Artificial Intelligence Has a Role in Community Bank M&A

“If you build it, he will come.” – “Field of Dreams” 1989

I use this famous quote from the classic baseball film Field of Dreams somewhat tongue-in-cheek. Many vendors and tech pundits seem to be pushing everyone to buy, install and let artificial intelligence rip through their organizations without so much as a game plan — “If you build it, he will come.” He, of course, meaning profits galore, God-like insights and your face on the cover of a magazine.

In all seriousness, AI plays an increasingly significant role in mergers and acquisitions (M&A), transforming how deals are identified, evaluated and executed.

Here are some key examples of how AI can impact your bank’s M&A process.

Target Identification and Screening

• Predictive analytics: AI can analyze vast amounts of data from various sources to identify potential acquisition targets. It helps predict which banks will likely be open to an acquisition or divestiture based on their financial health, market positioning and other indicators.
• Pattern recognition: AI tools can recognize patterns and correlations that human analysts might miss, providing insights into potential acquisition targets, including fintechs that may fit your bank’s strategy.

Due Diligence

• Accelerated processes: AI is not just a buzzword but a powerful tool revolutionizing the M&A landscape. It can significantly speed up the due diligence process by automating the analysis of large volumes of documents, contracts and financial records.
• Thorough analysis: Natural language processing (NLP) tools extract relevant information, flag potential risks and provide summaries, making the due diligence process more efficient and productive for community banks and ensuring a thorough and comprehensive review.
• Risk assessment: AI offers a powerful capability to assess risks. By analyzing past performance, compliance records and market conditions, AI can predict potential future risks using machine learning models.

Valuation and Deal Structuring

• Valuation models: AI-driven models can provide more-accurate bank valuations by incorporating real-time data and a more comprehensive range of variables than traditional methods.
• Scenario analysis: AI can run multiple scenarios and simulations to predict the outcomes of different deal structures, helping decision-makers choose the most favorable terms.

Integration Planning

• Post-merger integration (PMI): AI can assist in creating detailed integration plans by analyzing both banks’ cultural, operational and financial data. It can identify potential synergies and areas where integration might face challenges.
• Talent and culture fit: AI tools can assess the cultural compatibility of merging banks by analyzing communication patterns, employee sentiment and organizational structures.

Negotiation and Strategy

• Negotiation support: AI can provide real-time analysis and recommendations during negotiations by analyzing the other banks’ behavior, historical negotiation tactics and market conditions. It helps in making data-driven decisions during deal discussions.
• Strategic alignment: AI can help align M&A strategies with broader corporate goals by analyzing how potential acquisitions fit your bank’s long-term vision and objectives.

Legal and Compliance

• Contract analysis: AI can streamline the review of legal documents, identifying key terms, clauses and potential legal risks. This ensures compliance and reduces the risk of post-transaction disputes.
• Regulatory compliance: AI tools can help ensure the transaction complies with all relevant banking regulations by cross-referencing the deal terms with regulatory requirements.

How can your bank prepare to utilize these AI M&A benefits?
1. Assign an AI czar or committee to monitor general progress in AI and more specifically in banking.
2. Clearly define your goals and objectives.
3. AI relies on data to make accurate predictions and decisions. Ensuring your data is correct, relevant and up-to-date is essential.
4. When selecting AI tools and technologies, consider various aspects, such as affordability, scalability and user-friendliness.
5. It is essential to develop a robust data strategy that includes data collection, storage, processing and analysis.
6. Invest in training and education, which may include training in data science, machine learning or other AI-related skills.
7. It’s important to start small and scale up gradually.

Analyze performance metrics such as accuracy, speed and efficiency, monitor user feedback and adjust your bank’s AI algorithms or data strategy based on what you learn.