Resilient Financial API Integration with Live AI

Introduction

Financial API integration is the backbone of modern fintech applications, enabling banking systems, payment platforms, and other financial services to talk to each other seamlessly. Financial APIs serve as channels through which different financial platforms communicate and share functionality; they set the rules for data exchange that allow otherwise disparate systems to interoperate.

Setting up a financial API integration typically involves interacting with the provider’s documented endpoints, handling authentication, and managing data exchange. Developers must integrate with RESTful endpoints (URLs) provided by the financial institution or service, sending requests (for example, to retrieve an account balance or initiate a payment) and parsing the responses returned in a standard format like JSON.

However, integrating with financial APIs is not without challenges. From a developer’s perspective, some common pain points include:

• Breaking changes: API providers may introduce updates or new versions that are not backward-compatible. Even a minor change in an endpoint or data schema can break a previously working integration, leading to errors or downtime. In the rapidly evolving open-finance ecosystem, frequent API upgrades mean seamless migrations are more important than ever to avoid disrupting users
• Version management: Because financial APIs evolve over time, developers must constantly track version deprecations and releases. Maintaining compatibility often requires supporting multiple API versions or quickly refactoring code to use the latest endpoints when old ones are retired.
• Redeploys and maintenance overhead: Every API change can necessitate code updates and application redeploys on the client side. This ongoing maintenance cycle consumes development resources, and if updates aren’t rolled out in time, customers can experience outages when an upstream API changes unexpectedly.

Even well-documented services (with comprehensive OpenAPI specs) can introduce subtle changes that require quick turnaround fixes – a new field in a response or a modified parameter might break the client integration if left unaddressed. Without a resilient strategy, these frequent API changes can and do cause integration outages. This reality is the driving motivation behind our approach.

Why does this matter?

One missed spec change can cost millions:

• When Open Banking UK shipped Standard v4.0 with a mandatory FAPI 1.0 security uplift, every TPP had a 90-day clock to add new consent scopes or face access revocation. Open Banking
• The SWIFT ISO 20022 migration ends MT support on 22 November 2025; if banks keep sending legacy message types after that date, payments will be rejected in flight. SwiftSwift
• Mastercard Mastercom v6 will decommission the /imagestatus endpoint this October; dispute-ops dashboards that still poll the old route will simply 410. Mastercard Developers
• Plaid will drop identity fields from Income/Partner-Insights responses by 30 June 2025, breaking KYC pipelines that hard-code the old JSON shape. Plaid

Each of these updates hides in a changelog or YAML diff long before an outage surfaces. Your agent is the first (and cheapest) line of defense… if it can see the change.

In this post, you’ll explore why evolving APIs are one of the most underestimated pain points in modern finance and how evolving agents are the only scalable solution.

Solution Overview: Live AI Architecture for Financial API Integration

This community-built solution solves the evolving API spec problem by building agents that are deeply API-aware, not just at runtime, but continuously. These agents are equipped with a live parsing engine that monitors OpenAPI specifications in real time, detects changes, and automatically updates the API documentation in their knowledge base in real time using Pathway.

This allows agents to dynamically adjust to newly added endpoints, renamed parameters, or modified schemas without requiring manual intervention or changes in their code. The result is a resilient, future-proof assistant that always stays in sync with your financial infrastructure, even as it changes under the hood.

Why This Approach Is Unique for Financial API Integration with Live AI

1. API-Centric Data Retrieval with Scoped Access: Unlike traditional solutions that query entire databases, our approach leverages the platform’s existing API endpoints. By embedding user authentication in the headers of each API call, data retrieval is ensured to be both accurate and limited to the user’s authorized scope. This results in a highly secure and personalized experience for each user.
2. Intelligent Answer Generation Over Basic Navigation: Many solutions, like Rufus (Amazon), act as navigation tools that guide users to relevant pages. While these enhance UX to an extent, they stop short of generating intelligent answers. Our approach goes further by synthesizing intelligently generated answers from the data retrieved via APIs, elevating the user experience significantly.
3. Seamless Integration via OpenAPI Specification: Our solution is designed for effortless integration with existing platforms by adhering to the OpenAPI specification – a widely accepted standard. This minimizes the need for extensive customization, reducing implementation time and effort while maximizing compatibility and adaptability.
4. Efficiency Through DSPy and Smaller Language Models: Traditional approaches often rely on large-scale language models like GPT-x and Gemini to generate structured outputs, which can be resource-intensive and incur higher latency and costs. Our metrics, even with smaller LLMs like Gemma, are satisfactory, making the system cost-efficient.

Stages & Agents in a Live AI Financial API Integration

Step I. Query Handling

• Rephrasing the query for clarity to ensure it is easily understood and aligns with the system’s capabilities.
• Classifying the query to determine if retrieval or additional processing steps are required.
• Decomposing complex queries into simpler sub-queries to facilitate accurate and efficient processing.
• Checking the query for profanity or sensitive data to maintain ethical communication and ensure compliance with privacy and security standards before further processing.
• By utilizing chat history (previous queries and responses), the system generates more context-aware and accurate responses, thus improving the overall user experience and enabling more seamless interactions.

Step II: Endpoint Selection and Retrieval

• Identifying the most relevant finance API endpoints by embedding the query and comparing it to endpoint descriptions using similarity scoring.
• Selecting the top-k endpoints and re-ranking them based on relevance to ensure optimal results.
• Ensuring the input data complies with the required schema, typically derived from the openapi.json file, before making any API calls.
• Sending requests to the identified endpoints and handling the exchange of information as required.

Step III: Response Generation

• Processing the retrieved information through a network of AI agents to generate a suitable response.
• Performing additional actions, such as code generation or computation, if required by the query, handled by specific agents.
• Code Generation Agent: Classifies the query based on whether it requires code generation or simple information retrieval. If code is needed, the agent generates the appropriate code, executes it, and uses the result to generate the response. If no code is needed, the agent proceeds with response generation from the retrieved data.
• Verifying the response through guardrails to ensure it contains no inappropriate or sensitive content before final output.
• Guardrails: To ensure responses are safe and reliable, key guardrails have been incorporated such as filtering irrelevant or sensitive data from API responses and requiring explicit user consent for actions with long-term consequences. These mechanisms uphold privacy, security, and ethical standards while enhancing user trust.
• Presenting the final output, accompanied by follow-up questions for further interaction.
• Follow-up Queries Agent: This agent generates potential follow-up queries based on the original query after a response has been provided. This mechanism anticipates user needs, enhancing the conversational flow and encouraging deeper engagement.

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Metrics on Retrieval

Various models have been tested with different retrieval methods, including hybrid search, BM25, and k-nearest neighbors (KNN), to evaluate their effectiveness across several performance metrics. The evaluation criteria used to assess model performance include the context’s accuracy, precision, recall, and F1 score.

• Accuracy measures the proportion of correct predictions out of all predictions, indicating overall correctness.
• F1 Score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives.

Table 1: Evaluation of different methods for hybrid search in terms of F1 and Accuracy scores

Table 2: Performance metrics for response generation using different methods

Metrics on Latency

Metrics on Token Usage

JSON, YAML, XML, MIN-JSON TOKEN USAGE METRICS

The chart above shows how the same data compares in token count across different formats: min_json, json, xml, and yaml.

From the data, it’s clear that min_json is the most efficient, using the fewest tokens, while xml is the most token-heavy. This is used in the analysis to determine which format works best for sending data to the LLM. The goal is to keep token usage and costs low while still retaining all the important information. The x-axis contains three entries, each corresponding to a different type of data being analyzed.

Code Repository: Complete Setup & Usage Guide

Access the complete source code in this GitHub repository:

Resilient Financial API Integration with Live AIGitHub

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Conclusion: Making Financial API Integrations Resilient with Live AI

In a world where financial APIs constantly evolve, rigid agents inevitably fail. Exchanges tweak order parameters, brokers launch new instruments, and compliance rules shift overnight—changes that can silently break finance AI agents and automations.

With Pathway, you can build agents that automatically detect and respond to these real-time OpenAPI spec changes. There's no need for redeployments or constant fixes, ensuring your finance integrations remain resilient, accurate, and reliable, no matter how frequently the APIs evolve.

If you are interested in diving deeper into the topic, here are some good references to get started with Pathway:

• Pathway Developer Documentation
• Pathway's Ready-to-run App Templates
• End-to-end Real-time RAG app with Pathway
• Discord Community
• Pathway's LLM Tooling
• Power and Deploy RAG Agent Tools with Pathway

Beyond the key resources mentioned above, for more information on the concepts and frameworks that have been used, these are other options to get started:

• ReAct Prompting Technique; Prompting Guide
• DSPy: Framework for Declarative LLM Pipelines
• OpenAPI Learning Portal
• How does Financial API Integration work
• Financial APIs explained

Authors

• Yuvraj Motiramani