The End of the First-Draft DCF: What Anthropic's Finance Plugins Mean for Healthcare M&A

Last week, I posted on LinkedIn about Anthropic's new investment banking plugins for Claude. The post pulled in over 75,000 impressions. It resonated because, based on what I'm seeing, Anthropic just automated the core tasks of junior banking and corporate development workflows.

Automated DCF models. Instant comp tables. Synthesized due diligence data packs. All connected live to FactSet, S&P Global, and MSCI.

A few weeks prior, Anthropic released a legal AI plugin and Thomson Reuters experienced its worst single-day stock drop in history. Now the crosshairs are on finance.

From my vantage point, AI won't replace every M&A professional. But it will replace the ones who refuse to adapt. The analyst building first-draft DCFs and the associate maintaining deal models are seeing their core deliverables automated. This forces a critical question. Where does your value actually live?

Claude now connects directly to FactSet, S&P Global, and MSCI. It pulls live financial data without the standard copy-and-paste errors.

For valuation, the plugins generate a first-draft DCF on command. You provide the financials. Claude builds the architecture, populates the historicals, and runs projections.

I tested this myself. I had Claude build a working DCF template and an LBO model, both exported directly to Excel, in a matter of minutes. Clean, functional scratch models that would have taken an analyst hours from a blank workbook.

For market context, it generates precedent transaction and trading comp tables in seconds. For due diligence, it digests massive data room dumps and creates structured summaries with risk flags.

In my experience, deal teams historically spent 80 percent of their time building the model and 20 percent analyzing it. These tools flip that ratio. You spend 5 percent generating the baseline and 95 percent stress-testing assumptions.

Healthcare M&A involves complex revenue structures - capitated risk models, value-based care contracts, shifting fee-for-service schedules. The AI builds the base model. A human still has to adjust the architecture to reflect these realities. The bottleneck is no longer Excel execution. The bottleneck is critical thinking and deal judgment.

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You do not need to wait for your firm to approve an enterprise contract. Start using Claude today and treat it like a highly capable first-year analyst.

CIM Extraction
"Extract the historical revenue, EBITDA, and management add-backs by year into a clean table. Flag any inconsistencies in the add-back rationale and list the top three growth initiatives management is pitching."

Quality of Earnings Review
"Summarize the key differences between management's adjusted EBITDA and the auditor's proposed EBITDA. List the top three working capital risks and explain their potential impact on the purchase price."

Teaser Triage
"Based on these high-level metrics, calculate the implied valuation at 8x, 10x, and 12x EBITDA multiples. Outline the top five diligence questions we need to ask the broker on our first screening call."

Based on what I've seen across $1B+ in healthcare transactions, the professionals who thrive in this environment will stop competing on processing speed. Being the fastest person in Excel is no longer a sustainable career moat.

Use these tools to increase your throughput. Your value lies in knowing what the model actually means for the business.

I'll be sharing the AI-built DCF and LBO templates with subscribers soon. Stay tuned.

Shawn