How Leveraging AI For Deal Evaluations Provides A Competitive Edge

Integrating artificial intelligence (AI) into private equity firms' deal evaluation process can greatly enhance assessments, speed up decision making through automation and provide a potential...
United States Corporate/Commercial Law
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Integrating artificial intelligence (AI) into private equity firms' deal evaluation process can greatly enhance assessments, speed up decision making through automation and provide a potential competitive edge in an ever-evolving deal landscape.

To reap the benefits of AI, firms must leverage proprietary decision trees and focus on data preparedness, two key components of a successful strategy. These two fundamental tactics provide a good starting point for firms that are newly embarking on the AI journey.

Current to Future State—How Can Private Equity Firms Integrate AI to Gain a Competitive Advantage?

Deal evaluation has historically been a manual and time-intensive process. Most funds look at approximately 3,000 Confidential Information Memorandums (CIMs) per year with a handful moving to a letter of intent status. Analysts must review each CIM, manually rating whether a deal fits the fund's criteria, analyzing market sentiment reports and conducting other time-consuming tasks. Leveraging AI can greatly reduce manual work, improve data quality and create scalability while enhancing a firm's proprietary advantage.

To set up a proprietary flow, firms must incorporate their "score card" or go/no-go decision trees and focus on data preparedness to establish that the applicable data is accessible and consumable. As an example, a private equity firm may score opportunities based on sector, customer sentiment, growth opportunities and size of business, among other variables. A combination of analyzing a CIM, a market report on the potential target and other data could help with a go/no-go decision and reduce evaluation time significantly. Funds can choose to include data such as past deals and decisions, third-party acquired data or government data, among other sources.

Preparing the data — ensuring it is well cataloged and accessible — is a crucial step to enable a centralized and effective AI system.

AI in Practice—What Role Would AI Play in Deal Evaluation?

In addition to establishing a proprietary decision tree and preparing the data, further insights into the potential target can be gleaned efficiently and quickly by using AI. In the case of a financial services institution, for example, scanning and analyzing call reports and enforcement action documents from the Federal Financial Institutions Examination Council (FFIEC) and Federal Deposit Insurance Corporation (FDIC) could uncover insights into the institution, its competitors and opportunities that might otherwise be missed due to human error.

One way to understand how this would work would be to incorporate a node in the decision tree when evaluating these institutions to analyze the target company's standing with the FDIC by retrieving the relevant FDIC data and providing a "score" for that section. Similarly, the integrated AI system should be built to work section by section through the score card, retrieving the relevant information and producing a score. At the final stage, the AI system can summarize the work done into a final decision on whether to continue to evaluate or deprioritize the CIM/opportunity.

Future State in Action—How Can Private Equity Firms Stay in Front of AI?

With firms facing an incoming flow of 3,000-plus CIMs to process annually, the integrated AI system serves as a stage in the conversion funnel. The human analyst engages only in those deals where the score is above the desired threshold. A smaller haystack isn't the only benefit here. The AI has also helpfully pointed the analyst to where they need to spend time while sifting through the hay by pre-generating the evidence needed to create the score for each stage. With this, an analyst isn't only given a decision tree and a CIM, but also the data and evidence for each stage that determined the score via the AI.

Incorporating large language models into the deal sourcing process would shift an analyst's time from manually reading every CIM and scoring deals, to identifying the sources that will provide the most comprehensive insights and providing the final human perspective in the loop to reduce false-positive target candidates and confirm quality of output.

If firms aren't thinking about AI and creating a plan on how to prepare and implement it, they will soon fall behind. From proactive searches to analysis of incoming deal flow, AI will be an empowering assistant to make all firms more intelligent in their processes.

Originally published by 16 March, 2024

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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