Quotidien finance innovation, innovation financière journal
Financial Year with Finyear


Risk Management: Risk Modeling - to Drive Capital Project Performance

Debt, liquidity constraints and access to financing are major issues for public and private participants in major capital projects. Banks, investment funds, sponsors, industries or estates are less willing than ever to invest their money in projects without the return on investment.

Risk Management: Risk Modeling - to Drive Capital Project Performance
While such analysis provides a precise financial estimation of the project, financial evaluation of the most probable scenarios is not enough to protect against capital project risk. A complete assessment of the project leading to a decision on the investment should include a thorough risk evaluation.

Risk modeling is a crucial activity that allows project investors and management to determine on an ongoing basis—through a quantitative approach using deterministic or probabilistic methods—the levels of risk to which the project is exposed.


Once a project is launched, risks should be monitored during the entire project’s life in order to make sure that the anticipated value is ultimately delivered to investors. The overall risk management process—including risk modeling—is not a one-time activity but continues throughout the entire project lifecycle.

While probabilistic risk modeling cannot prevent the occurrence of risk events or project failure, it allows investors to complete a financial valuation of the project, taking into account all the risks and uncertainties which could prevent the project from reaching such performance targets as internal rate of return(IRR), return on capital (ROC), return on investment (ROI) and net present value (NPV) of the project’s cash flow, margin and earnings. It allows a more comprehensive and more accurate overall evaluation of the project. Risk modeling is a crucial activity that allows project investors and management to determine on an ongoing basis—through a quantitative approach using deterministic or probabilistic methods—the levels of risk to which the project is exposed.


In today’s increasingly complex and interconnected global operating environment, growing drivers of risk such as the scarcity of commodity resources, shortages of talented human capital, environmental responsibility, geo-political instability and technology dependence are significantly increasing the level of uncertainty in the development and delivery of capital projects. Investors who need a thorough financial analysis to decide whether to invest in a project, and project managers who need to implement robust on-going project control, agree on the need for extensive risk modeling. A qualitative or deterministic approach, however, is no longer suitable to effectively support the decision making process. A probabilistic approach is needed to deal with such uncertainties.

The specific risk response strategy for each project strongly depends on the nature of the risk to be treated, the overall exposure the project or organization is subject to and the current stage of the project life cycle. In any case, project risk response should be discussed periodically to reassess existing risks, verify the adequacy and effectiveness of planned and implemented mitigation actions, and eventually to define additional measures. The identification and assessment of emerging risk is not a one-time exercise. It should be performed continuously. At all stages of the project lifecycle those discussions should occur—in particular, during regular management meetings—thus allowing executives to gain a better understanding of current risk and its potential impact on the project.


Accenture has identified four essential elements for making better decisions:

1. Structured data management—entails a process that enables the investors and management team to base the modeling of risks on robust input, in order to create sound probability distribution of each risk.

2. A sound process for risk identification and analysis to ensure that:
- Every risk is correctly identified in every stage of the project lifecycle.
- Risks are periodically reviewed as the project develops.
- Key expertise is leveraged during risk identification and analysis.
- The experts involved take responsibility for their estimates.
- An effective “lessons learned” process is in place which creates the basis for continuous improvement.

3. An adequate supporting IT system to provide:
- A systematic and structured collection of data into a “loss database”.
- The running of the probabilistic model itself (for large and complex capital projects).
- The production of easy-to-read reports with specific views which can facilitate comprehension of the results even by “non-experts”.

4. Clear and effective risk governance must ensure that:
- Clear roles and responsibilities for risk management are defined at every level of the organization.
- Prompt communication is established among different stakeholders.
- The risk management process is fully integrated into traditional investment/capital project management processes.


A. Leleux is an Executive Director – Risk Management, cross industry, corporate finance lead for France, treasury lead for Europe. Based in Paris, and with over 17 years of global consulting and industry experience in the risk and finance space Leleux guides large conglomerates and forward thinking organizations in the finance, resources, product and public sectors in transformation projects and assignments on their journey to high performance.

L. Malbernard is a Senior Manager – Risk Management, based in Paris. Specializing in corporate finance and corporate treasury transformational assignments, Malbernard provides risk management consulting services focused on operational and financial risks quantification and risk department organization for large energy, utilities, transportation and insurance sector firms determined to become high performance businesses.

Download below the report in english (PDF 16 pages)

accenture_risk_modeling_drive_capital_project_performance.pdf Accenture-Risk-Modeling-Drive-Capital-Project-Performance.pdf  (1.16 Mo)

Lundi 3 Décembre 2012

Nouveau commentaire :

Your email address will not be published. Required fields are marked *
Votre adresse de messagerie ne sera pas publiée. Les champs obligatoires sont indiqués avec *

Recevez la newsletter quotidienne


Le Magazine

Lettres métiers

Livres Blancs