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Identifying At-Risk Names in Your Private Firm Portfolio — RiskCalc Early Warning Toolkit

This report outlines a practical approach for using RiskCalc EDF credit measures to effectively monitor large portfolios of private firms and to proactively identify at-risk names. The RiskCalc Early Warning Toolkit Excel add-in is an easy to use, yet comprehensive tool that allows users to focus costly and scarce resources on a highly targeted selection of the most at-risk names in their portfolios. This research for private firms compliments previous research on Early Warning Toolkit for public firms. The Early Warning Toolkit identifies at-risk names within a private firm portfolio well before default, using a number of different EDF-related risk metrics.

November 2018 Pdf Ziyi Sun, Dr. Janet Zhao, Gustavo Jimenez

Equity-at-Risk and Transfer Pricing: Annualised Expected Loss versus Cumulative Expected Loss

This article is intended as guidance for transfer pricing professionals in Luxembourg who are considering the equity-at-risk following the calculation of a loan's expected loss when using Moody's Analytics tools. This article does not provide final decision-making processes, which remain at the discretion of the transfer pricing professional, according to the specific case. This article is intended to create elements of thought and paths to economically and financially sound results.

November 2018 Pdf Christophe Marinier

Cloud-Based Credit Origination Solutions are Not all the Same

There are many benefits to using cloud-based technology including faster deployments and reduced cost of ownership. Modernizing your credit origination system and process is critical to keeping up with increased consumer demand for efficiency and convenience. With an ever-growing number of SaaS credit origination solutions popping up, how do you know which technology and which provider is right for you?

June 2018 Pdf Jill Coppersmith, Anju Govil

Uncertainty in Asset Correlation Estimates and Its Impact on Credit Portfolio Risk Measures

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.

March 2018 Pdf Jimmy Huang, Libor Pospisil

Redefining Loan Monitoring and Early Warning Signal Detection Through an Integrated Solution

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.

December 2017 Pdf Hichem Kerma

Maximize Efficiency: How Automation Can Improve Your Loan Origination Process

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.

December 2017 Pdf Doug Peterson

Five Indispensable Insights For The Evaluation of a Commercial Credit Origination System

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.

November 2017 Pdf Zuraidah Zaid

Build or Buy: Transforming Commercial Credit Origination

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.

April 2017 Pdf David Ratnage

Preparing for CECL: How community banks can prepare for implementation

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.

March 2017 Pdf

How to Become a Data-Driven Bank

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?

March 2017 Pdf

What Do 20 Million C&I Loan Observations Say about New Origination Dynamics? — Insights from Moody's Analytics CRD Data

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.

February 2017 Pdf Dr. Pierre Xu, Tomer Yahalom, May Jeng
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