Building agents at FI-s with Ntropy API

The software form factor is moving from tools we use to get things done to software entities that can make their own decisions and use tools autonomously to get things done. Agents, AI coworkers, teammates. We call them various things. They are inevitably joining the workforce at every single company and industry. Financial institutions are no exception. In fact they have been actively adopting and hungry for solutions.

When we started Ntropy to make financial data better with language models, the consumers of this data were other task specific models to identify fraud or improve authorization or underwrite risk, or humans looking at this information via dashbaords and screens and basing decisions on it.

Today, our consumer has changed. Our data is most useful for agents at FI-s , vertical specific, end to end integrated agents solving massive business problems.

I wanted to share what types of agents are consuming our data and how you can use Ntropy API to guarantee reliable, verifyable and consistent agentic output.

Accuracy matters 100x more when your user is an agent. Here is why

Humans can consume limited samples of data. Whether via dashboard or spreadsheets. Hence the qualit of the data matters, however they can operate within uncertainty and a large error bar making ad hoc decisions or creating processes.

Agents, on the other hand, are faster and can consume much more data to drive actions. Humans are used to natural language instructions and specifications that are often vague. Agents need much tighter specifications to follow and align and make decisions given the endless long tail and edge cases of the real world.

In this scenario, accuracy of the data fed into the agents is critical. One or two percentage points can make a big difference up the stream saving failures, mistakes and losses and accelerating adoption.

Bad data can propagate into automated decisions that carry financial, regulatory, and reputational risks.

A compliance monitoring agent missing a suspicious transaction because it was miscategorized; the bank could incur fines or allow illicit activity to go undetected. In fact, regulators have begun penalizing institutions for poor data quality. Citigroup, for example, was ordered to pay $135 million in fines for failing to fix data quality issues in its risk management systems on top of a prior $400 million fine for risk data problems​. This is a stark reminders that “garbage in, garbage out” can have multi-million-dollar consequences in finance.

The cost of mistakes tends to grow exponentially with transaction value. Misclassifying a $5 coffee purchase is inconsequential, but mislabeling a $5 million wire could trigger big errors down the line​

Agents + Ntropy AI: what are our customers building?

Many of our customers have been testing, building and shipping agents into production. Below are some great use cases we are seeing take off within financial instituions where our API and accuracy have been critical.

• Dispute Resolution Agents – When customers dispute transactions, AI agents are starting to help banks investigate and resolve these cases. The process involves checking the transaction details, merchant info, timestamps, etc. If the data is wrong (e.g., mis-identifying the merchant or amount), the agent could refund the wrong customer or deny a valid claim. Accuracy here directly impacts financial liability and customer trust. On the flip side, getting it right pays off: AI dispute automation can cut operational costs by up to 50% while speeding up resolution for customers

• Compliance agents – Banks use AI to monitor transactions for anti-money-laundering (AML), sanctions, and other compliance checks. These systems depend on enriched data to recognize who is getting paid and for what. A small data error could mean missing a red-flag transaction or generating false alarms. The consequences for mistakes are severe: banks face hefty fines and regulatory action if they fail to report issues accurately.

• Customer Support – Many financial institutions now deploy agents to handle customer inquiries. These AI agents often pull information from transaction data to answer questions like “What is this charge on my card?” or to help categorize a spending pattern for advice. If the enrichment is incorrect, the chatbot might give a nonsense answer or mislead the customer. That erodes trust and can even lead to legal risk if customers are misinformed.

• Reconciliation Agents – Accounting reconciliation is the process of matching transactions (e.g., a bank statement entry with an internal ledger entry or an invoice). AI-driven reconciliation agents can automate this painstaking work. However, if transaction descriptors aren’t standardized and accurate, the agent will struggle or make incorrect matches. Even a minor inconsistency (say a vendor name spelled slightly differently in two systems) can cause a cascade of unmatched items. Companies already spend up to 30% of finance teams’ time on manual reconciliations

• Accounts Receivable (AR) and Accounts Payable (AP) Automation – These are the lifeblood of a company’s cash flow: AR agents send invoices and apply incoming payments; AP agents process bills and execute payments. If an AR automation tool has incorrect transaction categorizations, it might fail to match a customer payment to the right invoice, leaving cash unapplied. If an AP agent has bad merchant data, it might pay a fraudulent invoice or duplicate a payment. The ROI for accuracy here is very clear. Organizations that implement high-quality data and automation in AP have reduced their cost per invoice from ~$8.78 down to $1.77 on average​

• Loan origination agents