Banking regulations contain a degree of subjectivity. Regulations are broad and pertain to the entire industry, but institutions can vary significantly in operations and size even within the subsets of rules that apply to each. Practically speaking, this often leaves the examination process subject to interpretation in terms of what is compliant and what is not.
We have discussed the essentials of CECL in a previous post in which we explained the new requirements. Below we show a simple path toward CECL compliance which may serve as a catalyst toward more refined and robust methods.
In response to an executive order signed by President Trump on February 3, 2017, the U.S. Department of the Treasury released a series of reports. These reports identify government policies that promote or inhibit regulation of the U.S. financial system in a manner consistent with the administration’s stated Core Principles. The most recent of these reports, released in July 2018, addresses nonbank financial institutions, financial technology, and financial innovation.
Fair Lending laws and regulations prohibit discrimination on prohibited bases, including race, gender, age and ethnicity.
Initially published in 1993, the FDIC Consumer News newsletter is celebrating 25 years of publication.
In our last post, we discussed the importance of understanding both what is and what is not included in the data for regression analysis. In this post, we further emphasize this point with an illustration relevant to a common CECL methodology – the probability of default/loss given default method (PD/LGD).
The first and most important step of any quantitative analysis is understanding what the data consists of. This, unfortunately, is many times ignored in econometric analyses.
The new CECL standards fundamentally change the allowed loan and lease loss (ALLL) calculation for GAAP-reporting institutions.
