General Information & Client Service
  • Americas: +1.212.553.1653
  • Asia: +852.3551.3077
  • China: +86.10.6319.6580
  • EMEA: +44.20.7772.5454
  • Japan: +81.3.5408.4100
Media Relations
  • New York: +1.212.553.0376
  • London: +44.20.7772.5456
  • Hong Kong: +852.3758.1350
  • Tokyo: +813.5408.4110
  • Sydney: +61.2.9270.8141
  • Mexico City: +001.888.779.5833
  • Buenos Aires: +0800.666.3506
  • São Paulo: +0800.891.2518
April 2018

With an immense amount of available data generated worldwide within the last two years, the next evolution of banking analytics will include information from a variety of open and closed sources.

At Moody’s Analytics, we are leveraging our credit risk expertise and long history of data and modeling to address the ever-growing demand for more insightful analytics. We are currently evaluating the predictive outcome of these alternative datasets as they are applied to various stages of the credit process. Given the promising results so far, we believe this is the future of more powerful decisioning capabilities within the financial industry.

In this webinar, a panel of research and data scientist experts across Moody’s Analytics discuss:

  • Social data in probability of default modeling
  • Closed and open data for location scoring
  • Text analytics for credit risk

Click Here for the Presentation

Related Insights
Whitepaper

Sovereign & Size-Adjusted EDF-Implied Rating Template (for Private Firms)

RiskCalc™ EDF™ (Expected Default Frequency) values and agency ratings are widely used credit risk measures. RiskCalc EDF values typically measure default risk for private companies, while agency ratings are only available for rated companies. A RiskCalc EDF value measures a company's standalone credit risk based on financial statement information, while an agency rating considers qualitative factors such as Business Profile, Financial Policy, external support, and country-related risks. Moody's Analytics new Sovereign & Size-Adjusted EDF-Implied Rating Template combines RiskCalc EDF values with additional factors to provide a rating comparable to agency ratings for private companies. The new template applies to RiskCalc EDF values across numerous geographies and regulatory environments. With the new template, users can generate a rating more comparable to an agency rating than RiskCalc EDF values or EDF-implied ratings. Analyzing data from 3,900+ companies in 60+ countries, we find that sovereign rating and total asset size, in addition to EDF value, have a statistically significant impact on an agency rating — our quantitative template incorporating these three variables reliably estimates agency ratings in a robust fashion.

December 2018 Pdf Maria Buitrago, Uliana Makarov, Dr. Janet ZhaoDr. Douglas Dwyer
Whitepaper

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
Whitepaper

August 2018 U.S. Middle Market Risk Report

Private firm default rates have declined steadily during the past five years. At 1.4%, the rolling 12-month default rate is down 74% from its September 2009 peak of 5.2%. This trend has been driven primarily by a decline in the charge-off rate, now at its lowest level in ten years. In addition, the percentage of borrowers in non-accrual status has decreased 56% since September 2009. The number of borrowers rated “Substandard” has seen a steady increase since the first quarter of 2016, above pre-crisis levels, reflecting banks' cautious lending practices.

August 2018 Pdf Irina Korablev, Lin Moon, Stephanie Yu
Whitepaper

Features of a Lifetime PD Model: Evidence from Public, Private, and Rated Firms

With the new CECL and IFRS 9 requirements, we see an increased need for lifetime probability of default models. In this document, we formally investigate and summarize the term structure properties consistently seen across public, private, and rated firms. We observe that the default rate for “good” firms tends to increase over time, while the default rate for “bad” firms decreases over time, an indication of the mean-reversion effect seen with firms' default risk.

May 2018 Pdf Sajjad Beygiharchegani, Uliana Makarov, Dr. Janet ZhaoDr. Douglas Dwyer

Leveraging Bank Internal Data and Industry Group Data for CECL Modelling

The presentation discussed strategic and tactical considerations when creating a CECL modeling approach. We discuss the approach of adapting models built from industry/peer group data and then examine leveraging bank internal ratings and industry data for both C&I and CRE portfolios.

April 24, 2018 Pdf Eric Bao, Dr. Yanping Pan, Yanruo Wang
Presentation

Applications of Alternative Data in Credit Decisioning

In this webinar, a panel of research and data scientist experts across Moody's Analytics discuss social data in probability of default modeling, closed and open data for location scoring, and text analytics for credit risk.

April 2018 Pdf Eric Bao, Irina Korablev, Rama Sankisa, Dr. Janet Zhao
Whitepaper

November 2017 U.S. Middle Market Risk Report

Private firm default rates have declined steadily during the past five years. At 1.5%, the rolling 12-month default rate is down 73% from its September 2009 peak of 5.3%. This trend has been driven primarily by a decline in the charge-off rate, now at its lowest level in the past ten years. In addition, the rate of borrowers in non-accrual status has decreased 53% since September 2009. Banks downgraded 17% of borrowers on their internal rating scales during the past year, compared to 15% in 2016.

November 2017 Pdf Lin Moon, Stephanie Yu, Irina Korablev
Whitepaper

Combining Financial and Behavioral Information to Predict Defaults for Small and Medium-Sized Enterprises – A Dynamic Weighting Approach

One large challenge lenders currently face is how to combine different types of information into metrics that can support good business decisions. Currently, the banking industry uses two primary types of information — financial information and behavioral information — independently, to assess risk. Financial information includes Income Statement, Balance Sheet, Cash Flow, and Financial Ratios. Behavioral information includes spending and payment patterns, among others. Both types of information provide unique insights, but, to date, they have not been combined to generate one comprehensive risk metric for commercial use.

September 2017 Pdf Alessio Balduini, Dr. Douglas DwyerDr. Janet Zhao, Sara Gianfreda, Reeta Hemminki, Lucia Yang
Presentation

Leveraging Industry Data for CECL Compliance Presentation Slides

In this presentation, Irina Korablev, Senior Director and Deniz Tudor, Director will discuss various tools that can capture economic, loan-level, and cohort-level data across several asset classes, which can be used for forecasting credit losses and benchmarking internal models.

August 2017 Pdf Dr. Deniz Tudor, Irina Korablev
RESULTS 1 - 10 OF 34