Artificial intelligence has been compared to a brilliant but overconfident junior professional: fast, persuasive, and wrong just often enough to cause serious problems if people stop thinking critically. That description fits its current role in mergers and acquisitions, particularly in banking and financial services.
AI is already changing which deals are pursued, how quickly transactions progress, and how much value survives after closing. It enables deal teams to analyze more data in less time than ever before. At the same time, it can accelerate bad assumptions, expose weak governance, and create new lines of inquiry for regulators. Understanding how to use AI as a disciplined tool – not as an oracle – is now a core competency for banks, credit unions, and FinTechs that are active in the deal market.
AI as a force multiplier – on both sides of the ledger
In the M&A context, AI functions as a force multiplier. It does not change a bank’s strategic objectives; it alters the speed and scale at which those objectives are pursued. When underlying governance, data practices, and integration discipline are strong, AI enhances those strengths. When they are weak, AI tends to magnify the weaknesses.
On the positive side, AI-driven tools now influence three key areas of transaction activity. First, they shape target identification by surfacing institutions that fit strategic, geographic, or portfolio profiles that might otherwise be overlooked. Second, they accelerate due diligence, allowing teams to process large volumes of contracts, performance data, and operational information in compressed timeframes. Third, they inform integration planning by highlighting overlaps, redundancies, and risk concentrations across systems, products, and customer bases.
However, the same capabilities can create trouble when they are used uncritically. Sophisticated dashboards and polished AI outputs can create a false sense of certainty. If data quality is poor, incentives are misaligned, or no one is asking hard questions about how the models work, AI will simply help the organization make bad decisions faster and with more confidence.
Target identification: pattern recognition with real consequences
The earliest impact of AI in M&A appears in target identification and strategic planning. Modern AI systems excel at pattern recognition across large, messy data sets. In banking and FinTech, that capability is being applied to internal and external data such as historic financial performance, loan and deposit behavior, interest-rate sensitivity, complaint and fraud patterns, branch-level profitability, and even public sentiment or social signals.
This allows acquirers to move beyond traditional relationship-driven pipelines and simple ratio screens. AI models can highlight institutions that resemble a bank’s most successful past acquisitions, identify peers whose performance is beginning to diverge from the market, or flag markets where customer and deposit behavior align with a buyer’s strengths. The result is a more dynamic, data-informed view of who might be a good candidate to acquire – or to be acquired by.
But this power comes with two conditions. First, decision-makers must understand the data and logic behind the AI’s recommendations. A bare statement that “the model says this is a great target” does not satisfy boards or regulators. They will expect clarity about what data was used, which factors were weighted, what was excluded, and what assumptions were made. Second, data quality still controls the outcome. Poorly integrated internal data and unreliable third-party data can lead to elegant but misleading conclusions. No amount of AI sophistication can rescue a strategy built on bad inputs.
Due diligence: faster, deeper review – not a compliance replacement
Due diligence is the area where AI’s impact is most visible today. Well-configured tools can read and summarize large volumes of contracts, flag key clauses related to change-of-control, regulatory obligations, data-use restrictions, and unusual risk-shifting, and categorize recurring themes across policy documents and operational manuals. They can also analyze performance data, portfolio characteristics, and operational metrics at a level that would be impractical with manual methods alone.
These capabilities are particularly valuable for triage. AI can help deal teams quickly identify which areas deserve in-depth follow-up, which agreements appear standard, and which policies diverge from the acquirer’s norms. For cross-institutional comparisons – across branches, regions, product lines, or customer segments – AI is often the only realistic way to see the full picture within a typical deal timetable.
However, AI is not a substitute for legal, compliance, or risk diligence. One of the greatest dangers is overreliance. Modern AI systems present their conclusions with impressive fluency and confidence, even when they are incomplete or incorrect. They are also poor at signaling what is missing from the record. Without subject-matter expertise to interpret the output, teams may miss critical context, nuance, or blind spots.
For that reason, best practice is to treat AI as an advanced first-pass reviewer. It can structure information, identify patterns, and produce draft summaries and issue lists. Human experts must still verify, contextualize, and challenge those outputs before they become the basis for board-level recommendations or regulatory submissions. The organizations that use AI most effectively in diligence are those that combine robust tooling with disciplined human review.
There is also an important legal and contractual overlay. Before diligence data is fed into any AI system, acquirers must confirm they have the right to process that data in that manner and that the platform itself has been vetted like any other critical vendor. Confidential supervisory information, for example, may be subject to strict handling limits. Many contracts contain data-use restrictions or limitations on onward transfer. AI platforms differ significantly in how they store, secure, and retain data, and whether they use customer inputs for model training. If a bank cannot explain these details to examiners, it is not ready to rely on the tool.
Regulators: AI does not shift accountability
Supervisory authorities have made clear in various contexts that AI does not shift accountability away from boards and senior management. When AI is used in M&A – whether for target identification, valuation analysis, or transaction structuring – regulators remain focused on the quality and integrity of the overall decision-making process.
In practice, this means several things. AI vendors and platforms should be incorporated into standard third-party risk management programs, including security, resilience, and compliance assessments. Models used to inform material decisions may fall under existing model-risk management frameworks and thus require documentation, validation, and ongoing monitoring. Institutions should be prepared to demonstrate that AI outputs were reviewed and validated by qualified personnel and that they did not bypass or weaken existing governance structures.
Documentation becomes particularly important. Institutions that use AI in their deal processes should keep clear records of which tools were used, for what purposes, and on what data; what assumptions and time periods were involved; how AI-generated analyses compared with traditional methods; and how the resulting insights were presented to and understood by the board. Regulators increasingly view AI as part of the story of how a decision was made. That story must be coherent, transparent, and defensible under examination.
Integration: where AI either pays off or exposes weaknesses
The real test of any transaction is integration. Announcements are made at signing; value is realized – or lost – in the months and years after closing. Here again, AI has significant potential and significant limitations.
On the potential side, AI can rapidly map overlapping systems, products, and processes across two organizations. It can identify redundant branches and platforms, highlight operational bottlenecks, and model how different integration scenarios are likely to affect customer behavior, deposit stability, and service levels. Once integration begins, AI-driven monitoring can track key performance indicators in near real time, flagging early signs of attrition, fraud, operational incidents, or service degradation that might previously have gone unnoticed until quarter-end reports.
What AI cannot do is make the strategic and cultural choices that determine whether an integration succeeds. It does not decide which system becomes the system of record, which products are sunset, how risk appetite is harmonized, or how policy and culture conflicts are resolved. It will, however, expose unresolved decisions and weak governance faster and more visibly. Unclear ownership, conflicting procedures, and lax access controls all tend to surface quickly when AI systems begin analyzing integrated data.
Culture is another area where AI’s limits are evident. No amount of analytics can eliminate the friction that comes from combining institutions with fundamentally different approaches to risk, compliance, or customer treatment. At best, AI can help identify where cultural conflicts are manifesting in metrics; it cannot resolve them.
Characteristics of institutions that use AI effectively in M&A
Experience across banking and financial services suggests that institutions gaining real value from AI in M&A share several characteristics.
They have strong fundamentals: relatively clean and well-governed data, clear documentation, and mature integration playbooks. AI enhances these strengths rather than compensating for deficiencies. They treat AI as a professional tool – powerful but fallible. Deal teams receive training not only on how to use the tools, but also on their limitations and failure modes. Senior leaders, including general counsel, CISOs, and heads of corporate development, remain actively engaged in decisions about where and how AI is deployed.
They embed AI into existing governance frameworks instead of creating parallel, unregulated workflows. Model-risk, third-party risk, and AI governance structures are involved early in planning. AI vendors and data brokers are evaluated with the same rigor as core technology providers. The mindset is one of defensibility: decisions are made with an eye toward how they would be explained to regulators, shareholders, or courts if challenged later.
Most importantly, these institutions keep human accountability front and center. Regardless of how sophisticated the tools become, a named individual – or body – still owns the recommendation to proceed with a transaction at a particular price and on particular terms. AI may inform that recommendation, but it does not replace the judgment.
AI is now a permanent feature of the M&A landscape in banking and financial services. The key question is not whether it will be used, but how. Institutions that treat AI as a powerful assistant, integrate it into strong governance, and remain candid about their own data and cultural realities are best positioned to use it to kill bad deals earlier, surface risks sooner, and preserve more of the value they promise. Those that treat it as a black-box oracle will eventually be reminded – by regulators, investors, or events – that accountability never left human hands.
This blog was drafted by Spencer Fane attorneys Shelli Clarkston, Mike Patterson, and Shawn Tuma, Chief Information Officer Allen Darrah, and Paul Schaus, the Managing Partner and Founder of CCG Catalyst.
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