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.
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.
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.
Even with CECL compelling banks to collect more internal loan data, we continue to emphasize profitability as the primary benefit of robust, proprietary, loan-level data. Make no mistake, the data template we outline below is for CECL modeling. CECL compliance, however, is a prerequisite to profitability. Also, while third-party data may suffice for some components of the CECL estimate, especially in the early years of implementation, reliance on third-party data can drag down profitability. Third-party data is often expensive to buy, may be unsatisfactory to an auditor, and can yield less accurate forecasts. Inaccurate forecasts mean volatile loss reserves and excessive capital buffers that dilute shareholder profitability. An accurate forecast built on internal data not only solves these problems but can also be leveraged to optimize loan screening and loan pricing decisions.