Review of Strategic Credit Positions by Credit Risk Manager

The bank’s top management and board on a periodic basis review its Credit Risk Strategy and Policy. These policy and strategies help protect the bank against credit exposure risk. Banks typically have different types of exposure - that is to individuals, corporates, commercial, etc. They grant credit to organizations at various locations, with different periods of maturity and profitability. The bank will follow different strategies to attract its target consumers. The bank might have different goals in terms of market classes and locations to achieve the desired profitability and diversification and will pursue a certain strategy to achieve it.

Of course this strategy will have to be followed with certain precautions. Primary among these precautions is the quality of credit. The bank must take into account the cost of capital. It must also weigh the risk vs. reward payoff. The bank’s capital buffer must also be taken into view while following a certain strategy. Washington Mutual, in search of high growth moved away from aggressively seeking deposits to a strategy of providing credit to consumers with poor credit records. This was a sure move towards impending disaster.

Banks need to have a credit strategy that is long term in its view. A very short term strategy will mean that when a certain bubble, say housing, bursts the bank will be faced with a wave of defaults which it might not be able to sustain. The strategy should be able to sustain the ups and downs of the business cycle.

Once the credit strategy framework is up, it is important that it is relayed to all relevant parties. They must be made accountable and ensure there is compliance. The board must oversee if top management is implementing the strategy as per the policies laid down. The bank’s remuneration policy must support rather than contradict the bank’s risk strategy.

On a day to day basis the credit manager would look for any spikes in credit exposures. The manager would look for such changes as it could signal some destabilizing features in the market which could lead to customers maxing their credit limits. This would mean a fall in the value of collateral such customers have provided. A bank usually has large positions with key customers, where even small downswings could result in the bank calling in for more security, in other words, a margin call.

Previously banks relied on a traditional system of haircuts that depended on the quality of collateral offered. For example if a customer offered government bonds as collateral, the bank would offer a haircut of 5% on lending value. This is because the credit quality of government bonds is stable. A blue chip equity would evoke a haircut of say 20%, and a corporate bond 10%. In some cases the bank would receive collateral not covered in its traditional list, with clear haircut stipulation.

This has now evolved into a more dynamic collateral assessment system. The modern system allows the credit manager to assess the collateral values on a day-to-day basis. This system also allows for correlation of risk factors and underlying assets to be accounted for. The credit manager can therefore implement a value at risk model for the banks collateral portfolio.

In the case of non-collateralized lending, the credit manager would be monitoring credit lines and patterns in their utilization. Sometimes in the course of business the credit line may be fully drawn. This could indicate that such maximum utilization of credit lines increases the risk of default.

A credit manager has to therefore keep a vigilant eye particularly on strategic positions. Despite this they are sometimes exposed to wrong way exposure and incur losses far beyond their provisions and forecasts.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.