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RiskSpan Ranks in Chartis Research RiskTech 100 2019

RiskSpan is excited to announce we have ranked on the RiskTech 100 report by Chartis Research. This represents a notable rise of fourteen spots compared to 2018. The Chartis RiskTech 100 analyzes firms in the risk technology space, and serves as one of the most trusted reports for clear and reliable information about the risktech space and the exciting new developments coming out of it. This jump in the rankings represents one of the largest gains in this year’s report, and reflects RiskSpan’s focus on applying innovative technology to our core offerings. RiskSpan provides a data, modeling, and analytics Platform and Services to the finance industry – including the commercial banking, insurance, and capital markets sub-segments. Our flagship data/modeling/forecasting/valuation software, the RiskSpan Edge Platform, is a cloud-native system for hosting loan and fixed-income securities data, performing historical and predictive analytics/forecasting, and generating explanatory reports and data visualizations. RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.  

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For over a decade, RiskSpan has been the consulting services vendor of choice for large banking, insurance, and capital markets participants. RiskSpan data scientists, technologists, and quants have handled data management, model development, and model validation, and we have adapted our products to the mid-sized and small commercial banking and insurance sectors. talk scope risktech 100 Interested in learning more about our platform and services? Get in touch today.


Risk-as-a-Service – Transforming Portfolio Market Risk Analytics

Watch RiskSpan Co-Founder and Chief Technology Officer, Suhrud Dagli, discuss RiskSpan’s Risk-as-a-Service offerings. RiskSpan’s market risk management team has transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speeds and costs never before possible in the industry. Still want more? Take a look at our portfolio market risk analytics page.


CRT Exposure to Hurricane Michael

Graph

With Hurricane Michael approaching the Gulf Coast, we put together some interactive charts looking at the affected metro areas, and their related CRT exposure (Both CAS and STACR). Given the large area of impact with Hurricane Michael, we have included a nearly exhaustive selection of MSA’s. Click on a deal ID along the left-hand side of the plot to view its exposure to each MSA. Most of the mortgage delinquencies in the wake of Hurricane Harvey quickly cured. Holders of securities backed by loans that ultimately defaulted (typically because the property was completely destroyed) had much of their exposure mitigated by insurance proceeds, government intervention, and other relief provisions.  






Analytics-as-a-Service – CECL Forecasting

The RiskSpan Edge Platform CECL Module delivers the technology platform and expertise to take you from where you are today to producing audit-ready CECL estimates. Our dedicated CECL Module executes your monthly loss reserving and reporting process under the new CECL standard, covering data intake, segmentation, modeling, and report generation within a single platform. Watch RiskSpan Director David Andrukonis explain the Edge CECL Module in this video.

 

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RiskSpan Edge Platform API

The RiskSpan Edge Platform API enables direct access to all data from the RS Edge Platform. This includes both aggregate analytics and loan-and pool-level data.  Standard licensed users may build queries in our browser-based graphical interface. But, our API is a channel for power users with programming skills (Python, R, even Excel) and production systems that are incorporating RS Edge Platform components as part of their Service Oriented Architecture (SOA).

Watch RiskSpan Director LC Yarnelle explain the Edge API in this video!

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CRT Deal Monitor: Understanding When Credit Becomes Risky

This analysis tracks several metrics related to deal performance and credit profile, putting them into a historical context by comparing the same metrics for recent-vintage deals against those of ‘similar’ cohorts in the time leading up to the 2008 housing crisis. You’ll see how credit metrics are trending today and understand the significance of today’s shifts in the context of historical data. Some of the charts in this post have interactive features, so click around! We’ll be tweaking the analysis and adding new metrics in subsequent months. Please shoot us an email if you have an idea for other metrics you’d like us to track.

Highlights

  • Performance metrics signal steadily increasing credit risk, but no cause for alarm.
    • We’re starting to see the hurricane-related (2017 Harvey and Irma) delinquency spikes subside in the deal data. Investors should expect a similar trend in 2019 due to Hurricane Florence.
    • The overall percentage of delinquent loans is increasing steadily due to the natural age ramp of delinquency rates and the ramp-up of the program over the last 5 years.
    • Overall delinquency levels are still far lower than historical rates.
    • While the share of delinquency is increasing, loans that go delinquent are ending up in default at a lower rate than before.
  • Deal Profiles are becoming riskier as new GSE acquisitions include higher-DTI business.
    • It’s no secret that both GSEs started acquiring a lot of high-DTI loans (for Fannie this moved from around 16% of MBS issuance in Q2 2017 to 30% of issuance as of Q2 this year). We’re starting to see a shift in CRT deal profiles as these loans are making their way into CRT issuance.
    • The credit profile chart toward the end of this post compares the credit profiles of recently issued deals with those of the most recent three months of MBS issuance data to give you a sense of the deal profiles we’re likely to see over the next 3 to 9 months. We also compare these recently issued deals to a similar cohort from 2006 to give some perspective on how much the credit profile has improved since the housing crisis.
    • RiskSpan’s Vintage Quality Index reflects an overall loosening of credit standards–reminiscent of 2003 levels–driven by this increase in high-DTI originations.
  • Fannie and Freddie have fundamental differences in their data disclosures for CAS and STACR.
    • Delinquency rates and loan performance all appear slightly worse for Fannie Mae in both the deal and historical data.
    • Obvious differences in reporting (e.g., STACR reporting a delinquent status in a terminal month) have been corrected in this analysis, but some less obvious differences in reporting between the GSEs may persist.
    • We suspect there is something fundamentally different about how Freddie Mac reports delinquency status—perhaps related to cleaning servicing reporting errors, cleaning hurricane delinquencies, or the way servicing transfers are handled in the data. We are continuing our research on this front and hope to follow up with another post to explain these anomalies.

The exceptionally low rate of delinquency, default, and loss among CRT deals at the moment makes analyzing their credit-risk characteristics relatively boring. Loans in any newly issued deal have already seen between 6 and 12 months of home price growth, and so if the economy remains steady for the first 6 to 12 months after issuance, then that deal is pretty much in the clear from a risk perspective. The danger comes if home prices drift downward right after deal issuance. Our aim with this analysis is to signal when a shift may be occurring in the credit risk inherent in CRT deals. Many data points related to the overall economy and home prices are available to investors seeking to answer this question. This analysis focuses on what the Agency CRT data—both the deal data and the historical performance datasets—can tell us about the health of the housing market and the potential risks associated with the next deals that are issued.

Current Performance and Credit Metrics

Delinquency Trends

The simplest metric we track is the share of loans across all deals that is 60+ days past due (DPD). The charts below compare STACR (Freddie) vs. CAS (Fannie), with separate charts for high-LTV deals (G2 for CAS and HQA for STACR) vs. low-LTV deals (G1 for CAS and DNA for STACR). Both time series show a steadily increasing share of delinquent loans. This slight upward trend is related to the natural aging curve of delinquency and the ramp-up of the CRT program. Both time series show a significant spike in delinquency around January of this year due to the 2017 hurricane season. Most of these delinquent loans are expected to eventually cure or prepay. For comparative purposes, we include a historical time series of the share of loans 60+ DPD for each LTV group. These charts are derived from the Fannie Mae and Freddie Mac loan-level performance datasets. Comparatively, today’s deal performance is much better than even the pre-2006 era. You’ll note the systematically higher delinquency rates of CAS deals. We suspect this is due to reporting differences rather than actual differences in deal performance. We’ll continue to investigate and report back on our findings.

Delinquency Outcome Monitoring

While delinquency rates might be trending up, loans that are rolling to 60-DPD are ultimately defaulting at lower and lower rates. The tables below track the status of loans that were 60+ DPD. Each bar in the chart represents the population of loans that were 60+ DPD exactly 6 months prior to the x-axis date. Over time, we see growing 60-DPD and 60+ DPD groups, and a shrinking Default group. This indicates that a majority of delinquent loans wind up curing or prepaying, rather than proceeding to default. The choppiness and high default rates in the first few observations of the data are related to the very low counts of delinquent loans as the CRT program ramped up. The following table repeats the 60-DPD delinquency analysis for the Freddie Mac Loan Level Performance dataset leading up to and following the housing crisis. (The Fannie Mae loan level performance set yields a nearly identical chart.) Note how many more loans in these cohorts remained delinquent (rather than curing or defaulting) relative to the more recent CRT loans. https://plot.ly/~dataprep/30.embed

Vintage Quality Index

RiskSpan’s Vintage Quality Index (VQI) reflects a reversion to the looser underwriting standards of the early 2000s as a result of the GSEs’ expansion of high-DTI lending. RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. We use the metric as an empirically grounded way to control for vintage differences within our credit model. VQI-History While both GSEs increased high-DTI lending in 2017, it’s worth noting that Fannie Mae saw a relatively larger surge in loans with DTIs greater than 43%. The chart below shows the share of loans backing MBS with DTI > 43. We use the loan-level MBS issuance data to track what’s being originated and acquired by the GSEs because it is the timeliest data source available. CRT deals are issued with loans that are between 6 and 20 months seasoned, and so tracking MBS issuance provides a preview of what will end up in the next cohort of deals. High DTI Share

Deal Profile Comparison

The tables below compare the credit profiles of recently issued deals. We focus on the key drivers of credit risk, highlighting the comparatively riskier features of a deal. Each table separates the high-LTV (80%+) deals from the low-LTV deals (60%-80%). We add two additional columns for comparison purposes. The first is the ‘Coming Cohort,’ which is meant to give an indication of what upcoming deal profiles will look like. The data in this column is derived from the most recent three months of MBS issuance loan-level data, controlling for the LTV group. These are newly originated and acquired by the GSEs—considering that CRT deals are generally issued with an average loan age between 6 and 15 months, these are the loans that will most likely wind up in future CRT transactions. The second comparison cohort consists of 2006 originations in the historical performance datasets (Fannie and Freddie combined), controlling for the LTV group. We supply this comparison as context for the level of risk that was associated with one of the worst-performing cohorts. The latest CAS deals—both high- and low-LTV—show the impact of increased >43% DTI loan acquisitions. Until recently, STACR deals typically had a higher share of high-DTI loans, but the latest CAS deals have surpassed STACR in this measure, with nearly 30% of their loans having DTI ratios in excess of 43%. CAS high-LTV deals carry more risk in LTV metrics, such as the percentage of loans with a CLTV > 90 or CLTV > 95. However, STACR includes a greater share of loans with a less-than-standard level of mortgage insurance, which would provide less loss protection to investors in the event of a default. Credit Profile Low-LTV deals generally appear more evenly matched in terms of risk factors when comparing STACR and CAS. STACR does display the same DTI imbalance as seen in the high-LTV deals, but that may change as the high-DTI group makes its way into deals. Low-LTV-Deal-Credit-Profile-Most-Recent-Deals

Deal Tracking Reports

Please note that defaults are reported on a delay for both GSEs, and so while we have CPR numbers available for August, CDR numbers are not provided because they are not fully populated yet. Fannie Mae CAS default data is delayed an additional month relative to STACR. We’ve left loss and severity metrics blank for fixed-loss deals. STACR-Deals-over-the-past-3-months CAS-Deals-from-the-past-3-months.

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RiskSpan Adds Home Equity Conversion Mortgage Data to Edge Platform

ARLINGTON, VA, September 12, 2018 — Leading mortgage data analytics provider RiskSpan added Home Equity Conversion Mortgage (HECM) Data to the library of datasets available through its RS Edge Platform. The dataset includes over half a billion records from Ginnie Mae that will expand the RS Edge Platform’s critical applications in Reverse-Mortgage Analysis. RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.

The HECM dataset is the latest in a series of recent additions to the RS Edge data libraries. The platform now holds over five billion records across decades of collection and is the solution of choice for whole loan and securities analytics. RiskSpan’s data strategy is simple. Provide our customers with normalized, tested, analysis-ready data that their enterprise modeling and analytics teams can leverage for faster, more reliable insight. We do the grunt work so that you don’t have to, said Patrick Doherty, RiskSpan’s Chief Operating Officer.  The HECM dataset has been subjected to RiskSpan’s comprehensive data normalization process for simpler analysis in RS Edge. Edge users will be able to drill down to snapshot and historical data available through the UI. Users will also be able to benchmark the HECM data against their own portfolio and leverage it to develop and deploy more sophisticated credit models.  RiskSpan’s Edge API also makes it easier-than-ever to access large datasets for analytics, model development and benchmarking. Major quant teams that prefer APIs now have access to normalized and validated data to run scenario analytics, stress testing or shock analysis. RiskSpan makes data available through its proprietary instance of RStudio and Python.

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Data-as-a-Service – Credit Risk Transfer Data

Watch RiskSpan Managing Director Janet Jozwik explain our recent Credit Risk Transfer data (CRT) additions to the RS Edge Platform.

Each dataset has been normalized to the same standard for simpler analysis in RS Edge, enabling users to compare GSE performance with just a few clicks. The data has also been enhanced to include helpful variables, such as mark-to-market loan-to-value ratios based on the most granular house price indexes provided by the Federal Housing Finance Agency. 

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RiskSpan to Offer Credit Risk Transfer Data Through Edge Platform

ARLINGTON, VA, September 6, 2018 — RiskSpan announced today its rollout of Credit Risk Transfer (CRT) datasets available through its RS Edge Platform. The datasets include over seventy million Agency loans that will expand the RS Edge platform’s data library and add key enhancements for credit risk analysis.  RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.  New additions to the RS Edge Data Library will include key GSE Loan Level Performance datasets going back eighteen years. RiskSpan is also adding Fannie Mae’s Connecticut Avenue Securities (CAS) and Credit Insurance Risk Transfer (CIRT) datasets as well as the Freddie Mac Structured Agency Credit Risk (STACR) datasets.  

Each dataset has been normalized to the same standard for simpler analysis in RS Edge. This will allow users to compare GSE performance with just a few clicks. The data has also been enhanced to include helpful variables, such as mark-to-market loan-to-value ratios based on the most granular house price indexes provided by the Federal Housing Finance Agency.  Managing Director and Co-Head of Quantitative Analytics Janet Jozwik said of the new CRT data, “Our data library is a great, cost-effective resource that can be leveraged to build models, understand assumptions around losses on different vintages, and benchmark performance of their own portfolio against the wider universe.”  RiskSpan’s Edge API also makes it easier-than-ever to access large datasets for analytics, model development and benchmarking. Major quant teams that prefer APIs now have access to normalized and validated data to run scenario analytics, stress testing or shock analysis. RiskSpan makes data available through its proprietary instance of RStudio and Python. 

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Big Companies; Big Data Issues

Data issues plague organizations of all sorts and sizes. But generally, the bigger the dataset, and the more transformations the data goes through, the greater the likelihood of problems. Organizations take in data from many different sources, including social media, third-party vendors and other structured and unstructured origins, resulting in massive and complex data storage and management challenges. This post presents ideas to keep in mind when seeking to address these.

First, a couple of definitions:

Data quality generally refers to the fitness of a dataset for its purpose in a given context. Data quality encompasses many related aspects, including:

  • Accuracy,
  • Completeness,
  • Update status,
  • Relevance,
  • Consistency across data sources,
  • Reliability,
  • Appropriateness of presentation, and
  • Accessibility

Data lineage tracks data movement, including its origin and where it moves over time. Data lineage can be represented visually to depict how data flows from its source to its destination via various changes and hops.

The challenges facing many organizations relate to both data quality and data lineage issues, and a considerable amount of time and effort is spent both in tracing the source of data (i.e., its lineage) and correcting errors (i.e., ensuring its quality). Business intelligence and data visualization tools can do a magnificent job of teasing stories out of data, but these stories are only valuable when they are true. It is becoming increasingly vital to adopt best practices to ensure that the massive amounts of data feeding downstream processes and presentation engines are both reliable and properly understood.

Financial institutions must frequently deal with disparate systems either because of mergers and acquisitions or in order to support different product types—consumer lending, commercial banking and credit cards, for example. Disparate systems tend to result in data silos, and substantial time and effort must go into providing compliance reports and meeting the various regulatory requirements associated with analyzing data provenance (from source to destination). Understanding the workflow of data and access controls around security are also vital applications of data lineage and help ensure data quality.

In addition to the obvious need for financial reporting accuracy, maintaining data lineage and quality is vital to identifying redundant business rules and data and to ensuring that reliable, analyzable data is constantly available and accessible. It also helps to improve the data governance echo system, enabling data owners to focus on gleaning business insights from their data rather than focusing attention on rectifying data issues.

Common Data Lineage Issues

A surprising number of data issues emerge simply from uncertainty surrounding a dataset’s provenance. Many of the most common data issues stem from one or more of the following categories:

  • Human error: “Fat fingering” is just the tip of the iceberg. Misconstruing and other issues arising from human intervention are at the heart of virtually all data issues.
  • Incomplete Data: Whether it’s drawing conclusions based on incomplete data or relying on generalizations and judgment to fill in the gaps, many data issues are caused by missing data.
  • Data format: Systems expect to receive data in a certain format. Issues arise when the actual input data departs from these expectations.
  • Data consolidation: Migrating data from legacy systems or attempting to integrate newly acquired data (from a merger, for instance) frequently leads to post-consolidation issues.
  • Data processing: Calculation engines, data aggregators, or any other program designed to transform raw data into something more “usable” always run the risk of creating output data with quality issues.

Addressing Issues

Issues relating to data lineage and data quality are best addressed by employing some combination of the following approaches. The specific blend of approaches depends on the types of issues and data in question, but these principles are broadly applicable.

Employing a top-down discovery approach enables data analysts to understand the key business systems and business data models that drive an application. This approach is most effective when logical data models are linked to the physical data and systems.

Creating a rich metadata repository for all the data elements flowing from the source to destination can be an effective way of heading off potential data lineage issues. Because data lineage is dependent on the metadata information, creating a robust repository from the outset often helps preserve data lineage throughout the life cycle.

Imposing useful data quality rules is an important element in establishing a framework in which data is always validated against a set of well-conceived business rules. Ensuring not only that data passes comprehensive rule sets but also that remediation factors are in place for appropriately dealing with data that fails quality control checks is crucial for ensuring end-to-end data quality.

Data lineage and data quality both require continuous monitoring by a defined stewardship council to ensure that data owners are taking appropriate steps to understand and manage the idiosyncrasies of the datasets they oversee.

Our Data Lineage and Data Quality Background

RiskSpan’s diverse client base includes several large banks (with we define as banks with assets totaling in excess of $50 billion). Large banks are characterized by a complicated web of departments and sub-organizations, each offering multiple products, sometimes to the same base of customers. Different sub-organizations frequently rely on disparate systems (sometimes due to mergers/acquisitions; sometimes just because they develop their businesses independent of one another). Either way, data silos inevitably result.

RiskSpan has worked closely with chief data officers of large banks to help establish data stewardship teams charged with taking ownership of the various “areas” of data within the bank. This involves the identification of data “curators” within each line of business to coordinate with the CDO’s office and be the advocate (and ultimately the responsible party) for the data they “own.” In best practice scenarios, a “data curator” group is formed to facilitate collaboration and effective communication for data work across the line of business.

We have found that a combination of top-down and bottom-up data discovery approaches is most effective when working accross stakeholders to understand existing systems and enterprise data assets. RiskSpan has helped create logical data flow diagrams (based on the top-down approach) and assisted with linking physical data models to the logical data models. We have found Informatica and Collibra tools to be particularly useful in creating data lineage, tracking data owners, and tracing data flow from source to destination.

Complementing our work with financial clients to devise LOB-based data quality rules, we have built data quality dashboards using these same tools to enable data owners and curators to rectify and monitor data quality issues. These projects typically include elements of the following components.

  • Initial assessment review of the current data landscape.
  • Establishment of a logical data flow model using both top-down and bottom-up data discovery approaches.
  • Coordination with the CDO / CIO office to set up a data governance stewardship team and to identify data owners and curators from all parts of the organization.
  • Delineation of data policies, data rules and controls associated with different consumers of the data.
  • Development of a target state model for data lineage and data quality by outlining the process changes from a business perspective.
  • Development of future-state data architecture and associated technology tools for implementing data lineage and data quality.
  • Invitation to client stakeholders to reach a consensus related to future-state model and technology architecture.
  • Creation of a project team to execute data lineage and data quality projects by incorporating the appropriate resources and client stakeholders.
  • Development of a change management and migration strategy to enable users and stakeholders to use data lineage and data quality tools.

Ensuring data quality and lineage is ultimately the responsibility of business lines that own and use the data. Because “data management” is not the principal aim of most businesses, it often behooves them to leverage the principles outlined in this post (sometimes along with outside assistance) to implement tactics that will to help ensure that the stories their data tell are reliable.


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