Only 15% of Agentic AI Vendors Serving Finance Know Finance

Key Takeaways

• Only 14.8% of agentic AI vendors serving finance have CEOs with meaningful finance experience.
• Most vendors offer general-purpose tooling not built for regulated financial workflows.
• Without domain fluency, AI agents often misalign with underwriting logic, compliance requirements, and institutional processes.
• Multimodal is different: Our leadership, architecture, and governance framework are purpose-built for financial automation.

Why We Conducted This Research

As more financial institutions adopt agentic AI to automate complex workflows, from underwriting and claims adjudication to credit analysis and loan origination, a question has emerged at the executive level: 

Do these AI vendors actually understand finance?

Agent platforms are multiplying, but too often they are built by generalists with little experience in regulated financial environments. In our view, that’s a red flag. In high-stakes workflows, the agents making decisions must align with how your institution actually operates.

To test our hypothesis, we analyzed 27 vendors (including ourselves) building agentic AI for finance. We evaluated their leadership backgrounds to see how many had real financial domain experience. The results confirmed our instincts.

Executive Summary

Key finding:‍

Just 4 out of 27 companies, fewer than 15 percent, have CEOs with meaningful experience in finance.

This matters because when domain fluency is absent at the leadership level, it often shows up in the product: generic tooling, poorly aligned decisioning logic, and workflows that fall short of compliance.

Multimodal is one of the few exceptions. Our founding team has deep operator-side experience across hedge funds, credit bureaus, and financial data science. Our stellar product, AgentFlow, wasn’t just engineered to work — it was built to work in finance.

Key Findings From the Data

1. Only 4 of 27 CEOs had finance experience

These four leaders included former investment bankers, hedge fund founders, and executives from insurers and credit bureaus. They brought a lived understanding of:

• Risk frameworks and underwriting logic
• Regulatory reporting and audit trails
• Institutional workflows and decision flows

2. Most platforms were generic and horizontal

The majority of companies in our analysis promote multi-agent frameworks, orchestration platforms, or workflow automation tools. But only a few reference finance-specific needs:

• No schema awareness of policy exceptions or loan documents
• No mention of regulatory thresholds (Basel, IFRS)
• Limited visibility into stepwise audit and approval logic

3. Domain misalignment leads to downstream friction

Firms that buy from generalist vendors often encounter:

• Agents requiring extensive customization or retraining
• Workflows that can't scale without manual oversight
• Difficulty satisfying compliance reviews and audits

Why This Should Matter to Finance Leaders

Agents Must Reflect the Logic of Finance

AI agents cannot function effectively in finance unless they are embedded with the logic, language, and structure of the domain itself. Unlike simple task automation, financial operations depend on layered decision-making processes, conditional policies, and strict regulatory frameworks.

In this environment, "reasoning" is not enough because agents must reason correctly within the boundaries of complex institutional logic.

For example, underwriting isn’t just about pulling numbers from documents. It involves evaluating risk tiers, referencing historical loss ratios, and applying judgment based on policy rules. A loan approval is more than a formality. It follows precise sequences tied to credit analysis, conditional criteria, and regulatory disclosures.

These layers require agents to understand what’s legally permissible, operationally valid, and audit-ready.

The point is simple: the logic of finance is non-negotiable. Any agent operating in this domain must be grounded in its rules and rhythms, or it risks introducing errors, misclassifications, or compliance violations.

When Vendors Don’t Know the Domain, Tools Don’t Fit

Without domain fluency, vendors deliver agents that:

• Misinterpret key data or document structures
• Require hardcoded exceptions that break under real usage
• Lack clarity and traceability in their outputs

Trust, Auditability, and ROI Start with Domain Alignment

ROI in financial automation depends not only on speed or scale, but also on building systems that reduce risk, increase confidence, and align with the regulatory expectations financial institutions must meet. 

When AI agents are not grounded in the logic of finance, organizations struggle to justify their deployments beyond isolated use cases.

Without domain alignment, firms encounter ballooning oversight costs, delayed workflows that still require manual triage, and agents whose outputs cannot stand up to audit scrutiny. In contrast, agentic systems that are tuned to financial realities deliver measurable ROI by minimizing human bottlenecks, accelerating throughput, and producing decision trails that are inherently auditable.

You can’t evaluate ROI in finance without:

• Reducing manual oversight
• Increasing straight-through processing
• Passing compliance reviews with minimal intervention

How We Conducted the Research

We reviewed 27 vendors that offer agentic or multi-agent AI platforms with financial use cases. We used publicly available information to evaluate their leadership.

Methodology

• Reviewed CEO and co-founder bios on LinkedIn, company websites, and Crunchbase
• Defined “finance background” as prior roles in:
  
  • Banking, insurance, credit, investment management
  • Financial regulators or advisory roles
  • Fintech, credit bureaus, or financial data science

Scope

• 20 vendors categorized as "Platforms"
• 7 categorized as "Point Solutions"
• Only 4 of 27 CEOs had relevant financial experience

Why Multimodal Is Different

Multimodal was built for financial institutions by people who know the domain.

Our CEO’s Background

Ankur Patel, our founder and CEO, is a Princeton alumnus and a three-time startup founder. He comes from the world of institutional finance and has spent the last decade at the forefront of AI innovation. Ankur previously founded Glean AI and R-Squared Macro, led machine learning initiatives at Bridgewater Associates, and headed data science at ThetaRay in New York City.

He brings deep expertise in natural language processing, unsupervised learning, and financial data science. He has seen two successful exits and authored AI books, including two published by O'Reilly Media: Applied Natural Language Processing in the Enterprise and Hands-On Unsupervised Learning Using Python.

His visionary leadership and insider perspective make him uniquely qualified to build agentic automation that actually fits the industry.

AgentFlow: Architected for Financial Workflows

We didn’t just repurpose a generic agent platform for finance. 

We built AgentFlow from the ground up to mirror how financial institutions operate. 

Every component is optimized for the realities of regulated, document-heavy, multi-stakeholder workflows.

AgentFlow does much more than merely automating steps. It respects the roles, rules, thresholds, and approval processes already embedded in your business. Finance teams don’t need to rewire their operations around the tool.

AgentFlow meets them where they are with finance workflows in mind:

• Process: Document-first, stepwise execution
• Search: Context-aware retrieval from internal systems
• Decide: Rule-aware, confidence-scored actions with SME tunability
• Create: Auto-generated summaries, memos, and audit logs

Each function is modular and can be fine-tuned per use case, whether that’s loan origination, claims processing, risk review, or compliance reporting. AgentFlow is designed to be practical, configurable, and trusted across the finance stack.

Security, Governance, and Trust Built In

AgentFlow is designed for the realities of regulated industries. It gives you control, visibility, and assurance over every agent action, while aligning with the expectations of auditors, risk teams, and compliance officers.

Our agents work with what you already have — no rip-and-replace or vendor lock-in.

• Secure deployment: AgentFlow is deployed within your own environment — your VPC or on-prem — ensuring data never leaves your control.
• Complete traceability: Every decision and action taken by an agent is logged in structured JSON, allowing for full visibility into how outcomes were generated.
• Confidence-aware automation: Role-based thresholds ensure that only low-risk tasks are automated end-to-end. Higher-risk actions trigger human-in-the-loop review or SME escalation.
• Governance by design: Teams can configure escalation paths, retraining intervals, and agent behavior policies, all within an auditable governance framework.
• Compliance-ready: Designed to align with standards like SOC 2, PCI DSS, and upcoming AI governance regulations, AgentFlow provides built-in support for audit preparation and risk reviews.
• Access controls: Fine-grained permissions and just-in-time credentialing allow different business roles to inspect, approve, or revise agent workflows without security compromise.

Talk to a Team That Speaks Finance

If you’re evaluating agentic AI vendors, choose one that understands your workflows, your risks, and your regulators.

Want to see the difference domain fluency makes? Book a demo and see how AgentFlow automates your financial workflows without compromising compliance, control, or context.

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