AI in Finance Is Here, But What About Entity Resolution?
Artificial intelligence has officially moved into the back office — not as a chatbot you ask questions to, but as infrastructure woven into the tools finance professionals use every single day. While AI in dealmaking is becoming more commonplace, the real question for seasoned investors is whether the data beneath those tools is reliable enough to trust.
AI is widely promoted as a transformative force in finance, promising faster deal sourcing, smarter due diligence, and better decision-making. But the real value of any AI system depends on the quality of the data behind it. While tools like ChatGPT, Claude and other LLMs can help build or power AI applications, they do not include proprietary datasets like those developed by platforms dedicated to dealmaking.
When AI workflow tools for investment firms sit on top of fragmented data sources, they generate outputs that sound authoritative but are built on incomplete pictures. Large language models can also hallucinate, presenting plausible answers even when the underlying data is missing or inconsistent across sources. The model doesn’t know what it doesn’t know. It simply produces the most likely response based on what it can see.
Platforms built on unified, normalized data tell a different story. When the entity layer is already resolved, with parent companies, subsidiaries, and legal entities correctly mapped before the analysis begins, a more complete picture starts to emerge. Facts can be verified across sources, inconsistencies become visible, and new questions about related data points naturally arise. That foundation makes every AI output downstream more reliable. In M&A, that’s all the difference.
The bottom line is without clean, comprehensive, and specialized data, even the most advanced AI simply automates existing limitations. This means it produces insights that are only as strong as the information it is trained to analyze. Entity resolution is like the “garbage in, garbage out” safeguard of data.
Is AI Finally Living Up to Its Promise in Financial Services?
Anthropic recently launched a suite of enterprise plug-ins for Claude Cowork, including dedicated agents for financial analysis, investment banking, equity research, private equity, and wealth management, built directly into the tools finance teams already use. Perplexity was even an earlier mover. Its finance product, launched in July 2025, combines real-time search with integrated data from various structured financial data providers and repositories, letting users pull sourced answers to financial queries without toggling between platforms.
Both companies built their products for workflows, not conversations. That’s the meaningful shift. Connectors to FactSet, LSEG, DocuSign, and S&P Global mean that AI can now pull from the data sources financial teams already rely on, rather than working around them. PwC’s announced collaboration with Anthropic to deploy these tools across regulated industries signals that large institutions are taking the infrastructure question seriously, too.
The market numbers confirm the direction. According to Deloitte’s 2025 GenAI in M&A Study, 86% of corporate and private equity firms have already integrated generative AI into their M&A workflows. And 83% of those firms have committed over $1 million to the effort specifically for their deal teams.
What Are the Real Data Challenges Lurking Beneath the AI Layer?
AI doesn’t fix your data. It uses your data. And in most financial institutions, that data is a mess. Finance teams routinely work across a tangle of internal CRMs, third-party databases, data vendors, and legacy systems that were never designed to talk to each other. This results in:
- A single company appearing under multiple name variations across internal systems, each attributed differently
- Broken corporate hierarchies where subsidiaries aren’t correctly linked to parent entities
- Duplicate records that inflate deal pipelines and distort competitive analysis
- Conflicting financial figures across platforms, making reconciliation a manual, time-consuming task
- Outdated or missing ownership data that undermines diligence from the start
- Data that is incorrect and should not be used. Even data providers make mistakes.
The same Deloitte study found that 65% of AI adopters in M&A cite data quality and availability as a leading barrier to broader deployment. Layering an LLM on top of these problems doesn’t resolve them, but amplifies them.
Why Does Entity Resolution for Finance Change Everything?
Entity resolution is the process of confirming that every record pointing to the same real-world company is correctly identified, linked, and attributed. It may sound technical, but the consequences of getting it wrong is significant. In practice, resolving companies is only one part of the picture. Investment teams also need to reconcile related entities such as transactions, investors, executives, and other relationships that shape how deals actually unfold.
When a firm runs portfolio exposure analysis, the model needs to know that “Acme Holdings LLC”, “Acme Holdings”, and “Acme Holdings Inc.” are the same entity, not three separate companies. The same principle applies to the people behind those companies and the transactions connecting them. If investors, executives, or past deals are recorded differently across systems, the broader picture of relationships and activity becomes distorted.
In financial services, entity resolution underpins both regulatory compliance and risk management by allowing institutions to consolidate everything tied to a specific entity across fragmented systems. Without that foundation, competitive analysis gets skewed, exposure maps become unreliable, and predictive models begin flagging the wrong signals.
In M&A and private capital, where the thesis of a deal can hinge on a target’s subsidiaries, ownership structure, investor history, or executive network, unresolved data is not a background problem but a deal risk. AI data infrastructure for dealmaking is only as strong as the underlying entity layer holding it together. Financial data reconciliation powered by AI delivers real value only when companies, people, and transactions are already correctly resolved.
How Does Cyndx Solve the Entity Resolution Problem for Dealmakers?
Cyndx was built for investment workflows by a team with decades of experience in the banking sector, which means AI infrastructure for dealmaking was prioritized from the start. Unlike your average chatbot, Cyndx brings together a proprietary database of over 32 million private and public companies with a full suite of purpose-built tools, all sharing the same underlying normalized data.
- Finder uses AI and natural language processing to map niche markets and surface acquisition targets. Dynamic company categorizations are updated daily, reflecting how companies describe themselves right now.
- Acquirer identifies companies predicted to become acquisition targets, letting deal teams get ahead of opportunities before competitors are looking.
- Raiser analyzes billions of data points to match companies with the right financial and strategic investors based on sector, deal size, and funding stage, and includes direct contact information for over 80 million contacts.
- Scholar is a generative AI research tool that produces 50+ page deep-dive reports in minutes. It uses Cyndx’s proprietary data, uploaded documents, and external sources.
- Valer is an AI-powered business valuation tool built by investment bankers. It produces professional-grade reports with public comparables, precedent transactions, adjustable WACC, and more.
The critical distinction isn’t any single tool, but all of them running on the same data. When your deal sourcing platform, valuation software, investor identification tool, and research engine share the same entity layer, insights flow between them instead of contradicting each other.
That eliminates the biggest time sink in traditional M&A: reconciling conflicting information from disconnected systems.
In an industry where everyone has access to the same models, having clean data and a purpose-built platform sets you apart. Contact us to know more.