Quantifying Contract Risk-Reward

How to evaluate the cost of a contract and why that’s important

Contracts inherently have value and risk. This is clear. What is perhaps less clear, however, is just how we go about defining those costs and the knowledge gap there is significant. If contracting parties operate without a clear understanding of their objectives or future circumstances — not to mention the means to validate objectives — then, the only approach to contract negotiation and optimization is through human judgment. Plainly, that is not enough.

In classical economic theory, actors have rational objectives to maximize utility and profit. They act independently upon complete information and can verify such data. Modern theories challenge these assumptions. Notably, incentive theory, incomplete contract theory, and transaction cost theory, collectively assert that the parties’ rational objectives are limited, their information is incomplete and asymmetric, and their objectives cannot be validated. That leaves us in an unfortunate spot particularly as it does not help us quantify contract risk-reward.

Axel Leijonhufvud, a Swedish economist, proposes a better approach. He writes: “Instead of looking for an alternative to replace [classical theory], we should try to imagine an economic theory to transcend its limitations.” In other words, he raises the possibility that data science — applying AI tools — can help professionals define optimal objectives and provide the parties with verifiable data.

How to value contracts

While modern theories observe that contracting objectives will not always be fully and definitively defined, the classical theory still holds that contracts are economic instruments measured in a range from pure profit, to social responsibility, to emotional values. Moreover, without some understanding of the objectives then, as Peter Drucker noted, “you can’t manage what you can’t measure.” In other words, you cannot know if your contracts are providing value unless measurable objectives are defined.

Filling the knowledge gap and defining what information is required to make informed decisions is the next necessary step towards defining contract risk-reward. That means understanding how much a contract costs to prepare, negotiate, and administer and how this cost compares to contract value. Does the agreement increase value by mitigating risks or reduce margins through increased costs?

You can’t manage what you can’t measure. Data science and the application of AI tools can finally help.

In general, costs increase with contract length, complexity, and variance from standards. However, these core costs are subject to several expense multipliers (including review time, number of reviews, number of reviewers, and pay scale of the reviewers), meaning that contract costs rise exponentially with length and complexity. This information allows for informed decisions and can suggest whether shorter, streamlined contracts, as opposed to longer, more comprehensive agreements, ultimately reduce costs in an amount greater than any additional risk exposure.

With this information, we can measure the cumulative value or cost of a contract by placing value on every term from each party’s perspective and gather insights on the most important deal terms. Some provisions, such as a warranty clause, are relatively easy to value. We can calculate the value (or cost) by the average cost of warranty remediation, multiplied by the percentage number of returns. Even the value of a counterparts clause can be determined. It is the cost savings of not having to mail the agreements for individual signature and the reduced risk of any future disputes. You can perform the exercise for every contract term with the following information.

  • Describe the value, benefit, or cost of the contract provision.
  • Give it a financial value.
  • Assess whether receipt or payment be accelerated or delayed. If so, by how many days.
  • Apply a probability of occurrence in percentage terms.

Use the formula to determine the effect on net margin:

Adjustment = (value/cost plus or minus acceleration or deferral) x probability

The results of the analysis can be surprising. For example, a 90-day payment term — 60 days longer than market norms — means that the payee will lack the use of the funds, which could have been invested in the company’s business, for a more extended period. If the company’s net margin is 10%, then this deferral will cost the company 1.5 cents for each dollar deferred, reducing its margin by 1.5%. In this case, the value is relatively low, but the probability of occurrence is 100%. On the other hand, an indemnity clause can expose the company to loss of full contract value. However, the likelihood of loss may be one in a thousand. In this case, the cost is high. But the probability is low. The resulting impact on the net margin is 0.5% or less than the longer payment terms.

A valuation exercise for each contract term will show that the values will not be the same for each party. If tabulated, the columns displaying each party’s valuations will highlight the critical objectives of each side. Moreover, because the valuations will differ, the resulting barter is not a zero-sum game. We can then apply game theory, such a Nash equilibrium, to model outcomes for each negotiation point and compare this to real-world data.

Validating analyses and applying insights

The data-driven approach outlined above does not mean that we should attempt to measure every contractual term to the nth degree. Instead, the analysis provides insights into the primary contractual drivers of value and potential value leakage sources. Most importantly, it allows us to build analytical models for testing and evaluation.

Why is this so important?

It allows us to model and apply contract negotiation strategies to automated contract review. We can use equilibrium or barter analysis to create fair and balanced contracts for a wide range of transactions that can serve as no-touch contracts and significantly reduce costs and mitigate risks. For more complex negotiations, the approach — backed with AI tools — can run AI simulations to create balanced negotiation positions. With this approach — buoyed by machine-driven analytic power — a practical and viable method for quantifying contract risk-reward is suddenly within reach. Its application has the potential to save enterprises millions of dollars in legal fees, countless man-hours, and the ability to properly prioritize legal resources. In other words, it’s significant!