data and analysis in this sector. Using its embedded Tableau functionality, RS Edge can quickly show investors the top country, carrier and aircraft exposures for each deal. Investors concerned about carriers that might be increasingly vulnerable to Coronavirus disruption (such as Italian or Asian airlines today and likely others in coming weeks) can determine the exposure to these countries using the platform. In addition, when news is announced that impacts the credit quality of carriers, investors can view exposure to these carriers and, with additional analysis, calculate the potential residual value of individual aircraft if the carrier goes bankrupt or the lease terminates. RiskSpan can also provide data on exposure to aircraft manufacturers and provide valuation of bonds backed by aircraft leases.  


Contact us to learn more about how RiskSpan helps clients manage their airline (and other risk) exposure and how we can assist with customized requests to perform further analysis requiring add-on data or calculations.

RS Edge for Loans & Structured Products: A Data Driven Approach to Pre-Trade and Pricing  

The non-agency residential-mortgage-backed-securities (RMBS) market has high expectations for increased volume in 2020. Driven largely by expected changes to the qualified mortgage (QM) patch, private-label securities (PLS) issuers and investors are preparing for a 2020 surge. The tight underwriting standards of the post-crisis era are loosening and will continue to loosen if debt-to-income restrictions are lifted with changes to the QM patch 

PLS programs can differ greatly. It’s increasingly important to understand the risks inherent in each underlying poolAt the same time, investment opportunities with substantial yield are becoming harder to find without developing a deep understanding of the riskier components of the capital structureA structured approach to pre-trade and portfolio analytics can help mitigate some of these challenges. Using a data-driven approach, portfolio managers can gain confidence in the positions they take and make data influenced pricing decisions 

Industry best practice for pre-trade analysis is to employ a holistic approach to RMBS. To do this, portfolio managers must combine analysis of loan collateral, historical data for similar cohorts of loans (within previous deals), and scenariofor projected performance. The foundation of this approach is:  

  • Historical data can ground assumptions about projected performance 
  • A consistent approach from deal to deal will illuminate shifting risks from shifting collateral 
  • Scenario analysis will inform risk assessment and investment decision  

Analytical Framework 

RiskSpan’s modeling and analytics expert, Janet Jozwik, suggests a framework for analyzing a new RMBS deal with analysis of 3 main components:  deal collateral, historical performance, and scenario forecasting. Combined, these three components give portfolio managers a present, past, and future view into the deal.  

Present: Deal Collateral Analysis 

Deal collateral analysis consists of: 1) a deep dive into the characteristics of the collateral underlying the deal itself, and 2) a comparison of the collateral characteristics of the deal being analyzed to similar deals. A comparison to recently issued deals can highlight shifts in underlying collateral risk within a particular shelf or across issuers.  

Below, RiskSpan’s RS Edge provides the portfolio manager with a dashboard highlighting key collateral characteristics that may influence deal performance. 

Example 1Deal Profile Stratification 


Example 2Deal Comparative Analysis 

Past: Historical Performance Analysis 

Historical analysis informs users of a deal’s potential performance under different scenarios by looking at how similar loan cohorts from prior deals have performedJozwik recommends analyzing historical trends both from the recent past and frohistorical stress vintages to give a sense for what the expected performance of the deal will be, and what the worst-case performance would be under stress scenarios. 

Recent Trend Analysis:  Portfolio managers can understand expected performance by looking at how similar deals have been performing over the prior 2 to 3 years. There are a significant number of recently issued PLS that can be tracked to understand recent prepayment and default trends in the market. While the performance of these recent deals doesn’t definitively determine expectations for a new deal (as things can change, such as rate environment), it provides one data point to help ground data-driven analyses. This approach allows users to capitalize on the knowledge gained from prior market trends.  

Historical Vintage Proxy Analysis:  Portfolio managers can understand stressed performance of the deal by looking at performance of similar loans from vintages that experienced the stress environment of the housing crisisThough potentially cumbersome to execute, this approach leverages the rich set of historical performance data available in the mortgage space 

For a new RMBS Dealportfolio managers can review the distribution of key features, such as FICO, LTV, and documentation typeThey can calculate performance metrics, such as cumulative loss and default rates, from a wide set of historical performance data on RMBS, cut by vintage. When pulling these historical numbers, portfolio managers can adjust the population of loans to better align with the distribution of key loan features in the deal they are analyzing. So, they can get a view into how a similar loans pool originated in historical vintages, like 2007, performed. There are certainly underwriting changes that have occurred in the post-crisis era that would likely make this analysis ultraconservative. These ‘proxy cohorts’ from historical vintages can provide an alternative insight into what could happen in a worst-case scenario.  

Future: Forecasting Scenario Analysis 

Forecasting analysis should come in two flavors. First, very straightforward scenarios that are explicitly transparent about assumptions for CPR, CDR, and severity. These assumptions-based scenarios can be informed with outputs from the Historical Performance Analysis above.  

Second, forecasting analysis can leverage statistical models that consider both loan features and macroeconomic inputs. Scenarios can be built around macroeconomic inputs to the model to better understand how collateral and bond performance will change with changing economic conditions.  Macroeconomic inputs, such as mortgage rates and home prices, can be specified to create particular scenario runs. 

How RiskSpan Can Help 

Pulling the required data and models together is typically a burdenRiskSpan’s RS Edge has solved these issues and now offers one integrated solution for:  

  • Historical Data: Loan-level performance and collateral data on historical and pre-issue RMBS deals 
  • Predictive Models: Credit and Prepayment models for non-agency collateral types 
  • Deal Cashflow Engine: Intex is the leading source for an RMBS deal cashflow library 

There is a rich source of data, models, and analytics that can support decision making in the RMBS market. The challenge for a portfolio manager is piecing these often-disparate pieces of information together to a cohesive analysis that can provide a consistent view from deal to dealFurther, there is a massive amount of historical data in the mortgage space, containing a vast wealth of insight to help inform investment decisions. However, these datasets are notoriously unwieldy. Users of RS Edge cut through the complications of large, disparate datasets for clear, informative analysis, without the need for custom-built technology or analysts with advanced coding skills.

Introducing: RS Edge for Loans and Structured Products

RiskSpan Introduces RS Edge for Loans and Structured Products  

RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products. 

RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products.  Users can derive insights from historical trends and powerful predictive forecasts under a range of economic scenarios on our cloud-native solution. RS Edge streamlines analysis by bringing together key industry data and integrations with leading 3rd party vendors. 

An on-demand team of data scientists, quants, and technologists with fixed-income portfolio expertise support the integration, calibration, and operation across all RS Edge modules 

RMBS Analytics in Action 

RiskSpan has developed a holistic approach to RMBS analysis that combines loan collateral, historical, and scenario analysis with deal comparison tools to more accurately predict future performance. Asset managers can define an acceptable level of risk and ground pricing decisions with data-driven analysis. This approach illuminates risk from shifting collateral and provides investors with confidence in their positions. 

Loan Analytics in Action 

Whole loan asset managers and investors use RiskSpan’s Loan Analytics to enhance and automate partnerships with Non-Qualified Mortgage originators and servicers. The product enhances the on-boarding, pricing analytics, forecasting, and storage of loan data for historical trend analytics. RS Edge forecasting analytics support ratesheet validation and loan pricing 

About RiskSpan 

RiskSpan provides innovative technology and services to the financial services industry. Our mission is to eliminate inefficiencies in loans and structured finance markets to improve investors’ bottom line through incremental cost savings, improved return on investment, and mitigated risk.  

RiskSpan is holding a webinar on November 6 to show how RS Edge pulls together past, present, and future for insights into new RMBS deals. Click below to register.

Case Study: RS Edge – Analytics and Risk

The Client

Large Life Insurance Company – Investment Group


The Problem

The Client was shopping around for an analytics and risk platform to be used by both the trading desk and risk managers.

RiskSpan Edge Platform enabled highly scalable analytics and risk modeling providing visibility and control to address investment analysis, risk surveillance, stress testing and compliance requirements.

The Solution

Initially, the solution was intended for both the trading desk (as pre-trade analysis) as well as risk management (running scenarios on the existing portfolio).  Ultimately, the system was used exclusively by risk management and used heavily by mid-level risk management. 

Cloud Native Risk Service

We have transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speed and cost never before possible in the industry.

Perform advanced portfolio analysis to achieve risk oversight and regulatory compliance with confidence. Access reliable results with cloud-native interactive dashboards that satisfy investors, regulators, and clients.

Two Flexible Options
Fund Subscriber Service + Managed Service

Each deployment option includes on-demand analytics, standard batch and over-night processing or a hybrid model to suit your specific business needs. Our team will work with customers to customize deployment and delivery formats, including investor-specific reporting requirements.

Easy Integration + Delivery
Access Your Risk

Accessing the results of your risk run is easy via several different supported delivery channels. We can accommodate your specific needs – whether you’re a new hedge fund, fund-of-funds, bank or other Enterprise-scale customer.

“We feel the integration of RiskSpan into our toolkit will enhance portfolio management’s trading capabilities as well as increase the efficiency and scalability of the downstream RMBS analysis processes.  We found RiskSpan’s offering to be user-friendly, providing a strong integration of market / vendor data backed by a knowledgeable and responsive support team.”

The Deliverables

  • Enabled running various HPI scenarios and tweaked the credit model knobs to change the default curve, running a portfolio of a couple hundred non-agency RMBS
  • Scaling the processing power up/down via the cloud, and they would iterate through runs, changing conditions until they got the risk numbers they needed
  • Simplified integration into their risk reporting system, external to RiskSpan

Get Started
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