“AI is taking M&A analytics to another level with much of the effort currently focussed on ‘smart automation’- making automation tools and processes smarter and more efficient”
With transactions becoming more complex and diverse, the M&A deal execution world continues to evolve. Acquirers are spending less time to assess their targets and using more time to justify their acquisition process as the timelines are getting shorter. The traditional approach of relying completely on spreadsheets to store and analyze data is becoming a thing of the past and so it’s important to create an alternative that not only simplifies the process of acquiring a target for the acquirers but also, improves the accuracy of their predictions according to the acquisition’s profitability simultaneously. Now, as a robust M&A model is becoming a norm for leaders around the world, can we consider Artificial Intelligence (AI) and analytics to be one of the possible solutions and rocket fuel for new possibilities in capabilities while handling M&A deals?
According to a Strategy Report by Accenture in 2019, advanced analytics can prove to be a boon for M&A deals. It can help in improving all stages of transactions, including deal identification, screening, pre-close planning and post-close-integration. Using traditional techniques might not help in identifying many potential targets that are usually small and private but using robust data sets and machine learning tools opens the door for the companies to create a set of acquisition possibilities to bring in the capabilities they are looking for in a considerably shorter time frame and a low cost.
Not Using AI/Analytics in M&A can make you fall behind
AI is taking M&A analytics to another level with much of the effort currently focussed on ‘smart automation’– making automation tools and processes smarter and more efficient. According to PwC’s AI Predictions 2019 survey, most of the biggest tech companies are betting on AI to improve productivity. With companies being able to harness the power of AI to grow their businesses, it is attracting a lot of venture capital investments signaling that the market for biggest AI acquisitions would be strong as companies mature and make their exits.
AI-powered apps allow M&A teams to rapidly adjust and normalize large data sets, with the processing power to evaluate multiple potential scenarios at speed. With the growth of the data sphere, and the rapid advancement of analytical tools and technologies capable of ingesting and analyzing data, a lot of processes across the M&A cycle right from initial identification of companies and screening of opportunities to enhanced AI financial due-diligence in-deal and value realization post-deal require a sophisticated AI integration plan that ensures a transaction to reach its optimal value.
Within M&A, the due-diligence process can be very complex, laborious and can also be inefficient sometimes. The reviewing of agreements, financial documents and contracts can create suffocating workloads that are monotonous and these characteristics become a catalyst for human errors. Therefore, to avoid such mishaps and to stay away from the repetitive nature of work involved in the due-diligence process, MergerWare tries to use AI and Machine Learning as effectively as possible to cross all the hurdles that come in the way of crossing all the phases in the M&A cycle.
Machine Learning in M&A
Machine Learning is considered to be one of the most powerful tools for predicting M&A and can also be used to augment fundamental analysis. In a research conducted by some students from Nicholas Centre of Corporate Finance and Investment Banking, where they developed a machine learning consulting project on ‘Which companies will be the target of an M&A acquisition in the next 12 months; they found out that their ensemble model proved to be most effective with 10.8 percent of its predictions in its test period being an acquisition target in the next 12 months. They also mentioned that this number was six times more than the base rate of M&A activity for the U.S listed private companies. They collected and analyzed over 1 billion data points and utilized neutral network, random models and ensemble models for their predictions.
AI and Machine Learning together can transform the M&A process by helping people in the Market and Data sector attraction, the Company selection process, M&A integration, M&A due-diligence and business valuation and also in the formation of existing strategies. Collectively, allowing machines to access and analyze the exponentially increasing data about economies, markets, companies and companies’ consumers can also help in the growth of the company. For more detailed information related to company growth read, ‘How to ensure growth of a company by M&A’
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