Finance leaders evaluating AI solutions, quite rightly, ask questions. What can it do? How fast? What’s the ROI?
But there’s a more important question that doesn’t get asked enough: what did this AI learn from?
The answer is vital. It will determine whether an AI system will understand payment behavior, credit patterns or collection dynamics, or whether it will fumble through finance workflows with the generic competence of something trained on Reddit threads and Wikipedia entries.
Domain Performance Depends on Domain Data
Generic large language models learn from the internet – millions of web pages, books and articles that build broad knowledge. That training works well for general tasks, but finance operations do not fall into this category.
Gartner research shows that domain-specific models can achieve faster time to value, improved performance and enhanced security for AI projects by providing a more advanced starting point for industry-specific tasks. When Google trained Med-PaLM 2 on medical data, for example, it achieved 86.5% accuracy on expert-level licensing questions – well beyond what generic models reach.
Finance has the same dynamic. Payment behavior follows patterns absent from public datasets. When buyers stretch payment terms, when disputes spike in certain industries, which collection strategies work for manufacturing versus retail – these patterns live in transaction data, not blog posts.
This difference matters in daily operations. A domain-trained AI recognizing that a customer’s payment delay matches patterns seen across thousands of similar buyers in that industry can guide prioritization decisions differently than one making educated guesses based on general business knowledge.
Training Quality Determines Governance Quality
Finance leaders also face increasing pressure to explain AI decisions. Regulators want to know why credit was extended or denied, auditors need to trace collection decisions back to source logic, and compliance teams require documentation showing bias wasn’t baked into models.
If you don’t know what trained your AI, you can’t answer those questions.
Data provenance – the ability to trace back through training sources – has become central to AI governance. When a model recommends escalating a collection case or adjusting credit terms, governance frameworks need visibility into what taught it to make that recommendation. Was it trained on anonymized, aggregated real-world payment data? Or scraped web content of unknown quality and bias?
The AI governance market is growing from $620 million in 2024 to a projected $7.38 billion by 2030, driven largely by demand for explainability and audit trails. Organizations deploying AI in sensitive financial operations need systems where decisions can be traced back through training data to source transactions.
Privacy-by-design becomes critical here. Training on real financial data requires strict controls: no personally identifiable information, one-way hashing, minimum source requirements to prevent individual identification, and data that never leaves a private infrastructure or trains external models. These are prerequisites for responsible AI in finance.
Some organizations try to solve this by fine-tuning generic models with their own data. That helps, but it’s layering domain knowledge on top of a generic foundation rather than building from domain expertise. The base model still makes assumptions learned from general internet training, which can surface in unexpected ways when handling specialized financial scenarios.
Agentic AI Increases Governance Requirements
Traditional AI tools offered recommendations that humans reviewed before acting. Agentic AI executes autonomously. An IBM and Morning Consult survey found that 99% of enterprise AI developers are now exploring or developing AI agents – systems that can plan, reason and act without waiting for human approval on every step.
That autonomy changes the governance equation. When an AI agent independently decides to contact a customer, escalate a dispute or modify payment terms, the audit trail needs to show what drove that decision. Modern AI governance frameworks emphasize that oversight must document not just what agents did, but why they made specific choices.
In finance, where decisions directly affect cash flow and customer relationships, this explainability requirement becomes non-negotiable. An agent trained on years of real Order-to-Cash execution data (anonymized payment experiences, dispute patterns and collection outcomes) can point to specific learned patterns when explaining its reasoning. An agent working from generic training lacks that specificity.
Real-time data flows compound this. The largest domain-specific financial data lakes now process payment experiences continuously – ours tracks over $7 trillion in B2B transactions across tens of millions of companies globally. This scale enables AI to recognize emerging patterns: payment behavior shifts, dispute surges in specific sectors and seasonal collection timing. Generic models updated periodically can’t match that currency.
What Finance Leaders Should Ask
Evaluating AI for financial operations requires different questions:
- Where did the training data come from? Broad internet scraping or domain-specific financial transaction data? Is there evidence of the scale needed to capture industry patterns?
- Can the vendor trace decisions back to training sources? When the AI recommends an action, can they show what patterns informed it?
- How is privacy handled? What controls prevent individual identification? Who has access to the data or models?
- Does the AI learn from current data or rely on static training? Fast-moving financial environments need regular exposure to fresh patterns.
The answers separate AI built for finance from AI adapted to it. Generic intelligence has its place, but autonomous financial operations require systems trained on the specific patterns, behaviors and outcomes that define how money moves through businesses.
Training data needs to be viewed as much more than technical detail. It’s the foundation that determines whether AI can handle the nuances of financial operations with the depth those operations demand.