Credit portfolio models rely on estimated and calibrated parameters, such as default and rating migration probabilities, recovery rates, and asset correlations. Users of these models must understand how various errors in the parameter estimates impact model outputs, for example Unexpected Loss (UL) or Economic Capital (EC). Asset correlations estimated using asset return time series are subject to inherent uncertainty — statistical errors — arising due to a limited length of the time series. The main question this paper addresses is how these errors translate into statistical errors in the estimated UL and EC. We illustrate several properties of the errors using an analytical method. As expected, longer time series lead to lower errors in UL and EC. Increasing the number of exposures in a portfolio, however, can reduce the errors in UL and EC only to a certain degree.
Jimmy Huang, Libor Pospisil
In this paper, we outline the challenges of traditional lending practices and examine each stage of the credit process to see how automation can improve and standardize underwriting procedures.
In this paper, we explore what monitoring lenders routinely undertake, why it is so difficult and what new technology tools are at their disposal to improve the process, and show how better monitoring can lead to better risk management and lower portfolio losses.
In this paper Moody's Analytics draw on more than a decade of experience with credit origination solution implementations, with banks of varying sizes, complexity, and geography. To share observations of five key steps that financial institutions must consider when evaluating a credit origination solution.
Technology is rapidly changing the way we do business. In the financial services sector, arguably the largest industry in the world, this has never been more true. From mobile accessibility to cloud computing, technology is driving a new wave of change fueled by a dynamic fintech industry comprising hundreds of companies – many of which did not exist ten or even five years ago. Unconstrained by legacy architecture, alternative and challenger lenders embracing these technologies offer a new customer experience in terms of accessibility, speed, and transparency.
Recently, the Financial Accounting Standards Board (FASB) issued the current expected credit loss (CECL) standard. Although CECL doesn't take effect until 2021 for most community banks and credit unions, there are some basic steps you can take right now to prepare for it.
Community banks are coming of age with the new power they can wield from the growing availability of advanced data analytics. Client data and the tools to analyze it can literally transform how community banks conduct their commercial lending business. Data-driven community banks can use data analytics to make informed decisions and more profitably serve their customers and streamline their operations. So why are data-driven community banks not the norm?
We construct and examine new origination of C&I loans to middle-market borrowers using the Loan Accounting System data extracted from Moody's Analytics Credit Research Database (CRD/LAS). We find that C&I loan origination declines during the Great Recession and recovers soon after. The magnitude of the decline and the speed of the recovery varies across segments. For example, new lending to the financial industry decreases more than to the non-financial industry during the recession and recovers faster afterwards. Another example, new originations during the recession consists predominantly of short-term loans, while long-term lending becomes more dominant post crisis. This finding suggests that banks are using loan tenor as a means to mitigate risk during crises, at times even more so than credit quality.
Dr. Pierre Xu, Tomer Yahalom, May Jeng