The challenge associated with simply gauging the risk associated with “end user computing” applications (EUCs) let alone managing it—is both alarming and overwhelming. Scanning tools designed to detect EUCs can routinely turn up tens of thousands of potential files, even at not especially large financial institutions. Despite the risks inherent in using EUCs for mission-critical calculations, EUCs are prevalent in nearly any institution due to their ease of use and wide-ranging functionality.
Are there performance differences between FHA insured loans and those guaranteed by the VA?
Watch the short video for insights and to see just how easy this can be done with RS Edge.
For many companies, the question is no longer whether to use open source tools, but rather how to implement them with the appropriate governance and controls. Have security concerns been accounted for? How does one effectively institute controls over bad code? Are there legal implications for using open source software?
There are two main challenges when implementing a machine learning solution: building a model that performs well and effectively leveraging the results. Having a good understanding of the machine learning process and model being used is key to tackling both issues. Using a predictive model without appropriately understanding it can substantially increase risk and lead to missed opportunities. If the performance of a model is unclear, misunderstood, or overestimated then subsequent decisions will be biased or outright wrong. Likewise, if the ability of a model is underestimated then its use will not be optimized.
Since the financial crisis began in 2007, the “Non-Agency” MBS market, i.e., securities neither issued nor guaranteed by Fannie Mae, Freddie Mac, or Ginnie Mae, has been sporadic and has not rebounded from pre-crisis levels. In recent months, however, activity by large financial institutions, such as AIG and Wells Fargo, has indicated a return to the issuance of Non-Agency MBS. What is contributing to the current state of the securitization market for high-quality mortgage loans? Does the recent, limited-scale return to issuance by these institutions signal an increase in private securitization activity in this sector of the securitization market? If so, what is sparking this renewed interest?
The GSE NPL sales program gives investors the opportunity to profit from investing in non-performing loans from Fannie Mae and Freddie Mac and maximizing the number of loans they can get to re-perform. Freddie Mac and Fannie Mae (the GSEs) have been selling non-performing loans (NPLs) since 2014 and 2015, respectively, to reduce their holdings of less liquid non-performing assets and gain favorable execution over holding the loans as they have done in the past.
VIENNA, Va., May 15, 2017 – RiskSpan, a data management and predictive analytics firm that specializes in solutions for the mortgage, capital markets, and banking industries, today announced a new release of its loan-level mortgage query tool, RS Edge.
Financial institutions are constantly seeking new ways to maintain a competitive advantage and increase efficiency. These days, many institutions are turning to technology as competition intensifies and the regulatory environment becomes increasingly uncertain. In order to stay afloat in the industry, these institutions are incorporating big data into their business strategy.
Complying with the DFAST/CCAR requirements within an existing quantitative models and model risk management framework is one of the most daunting of the many recent challenges banks, Bank Holding Companies (BHC) and some Investment Holding Companies (IHC) currently face. The Dodd-Frank Act Stress Tests (DFAST) require all financial institutions with total assets above $10 billion to do stress tests on their portfolio and balance sheet. The Comprehensive Capital Analysis and Review (CCAR) is generally required to be completed once a bank’s total assets are above $50 billion. The objective of both exercises is to simulate a bank’s balance sheet performance and losses in a hypothetical severe economic downturn over the next nine quarters. Given this common objective, most risk managers consider and complete both exercises together.
Using open source data modeling tools has been a topic of debate as large organizations, including government agencies and financial institutions, are under increasing pressure to keep up with technological innovation to maintain competitiveness. Organizations must be flexible in development and identify cost-efficient gains to reach their organizational goals, and using the right tools is crucial. Organizations must often choose between open source software, i.e., software whose source code can be modified by anyone, and closed software, i.e., proprietary software with no permissions to alter or distribute the underlying code.
VIENNA, Va., April 12, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, today announced it is relocating its headquarters to the Rosslyn business district in Arlington, Virginia.
The financial industry has traditionally been slow to adopt the latest data and technology trends, and the case of open source software is no exception. While open source has been around for decades, we’re only now seeing its manifestation within the finance and mortgage industries. Many institutions are exploring the viability of open source within the financial industry but hesitate to act because of the potential risks open source can expose them to.
VIENNA, Va., March 22, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, today at IBM’s InterConnect conference in Las Vegas, announced the launch of a new data and analytics API on the IBM Bluemix platform.
Last month I highlighted the role of the front-end insurance risk share process around Credit Risk Transfer (CRT). I reviewed what the front-end risk share model is in the current state and noted the expanded efforts underway to broaden the pool of MI’s and reinsurers as counterparties t
o expand the front-end offerings. This is in addition to the already successful back-end CRT which has found great success thus far. So, the key question for 2017 is what does CRT look like in a post housing reform environment where much of the capital at risk is not the government credit guarantee but is comprised of private capital?
The question of “build versus buy” is every bit as applicable and challenging to model validation departments as it is to other areas of a financial institution. With no “one-size-fits-all” solution, banks are frequently faced with a balancing act between the use of internal and external model validation resources. This article is a guide for deciding between staffing a fully independent internal model validation department, outsourcing the entire operation, or a combination of the two.
VIENNA, Va., March 9, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, announced that it has been selected for HousingWire’s 2017 HW TECH100™ award.
This year saw the highest number of nominees in the history of HW TECH100™, which recognizes leading companies that bring tech innovation to the U.S. housing economy. Among this year’s winners are other industry-leading firms such as Accenture, CoreLogic, and Freddie Mac.
Models based on Machine Learning are being increasingly adopted by the finance community in general and the mortgage market in particular. The use of modeling and data analytics has been key in the turnaround of this market; however, anyone who has worked with mortgage loan data knows it is notorious for errors and data gaps. Despite industry-wide efforts to incorporate robust quality control programs, challenges with mortgage data persist. Fortunately, combining machine learning in finance with cloud computing shows promise in addressing mortgage data gaps and producing more accurate results than traditional approaches.
VIENNA, Va., March 2, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, today announced the addition of Faith Schwartz, Senior Advisor of Accenture Credit Services (ACS), to RiskSpan’s Board of Directors.
The FHFA issued an RFI to solicit feedback from stakeholders on proposals from the GSEs to adopt additional front-end credit risk transfer structures and to consider additional credit risk transfer policy issues. There is firm interest in this new and growing execution for risk transfer by investors who have confidence in the underwriting and servicing of mortgage loans through new and improved GSE standards.
As we look forward to 2017 and the critical issues facing the nation’s housing finance system, one of the paramount matters will be the ongoing development of the Credit Risk Transfer (CRT) initiative.