General Information & Client Services
  • 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

As new regulations require increased visibility of risk management processes, financial institutions often struggle to find strategic value in new investments beyond regulatory compliance. There is a need for tools that not only optimize long-term business strategy but also answer last-minute questions about rapidly changing economic conditions. Linking strategic tools with forecasting models can also provide greater clarity on the purpose of stress testing initiatives and therefore enhance regulatory compliance.

Financial Organizations Are Looking for Capital Strategy Management Tools

Regulatory requirements have increased across financial institutions in the past decade, and they continue to demand more transparency across the organization. While chasing regulatory compliance, senior management often lacks the time and resources to leverage the output of regulatory exercises for strategic insights. Firms have invested an estimated $12 billion to $15 billion in risk technology and data infrastructure, according to a McKinsey survey.1 If they can appropriately leverage these investments, firms can expect benefits in the range of $19 billion to $24 billion. This paper focuses on how to unlock this value by using new tools to expand key performance metric forecasting under various economic conditions to guide and optimize business strategy.

There are a number of challenges and considerations that must be addressed to effectively forecast capital adequacy (e.g., Common Equity Tier 1). The scope of the calculation is the main issue, as the following must at a minimum be forecast consistently under each scenario:

  • Charge-offs
  • Allowances and resulting provisions
  • Interest income and expenses, as well as other sources of income
  • Risk-weighted assets

While all of these calculations are required for stress testing analysis, the key hurdle to leveraging stress testing infrastructure is the abundance of granular bottom-up models that are time-intensive and computationally demanding. However, understanding the portfolio and economic drivers in resulting forecasts remains critical and necessitates some drill-down abilities.

Currently, the processes in place for stress testing and other regulatory exercises focus on detailed granular analysis, but there is a market need for strategic tools to support a timely analysis for identifying which scenarios and strategies to drill into comprehensively. As shown in Figure 1, financial organizations have focused most of the investments on these goals:

  • Data quality, aggregation, and availability
  • Compliance and risk reporting

However, the $19 billion to $24 billion worth of benefits that McKinsey predicts are found in the top two layers:

  • Strategic decision-making
  • Optimization
Figure 1. Financial institutions’ main areas of investment
Financial institutions’ main areas of investment
Source: Moody's Analytics

When it comes to forecasting, capital planning groups are looking to understand the relative performance of scenarios, while regulators are continuing to ask how the organization ensures it adheres to its risk appetite. There is an increasing need for tools that optimize business strategy while simultaneously providing senior management rapid feedback on frequent “what if” scenarios.

A wide variety of “what if” market-moving events requires senior management to be adequately prepared by understanding the potential impact to their organizations. Events such as the UK’s vote to withdraw from the EU and a Chinese growth slowdown have highlighted the need to consider and implement appropriate strategies for these events. Market sentiment shifts rapidly and many events happen overnight, causing senior management to request timely answers on the possible impact to capital forecasts. There is a clear need for an abbreviated top-down analysis to provide rapid feedback and assess many strategies prior to running a more thorough analysis on the chosen scenarios.

Moreover, regulators are increasingly focused on how financial institutions adhere to risk appetite statements. Ensuring that claims made to the market trickle down into actionable measurement at the business-line level has been a challenge across institutions. Management must also show regulators that their internal processes support a portfolio that will withstand a wide variety of economic conditions, which may require running quantitative analyses for those scenarios and considering multiple growth strategies that would perform well within risk appetite bounds.

Business strategy optimization is desired and often elusive for many financial institutions. Following the financial crisis, common practice has moved toward managing capital almost entirely based on the expanded regulatory requirements and standards, but buffers are necessary to account for unforeseen market conditions. Most organizations do not have a way to quantify the “right size” capital buffer, as they do not have an efficient way to analyze additional scenarios and strategic actions. Furthermore, as acquisitions continue and further complicate the equation, data is often not available to run detailed bottom-up models to project the impact on capital ratios.

Capital Strategy Should Link Directly to Business as Usual

Capital strategy decisions are heavily scrutinized by the market. Tools that can aid in business optimization and risk quantification will help link operations and processes across diverse financial institutions. Focusing on the drivers of capital metrics such as provisions, interest income, and expenses allows a communicable strategic vision.

Provisions are modeled through a combination of credit loss and allowance models, and they have significant impact on forecast capital ratios by directly impacting net income. Credit losses are highly correlated with the economic cycle, and they are critical for strategic and “what if” analyses. Risk management and allowance requirements often use granular loss modeling, while forecasts for new volumes can be at a more aggregated level.

Interest income and expenses, in conjunction with capital strategy, drive forecasts of capital expectations over time based on a given scenario. Tools developed need to have flexibility in assumptions around items that contribute to net income.

Strategic tools should be tied directly to other models used by various business lines in financial institutions. One way to ensure consistency between strategic and regulatory initiatives is to directly anchor strategic results to forecasts generated by more granular models. The anchoring ensures strategic tools can produce directional indication for additional “what if” analyses and business strategies under consideration.

In conclusion, many financial institutions would benefit from using strategic decision-making tools that offer timely ways to consider strategies and manage risk appetite from the top down. In the past, capital planning, risk management, and portfolio management remained in silos within organizations, and in many cases, those functions were further distributed by region. Increasing scrutiny by the market and regulators has led to increasing demands on senior management to quantify and be able to justify strategic actions and decisions. This, in turn, has been driving the demand for tools that can be leveraged for strategic decision-making and optimization.

Footnotes

1 Harreis, Holger, Matthias Lange, Jorge Machado, Kayvaun Rowshankish, and David Schraa. "A marathon, not a sprint: Capturing value from BCBS 239 and beyond." Risk. McKinsey & Company. June 2015.

SUBJECT MATTER EXPERTS
As Published In:
Related Insights

Predicting Earnings: CECL's Implications for Allowance Forecasts

In this article, we demonstrate the effect of the new allowance framework by quantifying allowances and credit earnings volatility for a sample portfolio.

July 2017 WebPage Joy HartAnna Labowicz

CECL Treatment for the Investment Portfolio

In this presentation, our experts discussed common CECL considerations for structured credit and answer key questions on how to provide CECL estimates for structured credit.

April 2017 Pdf David KurnovNihil Patel

Investment Portfolios CECL Methodologies

In this fifth webinar in our series, our experts discussed common CECL considerations for structured credit and answered key questions on how to provide CECL estimates for structured credit.

April 2017 WebPage David KurnovNihil Patel

Expanding Sensitivity Analysis and Stress Testing for CECL

To ease the transition to CECL, firms can leverage and align existing risk management practices. Institutions are in the process of trying to determine which methodologies can be expanded to meet the CECL impairment model requirements, while retaining a consistency between other regulatory and risk management activities.

December 2016 WebPage Nihil Patel, Michael Gullette

Leveraging Basel and Stress Testing Models for CECL

Basel Advanced IRB models, other internal ratings models, and Stress Testing models were developed by many large financial institutions for capital management. Moody’s Analytics will outline how institutions can leverage these models to comply with FASB’s new impairment accounting standards.

October 2016 WebPage Emil LopezNihil Patel

Modeling IFRS 9 Impairments – Tactical Implementation Approaches

Learn how Moody’s Analytics is helping institutions of all sizes address the challenges of implementing the IFRS 9 impairment model.

October 2016 WebPage Burcu Guner, Nihil Patel

Managing Credit Portfolio Risk Under Basel III: Integrating Regulatory Capital with Economic Risks

In this webinar we will discuss different approaches in credit portfolio management, dangers of only using regulatory capital when optimizing your portfolio, how to appropriately incorporate regulatory capital considerations, and metrics to consider when optimizing your portfolio and setting appropriate limits.

June 2016 WebPage Nihil Patel

Implementing an IFRS 9 Solution: Challenges Faced by Financial Institutions

This article provides an overview of the new standard and analyzes the major challenges financial institutions will face in ensuring IFRS 9 compliance.

June 2016 WebPage Cayetano Gea-CarrascoNihil Patel

Making Risk Appetite Stick: How Data and Analytics Can Help

In this webinar, we discuss how institutions can overcome challenges to ensure that risk appetite can be monitored as well as key analytic metrics which can be leveraged for strategic decision-making.

April 2016 WebPage Nihil PatelEd Young

Linking Stress Testing and Portfolio Credit Risk

Nihil Patel, Senior Director, provides insight on how to link stress testing with portfolio credit risk for a comprehensive risk management solution.

October 2013 Pdf Nihil Patel

Applications of GCorr™ Macro: Risk Integration, Stress Testing, and Reverse Stress Testing

This research develops an approach to expand the Moody's Analytics Global Correlation Model (GCorr) to include macroeconomic variables. Within the context of this document, macroeconomic variables can include financial market variables, economic activity variables, and other risk factors. The expanded correlation model, known as GCorr Macro, lends itself to several functions that facilitate a cohesive and holistic risk management practice.

April 2013 Pdf Mariano Lanfranconi, Libor Pospisil, Andrew Kaplin, Dr. Amnon LevyNihil Patel

Modeling Credit Correlations: An Overview of the Moody's Analytics GCorr Model

The Moody's Analytics Global Correlation Model (GCorr™) is a multi-factor model for asset correlations. This document provides an overview of the GCorr framework, methodology, data used for estimation, and validation. In addition, this document describes the components of GCorr related to individual asset classes and their integration. The asset classes explicitly included in GCorr are: public firms, private firms, small and medium-sized enterprises, sovereigns, U.S. commercial real estate, and U.S. retail.

December 2012 Pdf Jimmy Huang, Mariano Lanfranconi, Nihil Patel, Libor Pospisil

New Methods for Modeling Sovereign Risk in Credit Portfolios

The recent sovereign debt crisis in Europe, along with the global increase in sovereign debt issuance, has motivated credit portfolio managers to renew their focus on managing sovereign risk. In response, Moody's Analytics Quantitative Research Group has developed new techniques for modeling sovereign asset correlations.

October 2011 WebPage Nihil Patel, Heather Russell, Vojislav Sesum