The Wave of the AI Revolution Hits the Financial Industry! Will Financial Analysts be "Replaced"? ― Anthropic Ventures into the "Next Battlefield" with Claude

The Wave of the AI Revolution Hits the Financial Industry! Will Financial Analysts be "Replaced"? ― Anthropic Ventures into the "Next Battlefield" with Claude

1. "What AI Takes from and Adds to Finance"

The financial industry is a world where the amount and freshness of information determine success or failure. Earnings reports, timely disclosures, filings with regulatory authorities, analyst reports, macro statistics, news, and market movements. The task of "reading, connecting, hypothesizing, verifying, and translating into an explainable form" is repeated daily.


Here, AI has entered right in the middle. U.S.-based Anthropic has highlighted capabilities with a stronger focus on financial analysis tasks in its latest model, "Claude Opus 4.6," targeting the financial sector. According to reports, Claude is being expanded to read corporate data, timely disclosures, and market information to handle financial analysis tasks. Following the announcement, stock prices of companies related to financial analysis and information services fell, as the market began to preemptively factor in the "threat."


The important point is not "faster calculations" but the move towards "seamlessly handling everything from reading information to output." In the financial field, the bottleneck often lies not in the numbers themselves but in "reading the rationale behind the numbers" and "documenting." If AI can shortcut these processes, the very design of work will change.


2. Why Finance is Prone to Become "Prey" for AI

Finance is well-suited to AI due to several favorable conditions.

  • Heavy reliance on text: Disclosure documents, minutes, contracts, notes, and explanatory materials—natural language is at the core of the work.

  • Semi-structured format: Financial statements, segment information, KPIs, etc., have certain formats.

  • No single "correct answer": Conclusions can change based on assumptions or scenarios, even with the same data. This is why "hypothesis generation" becomes valuable.

  • High verifiability: There is a culture of returning to the original material for verification (at least in theory).


Anthropic highlights improvements in financial benchmarks and the ability to create documents, spreadsheets, and presentation materials. In practical terms, financial work is a continuous cycle of "reading → extracting → organizing → comparing → storytelling." If AI can speed up this chain, it will fundamentally change how analysts spend their time.

3. The True Nature of the "Fear" Indicated by Stock Prices

Reports state that stock prices of companies related to financial analysis fell after the announcement. The market's reaction is more sensitive to the potential erosion of "profit structures" than to the superiority of technology.


Financial analysis tools and data companies have built value through (1) data organization, (2) search and visualization, (3) workflow integration, and (4) expert networks. However, if AI can, with a single natural language instruction from the user, "gather necessary data, extract key points, create comparison tables, and draft conclusions," then "having users operate the screen" itself ceases to be valuable. In extreme terms, what users seek is not tools but a "package of conclusions and rationale."


Of course, data licensing, compliance, audit response, and accountability cannot be easily replaced. But the market was wary that "the areas that can be replaced are broader than expected." That's why stocks move.

4. Reactions on Social Media: Enthusiasm, Cynicism, and the Question of Responsibility

What makes this topic interesting is that reactions on social media are not monolithic. Broadly speaking, at least four different sentiments can be observed.

 


(A) "The end of hellish work on the ground" camp: Expectation
On Hacker News, questions like "Is this just better formatting, or has the analysis itself improved?" are being asked, with discussions aiming to discern the essence of the functionality. On the other hand, many voices appreciate the value of "work that takes hours being reduced to minutes." Those who spend a lot of time on document creation and updates, in particular, have high expectations.


(B) "The jobs for young people will disappear" camp: Employment anxiety
On accounting and finance career boards, the sentiment is more blatant. Predictions like "simple tasks done by juniors will be the first to go" and "a team of 10 will become a team of 6" are repeated. This is more about the fear of "narrowing the entry point of a career" than "jobs disappearing." Analytical roles have an apprenticeship aspect, and the groundwork serves as a learning opportunity. If AI replaces this, the model for nurturing the next generation will be disrupted.


(C) "Who takes responsibility if it makes a mistake?" camp: Governance
On business-oriented platforms like LinkedIn, the strong point is the argument that "as AI shifts from assistant to operator, the locus of responsibility becomes suddenly ambiguous." In a world where misreading contract clauses, misinterpreting disclosures, or misunderstanding assumptions can directly lead to stress test or investment decision errors, to what extent can AI outputs be treated as "deliverables"? Ultimately, if humans review it, it only leads to efficiency, but the moment the review becomes a formality, accidents happen.


(D) "Convenient but also increases danger" camp: Security
Reports mention that Opus 4.6 has become stronger in discovering software vulnerabilities. This is good news from a white hat perspective, but there is also the reality that attackers use AI. Financial institutions are major targets for cyberattacks, and the more "defensive AI" is implemented, the more sophisticated "offensive AI" becomes—a dilemma.


Ultimately, social media discussions converge on how to allocate "convenience," "responsibility," and "employment." Technology increases what can be done, but organizations must decide what should be done.


5. What the Competition with OpenAI Indicates About the Next Phase of "Enterprise AI"

According to reports, OpenAI also announced improvements to its programming model around the same time. The main battlefield for AI has clearly shifted from consumer-facing chat to core enterprise operations.


What is important here is not that AI does something independently, but that it "melts" into existing business tools, data, and authority management. In the financial field, data is scattered, authority is finely divided, and audit logs are required. For AI to be widely used, it needs not only "intelligence" but also "ease of control." This is why companies are focusing not only on model performance but also on enterprise delivery formats and workflow integration.


The conditions for AI to truly win in finance are not simple precision competitions.

  • Reference and verification of original materials

  • Rationale for outputs (which part of which disclosure was used)

  • Audit logs and access control

  • Design for human intervention when errors occur


When these become "the norm," AI will not just be a convenient tool but a premise of operations.

6. Changes in Japanese Financial and Corporate Practices (Looking Ahead)

For Japanese companies and financial institutions, the impact will come in stages.


Stage 1: Automation of document creation progresses
The speed of creating standardized materials like earnings presentation materials, monthly reports, and macroeconomic summaries will increase. Initially treated as "drafts," the more successful experiences accumulate, the greater the risk of human review becoming thinner.


Stage 2: AI replaces the "pre-processing" of research
Summarizing news and disclosures, creating comparison tables, and organizing points will rely on AI. Humans will focus on hypothesis testing and decision-making, but there is also the risk of a decline in "fundamental skills."


Stage 3: The boundary between analysis and judgment wavers
Conclusions presented by AI begin to be treated as "premises" in meetings. In organizations with weak governance, the seeds of accidents increase.

Therefore, the point of AI introduction is not "whether to use it or not." It is "which processes to entrust to AI and where humans should intervene" and "how to design the lines of responsibility."


7. Conclusion: The Essence of Finance Returns to "Handling of Rationale"

The news of Anthropic stepping into finance is not just a model update. It is a signal that AI is moving closer to the core of operations in the "highly accountable domain" of finance. The stock market's reaction can be said to have preemptively factored in its destructive power.


As indicated by reactions on social media, expectations are high. However, the anxiety is also rational. The stronger the conclusions AI produces, the more organizations must explicitly define "verification of rationale" and "responsibility."


Ironically, the more AI changes finance, the more finance will return to its "roots"—rationale and explanation.


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