METHOD OF ASSESSING PROJECT CREDIT RISK FIELD OF THE INVENTION

The present invention relates broadly to a computer-implemented method of assessing credit risk for a project, such as an infrastructure construction project. BACKGROUND OF THE INVENTION

Construction risk on Individual construction projects is typically assessed by contractor organisations on the basis of 'contractor experience'. Of necessity, this experience is limited to the immediate experience of individuals within the particular organisation. Contingency allowances for unexpected risks are typically allowed on the same basis. Thus the limitation on projection reliability is the scope of the experience of the individuals making the judgements.

Over the past decade, software has been developed which allows Monte Carlo simulations to be undertaken on overall cost estimates by assigning outcome ranges to discrete components of the project costs estimate. Of necessity, input to these packages requires subjective judgement, and the worth of that judgement depends entirely on the experience of those making that judgement. Thus while these tools are growing in usage, the results of analyses undertaken with them remain limited by the experience and/or judgement of the anafyst(s). External parties (such as auditors, banks & lending organisations) wishing to rely on those judgements will rarely be in a position to challenge the contractor's judgements in a meaningful way, whether those judgements are based solely on experience or are added to by the numerical results derived using Monte Carlo simulations in the manner described above. Hence project specific outcomes will typically be driven by advice given by persons directly or indirectly associated with the project cost estimate. There is presently no reliable method for independently assessing the financial or time risks of specific construction projects.

SUMMARY OF THE INVENTION According to the present invention there is provided computer-implemented method of assessing credit risk for a project, the method comprising the steps of: providing preselected fields relating to risk factors associated with (i) the project itself, and (ii) one or more contractors to be engaged for the project; inputting project specific data and contractor specific data into the preselected fields of the respective risk factors; and applying a predetermined algorithm to the project and the contractor specific data to provide a quantitative indication of the credit risk for the project.

Preferably the method also comprises the steps of: assessing the project specific data to derive a project risk; assessing the contractor specific data to derive a contractor risk.

More preferably these assessments are qualitative. Even more preferably the predetermined algorithm is applied to the project and the contractor risks.

Preferably the step of providing preselected fields involves categorising either or both of the risk factors associated with the project and the contractor. The categories relating to the project itself may include design complexity, time constraints, working conditions and market factors. The categories relating to the contractor may include familiarity with the project and key staff availability. Preferably the method further comprises the step of providing preselected fields relating to risk factors associated with a combination of the project and the contractor. More preferably the method involves inputting data into these combined fields. This may involve setting categories of various worktypes relating to a project and inputting financial data into each of the worktypes, for example the percentage of that worktype (such as underground) in dollar terms based on the total cost of the project.

Preferably the step of applying the predetermined algorithm to the project and contractor risks fncludes computation of a probabilistic cash outcome distribution for the project. More preferably application of the predetermined algorithm together with project pricing and cost allows calculation of a probabilistic cost and cash outcome distribution for the project.

BRIEF DESCRIPTION OF THE FIGURES

In order to achieve a better understanding of the nature of the present invention a preferred embodiment of the method of credit risk assessment will now be described, by way of example only, with reference to the exemplary screenshots of Figures 1 to 10.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In a preferred embodiment of the invention there is a method of assessing credit risk for a project, such as an infrastructure construction project. The method is implemented with computer software that provides a systematic risk assessment model. This model in its preferred form gives an insight into project risk factors including:

(i) project uncertainty from a construction contractor's perspective , including how contractor's deal with that uncertainty in their pricing and its impact on financial outcomes at project and business level; and

(ii) how project and corporate uncertainty may impact upon project guarantors or financiers of major construction projects.

The computer software of this embodiment has a number of applications and potential end users who fall into the categories of:

(i) financial institutions who assume project risk including equity, debt, underwriting, guarantees, looking to:

(A) quantify thθ risk of cost overrun;

(B) assess the probability of contractor default;

(C) quantify a loss given contractor default

(D) quantify the adequacy of security provisions; and (E) monitor project credit risk during construction;

(ii) rating agencies to create or enhance their existing rating approaches for:

(A) construction phase of PPP (private financed) projects;

(B) operation phase of PPP projects; and

(C) contracting organisations; (iii) contractors for use by their executive teams for:

(A) margin setting on new projects;

(B) quantifying risk and return of portfolio, historically, existing and projected; and

(C) communicating with project financiers in a language understood by the financial market.

In this embodiment, the software in determining the project credit risk assesses a combination of factors specific to the project together with factors related to contractor(s) engaged lor the project. The following example is intended to better illustrate how this is achieved in terms of selecting and assessing these risks factors and quantitatively combining risk values derived from each of these factors to arrive at a measure of the credit risk.

The preferred software has 4 primary modules/applications, namely: (i) a project credit risk assessment tool; (ii) a contractor's whole business assessment module; (iii) a project default cost completion application; and (iv) a project and contract risk monitoring tool.

The first module and key aspects of the invention reside in the risk assessment tool where for example it is possible to quantitatively assess the probable cash outcome of a project from the contractors perspective. In this embodiment there are 6 steps in the analysis of the probable cash outcome. Figures 1 to 7 are exemplary screenshots for each of these steps. The first step relates to a combination of project and contractor associated risk factors. The second step Is directed to factors which relate to specifics of the project itself whereas the third step is directed to contractor-specific factors. The remaining 3 steps involve the application of an algorithm to the entered data in order to calculate and illustrate the cost outcome distribution and credit risk. It is recognised in this method that each project may include a number of different work types.

Therefore, step 1 of the analysis involves setting the percentage on a total cost estimate basis for each work type as shown in Figure 1. For each work type a standardised risk distribution has been determined based on one or more of the following:

• a statistical analysis of actual contractor data; • empirical data derived from studies relating to the accuracy of construction cost estimates;

• benchmarking data from the American construction industry; and

• empirical data derived from experience of the inventors.

Step 2 of the method focuses on project specific risks. The project specific risks are generally independent of the contractor undertaking the project. In this example and as illustrated in the screenshot of Figure 2, four (4) categories of specific risk have been identified, as being the risks that are most likely to influence project cost outcome. The identification of these four (4) main categories has been based on experience of the inventors and empirical data. The four (4) categories of project specific risks are:

• design complexity; • time constraints;

• working conditions; and

• market factors.

For each of the four (4) categories of project specific risks there are in this example further classification of each risk as follows: • Design complexity: o standard; o unusual; o unique.

• Time constraints: o normal;

o rapid delivery required.

• Working conditions: • o normal industrial relation risks for a region; o special environmental or working conditions. • Market lactors: o average demand; o excess demand.

Step 3 of the method focuses on contractor specific risks. In this example and as illustrated in the screenshot of Figure 3, the two (2) categories of contractor specific risks are: • contractor familiarity with work type; and

• availability of key staff with direct experience of similar work.

For each of the contractor specific risks of this embodiment there is a "Yes" or "No ^* classification.

With the Work Type, Project Specific Risks and Contractor Specific Risks set, a risk matrix algorithm computes an adjusted project premium based on a generic risk value for each work type on the project. A weighted average of each of the adjusted project risk premiums is then used to determine a probabilistic cost outcome for the project.

Step 4 of the method focuses on Project pricing and costing. The initial project price as provided by the contractor or as estimated by the users is the starting point for this step, in this example $110 million, A margin, expressed as a percentage of the selling price, in this case 10%, is then applied to calculate the initial cost estimate, in this example $99 million. The margin is in this embodiment a gross project margin and includes contingency and an allowance for recovery of head office or offsitβ overhead amounts.

With the Work Type, Project Specific Risks and Contractor Specific Risks and Project Pricing and Costing set, a risk matrix algorithm computes a cost and cash outcome distribution for the project. Figure 5 shows a sample risk matrix. The risk matrix is an important element in this aspect of the invention. The factors included in the risk matrix come from the data described at the top of page 6. The factors included under each of the columns "Generic Base Score" (both columns), and all the columns under "PROJECT RATING SYSTEM- QUALITATIVE SCORES" represent empirical data from direct experience of the inventors and may be varied in certain circumstances. The matrix provides preselected fields in which for this example qualitative data or scores are input for both the project specific risk factors and the contractor specific risk factors.

The first column of the matrix of Figure 5 is the work type and the second column allocates a cost to this work type. The third column is the dollar value for the work type expressed as a percentage of the total estimate of all work types for the project. The red numbers in the matrix represent the

choice of factors for the example project. The summation of the red numbers under "PROJECT RATING SYSTEM- QUALITATIVE SCORES" and the "Generic base Score" numbers In the righthand of the two columns gives the project rating score in the third to last column "Actual Rating". For example, "Roads & Bridges, Marine" has a summed rating of 8 from a base score of 7 added to a qualitative "Design" score of 1. In this example the "CONTACTOR RATING" scores for the risk factors of "Familiarity" and "Key People" have each been qualitatively assessed as "0" meaning the contractor is well suited and staffed for the project.

The two columns under "PROJECT UNCERTAINTY FACTOR" give the factors to be applied to compute the overall project risk factor or rating represented by the calculated standard deviation. The Outcome Std deviation for Rating Value" is (Actual rating)/(Generic base score)x(Genβric base score Std deviation). Thus for "Building" (4/4)x6%= 6%; for "Roads & Bridges, Marine" (8/7)x10.5% = 12%, & so on. The "Actual weighted score" is the standard deviation calculated from (% work type) x {Outcome Std Deviation"). For example, for "Building" 45%x6%= 2.7%, for "Roads & Bridges, Marine 40.8%x12%= 4.9% and so on. The standard deviation for the project rating is then the sum of the "Actual weighted score" for each. In this case, the overall project risk factor or rating is represented by a standard deviation of 9.1%.

The risk matrix in this example starts with a base standard deviation for each work type for a generic project. The values can vary between a standard deviation of 6% to greater than 15% for different work types. The risk matrix identifies a worst case for each work type for a generic project, excluding consideration of "Special Risks, Project Specific". The matrix allows one to make a series of special provisions for unusual factors. Excluding the Special Risks allowance, the worst case varies between a standard deviation of 10% to 25%. With tha addition of Special Risks, one can move up substantially on these limits.

In this embodiment Specific Project and Contractor risks of project are qualitatively used to assess the actual project risk when undertaken by the actual contractor under consideration. The 'Contractor risk" factor is a function of the experience record of both personnel Immediately available for a specific project and the organisational experience with the specific work type. Thus while there are 'average' basic standard deviation values for specific work types, the range is a function of the above two factors. As an example, personnel with direct and immediate experience of the work type(s) involved in a specific project will generally produce a significantly better outcome than those who have little or no direct experience. This principle can be taken into account when projecting production targets. Organisations with intermittent experience in the specific work type are likely to perform significantly worse than those with continuous experience in the same work type. The combination of an experienced organisation with an experienced workforce is highly likely to produce a much better result than that achieved by an organisation with intermittent experience in the specific work type and an inexperienced workforce. The factors included in the risk matrix of Figure 5 to account for these performance variables within each work type are based on empirical data derived by the inventors over a number of years of direct observation and experience

A project risk value Is determined by calculating the weighted average of the various work type risk premiums. Both the inventors' experience (including a considerable record of actual project data accumulated over a number of years) and numerous studies by academic institutions around the world have determined that the outcome cost can reasonably be represented by a normal distribution. Adopting this as the case, the calculated project risk vaJue or project standard deviation can be used to determine the probabilistic cost outcome. Using the contract price and an assessment of the contractor's margin, a deterministic value for the mean outturn cost is calculated. Using this mean outturn cost, the probabilistic project risk values for standard deviation and assuming the distribution will be normal, a probabilistic outturn cost is generated. To determine the cash outcome from the contractor's perspective, the probabilistic outturn cost is subtracted from the contractor's contract price. The cash outcome is also expressed as a percentage of trie contract price.

Step 5 of the method displays the computed cost outcome distribution calculated in Step 3. In this example and as shown in Figure 6, the contractor's project cost outcome is shown both as a cumulative cost distribution and as a normal probability distribution function. The initial cost estimate of $99 million and the initial selling price of $110 million are shown below the 2 graphs to illustrate the difference between cost and price. There is also a check function shown to display the standard deviation for the outcome distribution. The mean of the outcome distribution will be the initial cost estimate value of in this example $99 million. Step 6 of the method illustrates the computed cost outcome distribution and the contractor's cash position. As shown in the exemplary screenshot of Figure 7 the contractor's cash position is shown on a histogram with probability outcome on the y-axis and dollar amount on the x-axis. Each of the histogram bars are conditionally formatted so a cash result equal to or greater than margin is shown in green, a value less than the margin but greater than zero is shown in yellow and values less than zero are shown in red. In this example a summary of the 99% probability level is shown with values for the final cost outcome at the 99% confidence level. In this case there is a 99% probability that the cost of the project will not exceed $114.6 million which means that the contractor's loss from the project will not exceed $4.6 million (based on a contractor's selling price of $110 million). Figures 8 and 9 are other screenshots of this example showing how input data can be varied and dynamically tested for its affect on project credit risk. Figure 8 shows the worktype as 100% "Standard bldg" whereas for the same project pricing Figure 9 shows the worktype mix covering a combination of 50% "Standard bldg", 15% "Building services" and the balance in other worktypes. It can be seen that Figure 9 (with a 99% probability of the project not exceeding $124.7 million) in effect provides a lower confidence level as compared to Figure 8 (with $114.6 million for the same confidence level).

The second module of this embodiment of the preferred software solution focuses on the volatility of profits from a contractor's portfolio of projects. Assessing a contractor's portfolio of projects business involves consideration of the factors affecting corporate profit outcomes.

A typical contractor has a number of projects running simultaneously. All projects are subject to variability, with the major differences between projects being the extent of variability consistent with work type and project specific issues relevant to market factors and relevant staff experience at the time. A structured series of questions, developed by the inventors, is used to obtain the relevant turnover ranking and work mix data from the contractor. By considering the individual projects within a contractor's portfolio on a generic basis, a weighted average value for portfolio risk is quantified.

Provided a significant proportion of a contractor's annual turnover is not dependent on a small number of projects, financial outcome variations will generally be substantially smoothed relative to individual project outcomes. In this embodiment and in assessing contractor specific risk factors the portfolio computation proceeds as follows. The standard deviation of a group of N approximately equal value projects, each with Standard deviation V is well established mathematically as σλ/N . The inventors have developed a series of structured questions which allow a reasonable representation of a Contractor's portfolio by Work Type and Project size. It is generally understood that around 80% of a contractor's turnover typically comes from about 20% of projects by number, and it is these projects which are likely to have the greatest impact on portfolio outcome. Combining the information obtained from the structured questions with the appropriate risk values derived from the risk matrix shown in Figure 5 allows computation of the Standard deviation of a Contractor's portfolio of projects. Once that information is coupled with balance sheet information, the probability distribution of the Contractor's gross margin earned from construction operations can bθ computed. Hence a contractor's expected profit (being gross margin achieved on all projects less corporate overheads) can be expressed in probability terms as either dollars or a percentage of expected or actual turnover. Using the historic profit distribution profile and the target project financials, a projected profit distribution profile is calculated. The portfolio outcome is at least as important as the project outcome, since financial default may arise from factors quite unrelated to the specific project under consideration. The projected distribution of likely profit can be used by various credit committees to determine the likelihood of a contractor default

The third module of this embodiment of the preferred software solution is directed to a scenario where in the event of a contractor default, the project sponsors or guarantors step in to complete remaining construction. This module of the method is used to assess the cost to complete at various stages throughout the project, figure 10 is a visual representation of the expected premium required to complete the remaining works of a project. This module can be used by financial institutions to assess the level and timing of the amount of their capital they are required to

provision for their borrowing or financial guarantee. It can also be used to assess the level and timing of security to be held against the contractor.

The fourth module of this embodiment of the preferred software solution allows for progress to be monitored once construction is underway. This application includes a methodology for assisting users to track project progression against expected performance targets and to quantify the forecast cost and time to completion throughout the project. It also allows financiers to monitor changes in risk in the contractor's portfolio of projects. This approach includes a process for obtaining relevant data for progressively updating the project credit risk assessment model.

Now that a preferred embodiment of the present invention has been described it will be apparent to those skilled in the art that the method of assessing project credit risk has at thø least the following advantages:

1. The preferred method provides a tool for independently assessing the financial risk associated with specific construction projects;

2. The methodology enables an independent third party or financier to establish confidence limits on project outcome versus expectation for any particular project and to extend this to a contractor organisations portfolio of projects in progress at the time of commencement of any particular project, with and without the particular project;

3. Trie method allows a lending organisation to logically and consistently compute probability confidence limits for the "probability of default" (which factor includes consideration of both the specific project and th© contractor's portfolio of projects) and

"loss given default" (being the two factors required by the international bank for settlements, BASEL Il provisions) when assessing credit risk of a project;

4. Associated aspects of the methodology allow one to clearly identify the sensitivity of a contractor's portfolio outcome to worktype mix and to identify in advance any significant change in portfolio risk, hence an organisations likely future financial performance compared to its historical performance;

5. Associated aspects of the methodology allow a lending organisation to logically and consistently recafculate balance sheet provfslons reasonably needed for any given project as it proceeds thus reducing provisions for projects running well and potentially increasing provisions for projects running into difficulties.

Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. For example, one can make a series of special provisions for very unusual factors, highly unique designs, chemical or manufacturing processes which involve untested technology or a significant scale up of

technology in use. The system and methodology allow for the inclusion of all such factors in the line "Special Risks, Project Specific". The risk factors within each of the project specific and contractor specific categories may vary from those factors described. The worktypes of the preferred matrix may also vary and still remain within the scope of the invention. All such variations and modifications are to bθ considered within the ambit of the present invention the nature of which is to be determined from the foregoing description.