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GSE: Datamart Design and Build

The Problem

A government-sponsored enterprise needed a centralized data solution for its forecasting process, which involved cross-functional teams from different business lines.​

The firm also sought a cloud-based data warehouse to host forecasting outputs for reporting purposes with faster querying and processing speeds.​

The firm also needed assistance migrating data from legacy data sources to new datamarts. The input and output files and datasets had different sources and were often in different formats. Analysis and transformation were required prior to designing, developing and loading tables.  

The Solution

RiskSpan built and now maintains a new centralized datamart (in both Oracle and Amazon Web Services) for the client’s revenue and loss forecasting processes. This includes data modeling, historical data upload, and the monthly recurring data process.

The Deliverables

  • Analyzed the end-to-end data flow and data elements​
  • Designed data models satisfying business requirements​
  • Processed and mapped forecasting input and output files​
  • Migrated data from legacy databases to the new sources ​
  • Built an Oracle datamart and a cloud-based data warehouse (Amazon Web Services) ​
  • Led development team to develop schemas, tables and views, process scripts to maintain data updates and table partitioning logic​
  • Resolved data issues with the source and assisted in reconciliation of results

GSE: ETL Solutions

The Problem

The client needed ETL solutions for handling data of any complexity or size in a variety of formats and/or from different upstream sources.​

The client’s data management team extracted and processed data from different sources and different types of databases (e.g. Oracle, Netezza, Excel files, SAS datasets, etc.), and needed to load into its Oracle and AWS datamarts for it’s revenue and loss forecasting processes. ​

The client’s forecasting process used very complex large-scale datasets in different formats which needed to be consumed and loaded in an automated and timely manner.

The Solution

RiskSpan was engaged to design, develop and implement ETL (Extract, Transform and Load) solutions for handling input and output data for the client’s revenue and loss forecasting processes. This included dealing with large volumes of data and multiple source systems, transforming and loading data to and from data marts and data ware houses.

The Deliverables

  • Analyzed data sources and developed ETL strategies for different data types and sources​
  • Performed source target mapping in support of report and warehouse technical designs​
  • Implemented business-driven requirements using Informatica ​
  • Collaborated with cross-functional business and development teams to document ETL requirements and turn them into ETL jobs ​
  • Optimized, developed, and maintained integration solutions as necessary to connect legacy data stores and the data warehouses

Case Study: Web Based Data Application Build

The Client

Government Sponsored Enterprise (GSE)

The Problem

The Structured Transactions group of a GSE needed to offer a simpler way for broker-dealers to  create new restructured securities (improved ease of use), that provided flexibility to do business at any hour and reduce the dependence on Structured Transactions team members’ availability. 

The Solution

RiskSpan led the development of a customer-facing web-based application for a GSE. Their structured transactions clients use the application to independently create pools of pools and re-combinable REMIC exchanges (RCRs) with existing pooling and pricing requirements.​

RiskSpan delivered the complete end-to-end technical implementation of the new portal.

The Deliverables

  • Development included self-service web portal that provides RCR, pool-of-pool exchange capabilities, reporting features ​
  • Managed data flows from various internal sources to the portal, providing real-time calculations​
  • Latest technology stack included Angular 2.0, Java for web services​
  • Development, testing, and config control methodology featured DevOps practices, CI/CD pipeline, 100% automated testing with Cucumber, Selenium​
  • GIT, JIRA, Gherkin, Jenkins, Fisheye/Crucible, SauceLabs, for config control, testing, deployment

Case Study: Web Based Data Application Build

The Client

GOVERNMENT SPONSORED ENTERPRISE (GSE)

The Problem

The Structured Transactions group of a GSE needed to offer a simpler way for broker-dealers to  create new restructured securities (improved ease of use), that provided flexibility to do business at any hour and reduce the dependence on Structured Transactions team members’ availability. 


The Solution

RiskSpan led the development of a customer-facing web-based application for a GSE. Their structured transactions clients use the application to independently create pools of pools and re-combinable REMIC exchanges (RCRs) with existing pooling and pricing requirements.​

RiskSpan delivered the complete end-to-end technical implementation of the new portal.


The Deliverables

  • Development included self-service web portal that provides RCR, pool-of-pool exchange capabilities, reporting features ​
  • Managed data flows from various internal sources to the portal, providing real-time calculations​
  • Latest technology stack included Angular 2.0, Java for web services​
  • Development, testing, and config control methodology featured DevOps practices, CI/CD pipeline, 100% automated testing with Cucumber, Selenium​
  • GIT, JIRA, Gherkin, Jenkins, Fisheye/Crucible, SauceLabs, for config control, testing, deployment

CONTACT US

RiskSpan Edge & CRT Data

For participants in the credit risk transfer (CRT) market, managing the massive quantity of data to produce clear insights into deal performance can be difficult and demanding on legacy systems. Complete analysis of the deals involves bringing together historical data, predictive models, and deal cash flow logic, often leading to a complex workflow in multiple systems. RiskSpan’s Edge platform (RS Edge) solves these challenges, bringing together all aspects of CRT analysis. RiskSpan is the only vendor to bring together everything a CRT analyst needs:  

  • Normalized, clean, enhanced data across programs (STACR/CAS/ACIS/CIRT),
  • Historical Fannie/Freddie performance data normalized to a single standard,
  • Ability to load loan-level files related to private risk transfer deals,
  • An Agency-specific, loan-level, credit model,
  • Seamless Intex integration for deal and portfolio analysis,
  • Scalable scenario analysis at the deal or portfolio level, and
  • Vendor and client model integration capabilities.
  • Ability to load loan-level files related to private risk transfer deals.

Deal Comparison Table All of these features are built into RS Edge, a cloud-native, data and analytics platform for loans and securities. The RS Edge user interface is accessible via any web browser, and the processing engine is accessible via an application programming interface (API). Accessing RS Edge via the API allows access to the full functionality of the platform, with direct integration into existing workflows in legacy systems such as Excel, Python, and R. To tailor RS Edge to the specific needs of a CRT investor, RiskSpan is rolling out a series of Excel tools, built using our APIs, which allow for powerful loan-level analysis from the tool everyone knows and loves. Accessing RS Edge via our new Excel templates, users can:

  • Track deal performance,
  • Compare deal profiles,
  • Research historical performance of the full GSE population,
  • Project deal and portfolio performance with our Agency-specific credit model or with user-defined CPR/CDR/severity vectors, and
  • Analyze various macro scenarios across deals or a full portfolio

Loan Attribute Distributions

The web-based user interface allows for on-demand analytics, giving users specific insights on deals as the needs arise. The Excel template built with our API allows for a targeted view tailored to the specific needs of a CRT investor.

For teams that prefer to focus their time on outcomes rather than the build, RiskSpan’s data team can build custom templates around specific customer processes. RiskSpan offers support from premiere data scientists who work with clients to understand their unique concerns and objectives to integrate our analytics with their legacy system of choice. Loan Performance History The images are examples of a RiskSpan template for CRT deal comparison: profile comparison, loan credit score distribution, and delinquency performance for five Agency credit risk transfer deals, pulled via the RiskSpan Data API and rendered in Excel. ______________________________________________________________________________________________

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Fannie Mae’s New CAS REMIC: Why REITs Are Suddenly Interested in CRT Deals

Fannie Mae has been issuing credit-risk-transfer (CRT) deals under its Connecticut Avenue Securities (CAS) program since 2013. The investor base for these securities has traditionally been a diverse group of asset managers, hedge funds, private equity firms, and insurance companies. The deals had been largely ignored by Real Estate Investment Trusts (REITs), however. The following pie charts illustrate the investor breakdown of Fannie Mae’s CAS 2018-C06 deal, issued in October 2018. Note that REITs accounted for only 11 percent of the investor base of the Group 1 and Group 2 M-2 tranches (see note below for information on how credit risk is distributed across tranches), and just 4 percent of the Group 1 B-1 tranche. Things began to change in November 2018, however, when Fannie Mae began to structure CAS offering as notes issued by trusts that qualify as Real Estate Mortgage Investment Conduits (REMICs). The first such REMIC offering, CAS 2018-R07, brought about a substantial shift in the investor distribution, with REITs now accounting for a significantly higher share. As the pie charts below illustrate, REITs now account for some 22 percent of the M-2 tranche investor base and nearly 20 percent of the B-1 tranche.

What Could Be Driving This Trend?
It seems reasonable to assume that REITs are flocking to more favorable tax treatment of REMIC-based structures. These will now be more simplified and aligned with other mortgage-related securities, as Fannie Mae points out. Additionally, the new CAS REMIC notes meet all the REIT income and asset tests for tax purposes, and there is a removal on tax withholding restrictions for non-U.S. investors in all tranches. The REMIC structure offers additional benefits to REITs and other investors. Unlike previous CAS issues, the CAS REMIC—a bankruptcy-remote trust—issues the securities and receives the cash proceeds from investors. Fannie Mae pays monthly payments to the trust in exchange for credit protection, and the trust is responsible for paying interest to the investors and repaying principal less any credit losses. Since it is this new third-party trustee issuing the CAS REMIC securities, investors will be shielded from exposure to any future counterparty risk with Fannie Mae. The introduction of the REMIC structure represents an exciting development for the CAS program and for CRT securities overall. It makes them more attractive to REITs and offers these and other traditional mortgage investors a new avenue into credit risk previously available only in the private-label market.

End Note: How Are CAS Notes Structured?
Notes issued prior to 2016 as part of the CAS program are aligned to a structure of six classes of reference tranches, as illustrated below:
Catastrophic Risk
Two mezzanine tranches of debt are offered for sale to investors. The structure also consists of 4 hypothetical reference tranches, retained by Fannie Mae and used for allocation of cash flows. When credit events occur, write-downs are first applied to the Fannie Mae retained first loss position. Only after the entire first loss position is written down are losses passed on to investors in mezzanine tranche debt – first M2, then M1. Loan prepayment is allocated along an opposite trajectory. As loans prepay, principal is first returned to the investors in M1 notes. Only after the full principal balance of M1 notes have been repaid do M2 note holders receive principal payments. Beginning with the February 2016 CAS issuance (2016-C01), notes follow a new structure of seven classes of reference tranches, as illustrated below:
Catastrophic Risk
In addition to the two mezzanine tranches, a portion of the bottom layer is also sold to investors. This allows Fannie Mae to transfer a portion of the initial expected loss. When credit events occur, both Fannie Mae and investors incur losses. Additionally, beginning with this issuance, the size of the B tranche was increased to 100 bps, effectively increasing the credit support offered to mezzanine tranches. Beginning with the January 2017 CAS issuance (2017-C01), notes follow a structure of eight classes of reference tranches, as illustrated below:
Catastrophic Risk
Fannie Mae split the B tranche horizontally into two equal tranches, with Fannie Mae retaining the first loss position. The size of the B1 tranche is 50 bps, and Fannie Mae retains a vertical slice of the B1 tranche.


Case Study: RiskSpan Edge Platform Agency MBS Module

The Client

Multiple Agency Traders and the Research & Strategy Division of a Major Investment Bank

The Problem

RiskSpan leverages its extensive expertise to help clients rapidly access the drivers of prepayment risk and prepayment trends. Our analytical platform provides ultimate flexibility and speed to perform quickly turn securities level data into information to based decisions.

The Solution

The RiskSpan Edge Platform is used by the Agency Trading desk to slice and dice data and look for patterns among various bonds using the graphical interface. The RiskSpan Edge Platform offers users access to current and historical data on Ginnie Mae, Fannie Mae, and Freddie Mac (“Agencies”) pass-throughs as well as other data sets.

The tool provides a flexible user interface that supports analysis of prepayment data and actionable reporting. The database includes all monthly pool level data published by the Agencies dating back to 1995.  This data includes pool factors, geographic concentrations and supplemental pool level collateral information. The Prepayment Analytics tool provides a flexible user interface that supports intuitive analysis of the prepayment data and actionable reporting delivered quickly to decision‐makers. The database includes all monthly data published by the Agencies for all months back to 1995, including factors, geographic breakdowns and supplemental disclosure information.

The Deliverables

RiskSpan provides the tools for comprehensive Agency MBS analysis.

  • Visualizing data with integrated graphing and charting
  • Researching new prepayment trends
  • Creating user-defined data tables
  • Exporting customized charts and graphs for marketing purposes


Developing Legal Documents | Contract and Disclosure Tool

In a world of big data and automation, many financial institutions and legal advisors still spend an extraordinary amount of time creating the legal documentation for new financial instruments and their ongoing surveillance. RiskSpan’s Contract and Disclosure Tool, reduces the risk, time, and expenses associated with the process (patent pending).

The Tool automates the generation of a prospectus supplement, the content of which is a complex combination of static and dynamic legal language, data, tables, and images. Based on a predefined set of highly customizable rules and templates, the application dynamically converts deal-specific information from raw data files and tables into a legally compliant disclosure document. Authorized personnel can upload the data files onto the Tool’s intuitive UI, with total control and transparency over document versions and manual content changes which are automatically tracked, and which users can review, approve, or reject before finalizing the document for publication.

While there is no substitute for the legal and financial expertise of the attorneys and modelers in the financial security space, the Tool allows these professionals to make the most of their time. Rather than manually creating documentation from spreadsheets, data files, and multiple templates, users begin their analysis with a complete, pre-generated English-language document. If manual changes are further required, users can update the input data files and re-create a new document or directly and seamlessly edit the text using the application’s editing screen, which also allows users to easily visualize the changes between the versions, by highlighting content that was updated, added or deleted.

Automating the generation of legal content quantitatively decreases fees, increases productivity, and results in a much quicker turnaround, freeing up time to accommodate other business activities. The Tool’s superior computing power can turn around initial draft versions of the disclosure documents in just a few seconds!

Another feature that is difficult to overlook is the reduction of risk. It is very important that legal documentations accurately and completely reflect all of a deal’s terms and conditions. The Tool allows the legal and financial staff to focus on the deal structure, rather than manually identifying and duplicating content from prior deal templates, thereby minimizing the risks of human data errors.

The application accomplishes this in several ways. First, directly translating existing files that are used in other modeling functions ensures that model and documentation data remains aligned. Second, the static language is generated in accordance with the deal structure, leaving little room for variation. Third, a set of built-in quality control tools alerts users to missing files and data, inconsistent and erroneous structures, incorrect principal and interest payment rules, and unusual structures that require further review. Fourth, the tool keeps track of content updates and changes, and allows for version control, so users can track and review changes in document versions.

Introducing new technologies into nuanced processes can be problematic. Certainly, developing legal documents is not a one-size-fits-all proposition. Every document has its own format, criteria and legal requirements. RiskSpan’s Contract and Disclosure Tool is highly customizable to varying financial instruments and deal structures with exceptional focus on accurate legal content, quality control, and aesthetics of the final product, freeing up premium time and resources for other priorities.

____________________________________________________________________________________________

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SOFR, So Good? The Main Anxieties Around the LIBOR Transition

SOFR Replacing LIBOR

The London Interbank Offered Rate (LIBOR) is going away, and the international financial community is working hard to plan for and mitigate risks to make a smooth transition. In the United States, the Federal Reserve’s Alternative Reference Rates Committee (ARRC) has recommended the Secured Overnight Financing Rate (SOFR) as the preferred replacement rate. The New York Fed began publishing SOFR regularly on April 3, 2018. In July 2018, Fannie Mae issued $6 billion in SOFR-denominated securities, leading the way for other institutions who have since followed suit. In November 2018, the Federal Home Loan (FHL) Banks issued $4 billion in debt tied to SOFR. CME Group, a derivatives and futures exchange company, launched 3-month and 1-month SOFR futures contracts in 2018. All of these steps to support liquidity and demonstrate SOFR demand are designed to create a rate more robust than LIBOR—the transaction volume underpinning SOFR rates is around $750 billon daily, compared to USD LIBOR’s estimated $500 million in daily transaction volume. 

USD LIBOR is referenced in an estimated $200 trillion of financial contracts, of which 95 percent is derivatives. However, the remaining cash market is not small. USD LIBOR is referenced in an estimated: $3.4 trillion in business loans, $1.3 trillion in retail mortgages and other consumer loans, $1.8 trillion in floating rate debt, and $1.8 trillion in securitized products. 

The ARRC has held consultations on its recommended fallback language for floating rate notes and syndicated business loans—the responses are viewable on the ARRC website. On December 7, the ARRC published consultations on securitizations and bilateral business loans, which are both open for comment through February 5, 2019.  

Amid the flurry of positive momentum in the transition towards SOFR, anxiety remains that the broader market is not moving quickly enough. ARRC consultations and working groups indicate that these anxieties derive primarily from a few specific points of debate: development of term rates, consistency of contracts, and implementation timing.

Term Rates

Because the SOFR futures market remains immature, term rates cannot be developed without significant market engagement with the newly created futures. The ARRC Paced Transition Plan includes a goal to create a forward-looking reference rate by end-of-year 2021 – just as LIBOR is scheduled to phase out. In the interim, financial institutions must figure out how to build into existing contracts fallback language or amendments that include a viable alternative to LIBOR term rates.  

The nascent SOFR futures market is growing quickly, with December 2018 daily trade volumes at nearly 16,000. However, they pale in comparison to Eurodollar futures volumes, which logged daily averages around 5 million per day at CME Group alone. This puts SOFR on track according to the ARRC plan, but means institutions remain in limbo until the futures market is more mature and term SOFR rates can be developed. 

In July 2018, the Financial Stability Board (FSB) stated their support for employment of term rates primarily in cash markets, while arguing that spreads are tightest in derivative markets focused around overnight risk-free rates (RFRs), which therefore are preferred. An International Swaps and Derivatives Association (ISDA) FAQ document published in September 2018 explained the FSB’s request that “ISDA should develop fallbacks that could be used in the absence of suitable term rates and, in doing so, should focus on calculations based on the overnight RFRs.” This marks a major change, given that derivatives commonly reference 3-month LIBOR, and cash products are dependent on forward-looking term rates. Despite the magnitude of change, transition from LIBOR term rates to an alternative term rate based on limited underlying transactions would be undesirable.

The FSB explained:

Moving the bulk of current exposures referencing term IBOR benchmarks that are not sufficiently anchored in transactions to alternative term rates that also suffer from thin underlying markets would not be effective in reducing risks and vulnerabilities in the financial system. Therefore, the FSB does not expect such RFR-derived term rates to be as robust as the RFRs themselves, and they should be used only where necessary.

In consultation report published December 20, 2018, ISDA stated the overwhelming majority of respondents preference for fallback language with a compounded setting in arrears rate for the adjusted RFR, with a significant and diverse majority preferring the historical mean/median approach for the spread adjustment.

Though ISDA’s consultation report noted some drawbacks to the historical mean/median approach for the spread adjustment, the diversity of supporters – in all regions of the world, representing many types of financial institutions – was a strong indicator of market preference. By comparison, there was no ambiguity about preference for the RFR in fallback language: In almost 90 percent of ISDA respondent rankings, the compounded setting in arrears rate was selected as the top preference for the adjusted RFR. 

In the Structured Finance Industry Group (SFIG) LIBOR Task Force Green Paper, the group indicates strong preference for viable term rates and leaves the question of whether such calculations should be done in advance or in arrears as an open item, while indicating preference for continuing prospectively determining rates at the start of each term. They list their preference for waterfall options as first an endorsed forward-looking term SOFR rate, and second, a compounded or average daily SOFR. SFIG is currently drafting their response to the ARRC Securitization Consultation, which will be made public on the ARRC website after submission. 

Despite stated preferences, working groups are making a concerted effort to follow the ARRC’s guidance to strive for consistency across cash and derivative products. Given the concerns about a viable term rate, some market participants in cash products are also exploring the realities of implementing ISDA’s recommended fallback language and intend to incorporate those considerations into their response to the ARRC consultations. 

In the absence of an endorsed term rate, pricing of other securities such as fixed-rate bonds is difficult, if not impossible. Additionally, the absence of an endorsed term rate creates issues of consistency within the rate itself (i.e., market standards will need to developed around how and over what periods the rate is compounded). The currently predominant recommendation of a compounding in arrears overnight risk-free rate would also have added complexity when compared with any forward-looking rate, which is exacerbated in the cash markets with consumer products where changes must be fully disclosed and explained. Compounding in arrears would require a lock-out period at the end of a term to allow institutions time to calculate the compounded interest. Market standards and consumer agreement around the specific terms governing the lock-out period would be difficult to establish.

Consistency:

While ISDA has not yet completed formal consultation specific to USD LIBOR and SOFR, and their analysis is only applicable to derivatives and swaps, there are several benefits to consistency across cash and derivatives markets. Consistency of contract terms across all asset classes during the transition away from USD LIBOR lowers operational, accounting, legal, and basis risk, according to the ARRC, and makes the change easier to communicate and negotiate with stakeholders.  

Though it is an easy case to make that consistency is advantageous, achieving it is not. For example, the Mortgage Bankers Association points out that the ISDA-selected compounding in arrears approach to interest accrual periods “would be a very material change from current practice as period interest expenses would not be determined until the end of the relevant period.” The nature of the historical mean/median spread adjustment does not come without drawbacks. ISDA’s consultation acknowledges that the approach is “likely to lead to value transfers and potential market disruption by not capturing contemporaneous market conditions at the trigger event, as well as creating potential issues with hedging.” Additionally, respondents acknowledge that relevant data may not yet be available for long lookback periods with the newly created overnight risk-free rates.  

The effort to achieve some level of consistency across the transition away from LIBOR poses several challenges related to timing. Because LIBOR will only be unsupported (rather than definitively discontinued) by the Financial Conduct Authority (FCA) at the end of 2021, some in the market retain a small hope that production of LIBOR rates could continue. The continuation of LIBOR is possible, but betting a portfolio of contracts on its continuation is an unnecessarily high-risk decision. That said, transition plans remain ambiguous about timing, and implementation of any contract changes is ultimately at the sole discretion of the contract holder. Earlier ARRC consultations acknowledged two possible implementation arrangements:   

  1. An “amendment approach,” which would provide a streamlined amendment mechanism for negotiating a replacement benchmark in the future and could serve as an initial step towards adopting a hardwired approach.  
  2. A “hardwired approach,” which would provide market participants with more clarity as to a how a potential replacement rate will be identified and implemented. 

However, the currently open-for-comment securitizations consultation has dropped the “amendment” and “hardwired” terminology and now describes what amounts to the hardwired approach as defined above – a waterfall of options that is implemented upon occurrence of a predefined set of “trigger” events. Given that the securitizations consultation is still open for comment, it remains possible that market respondents will bring the amendment approach back into discussions.  

Importantly, in the U.S. there are currently no legally binding obligations for organizations to plan for the cessation of LIBOR, nor policy governing how that plan be made. In contrast, the European Union has begun to require that institutions submit written plans to governing bodies.

Timing

Because the terms of implementation remain open for discussion and organizational preference, there is some ambiguity about when organizations will begin transitioning contracts away from LIBOR to the preferred risk-free rates. In the structured finance market, this compounds the challenge of consistency with timing. For commercial real estate securities, for example, there is possibility of mismatch in the process and timing of transition for rates in the index and for the underlying assets and resulting certificates or bonds. This potential challenge has not yet been addressed by the ARRC or other advisory bodies.

Mortgage Market

The mortgage market is still awaiting formal guidance. While the contributions by Fannie Mae and the FHLBanks to the SOFR market signal government sponsored entity (GSE) support for the newly selected reference rate, none of the GSEs has issued any commentary about recommended fallback language specific to mortgages or guidance on how to navigate the fact that SOFR does not yet have a viable term rate. An additional concern for consumer loan products, including mortgages, is the need to explain the contract changes to consumers. As a result, the ARRC Securitization consultation hypothesizes that consumer products are “likely to be simpler and involve less optionality and complexity, and any proposals would only be made after wide consultation with consumer advocacy groups, market participants, and the official sector.”  

For now, the Mortgage Bankers Association has recommended institutions develop a preliminary transition plan, beginning with a detailed assessment of exposures to LIBOR.

How can RiskSpan Help?

At any phase in the transition away from LIBOR, RiskSpan can provide institutions with analysts experienced in contract review, experts in model risk management and sophisticated technical tools—including machine learning capabilities—to streamline the process to identify and remediate LIBOR exposure. Our diverse team of professionals is available to deliver resources to financial institutions that will mitigate risks and streamline this forthcoming transition.


Case Study: Loan-Level Capital Reporting Environment​

The Client

Government Sponsored Enterprise (GSE)

The Problem

A GSE and large mortgage securitizer maintained data from multiple work streams in several disparate systems, provided at different frequencies. Quarterly and ad-hoc data aggregation, consolidation, reporting and analytics required a significant amount of time and personnel hours. ​

The client desired configurable integration with source systems, automated acquisition of over 375 million records and performance improvements in report development.

 

The Solution

The client engaged RiskSpan Consulting Services to develop a reporting environment backed by an ETL Engine to automate data acquisition from multiple sources. 

The Deliverables

  • Reviewed system architecture, security protocol, user requirements and data dictionaries to determine feasibility and approach.​
  • Developed a user-configurable ETL Engine, developed in Python, to load data from different sources into a PostgreSQL data repository hosted on Linux server. The engine provides real-time status updates and error tracking.​
  • Developed the reporting module of the ETL Engine in Python to automatically generate client-defined Excel reports, reducing report development time from days to minutes​
  • Made raw and aggregated data available for internal users to connect virtually any reporting tool, including Python, R, Tableau and Excel​
  • Developed a user interface, leveraging the API exposed by the ETL Engine, allowing users to create and schedule jobs as well as stand up user-controlled reporting environments​


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