Automating Banking Services Flows

Automating Banking Services Flows with LLM-Driven Document Understanding

Client

A top-tier bank that is committed to operational excellence through intelligent automation and AI adoption.

Context

Banking services flows within a bank activitiy are daily and numerous, and the entire process of handling banking requests is a labor-intensive and prone to errors process within the bank. It involved reading multi-page documents, validating debtor data, and executing account actions manually. As volumes increased, the process becomes a bottleneck for compliance and customer response times. The challenge for the R&D AI department was to reimagine part of the banking services flows using large language models (LLMs), robotic process automation (RPA), and secure backend integrations. The goal was to fully automate document understanding and decision support for banking services requests, while maintaining human oversight for quality assurance. A team of our experienced developers were part of this core development department.

Objectives

• Reduce manual handling of banking services documents
• Enable AI-assisted document understanding
• Integrate decision logic with banking systems for real-time validation
• Preserve compliance, traceability, and human-in-the-loop validation

The Solution: The Banking Services Process Automation Tool

1. Document Understanding with LLMs

Documents, often exceeding 100 pages, were indexed and processed using retrieval-augmented generation (RAG) models—ensuring only relevant excerpts were passed to the LLM for interpretation. The heart of the solution is Vertex AI, Google Cloud’s large language model platform. For each scenario, tailored prompts are crafted to enable the model to extract specific information from documents or make decisions across multiple documents, based on the rules and context defined in the prompt.

2. Automated Processing via RPA & Prompt APIs

Custom- designed robots collect daily banking services emails, extract attachments, and call the LLM via a custom Python web server that hosts:

• Prompt management APIs
• Input/output configurations
• Token usage tracking
• Document-type-specific routing logic

The output is structured as a validated JSON, which is then passed to banking systems to check customer status, account balances, and apply actions on accounts if this is the situation.

3. Test-Driven & Prompt-Driven Development

Each prompt is tested against expected outputs using Prompt-Driven Development (PDD) and Test-Driven Development (TDD) workflows. A confidence score module evaluates each LLM response. If the score is too low or semantic similarity fails against the expected output, the request is flagged for human review.

4. Human Oversight for Critical Decisions

All final decisions are still reviewed by a human operator, especially for:

• Exceptional cases not covered by prompt logic
• Low-confidence outputs
• Ambiguities in texts

This hybrid approach enables trust in automation while preserving responsibility on the outcome.

Results

Following the implementation of the Banking Services Automation Flow, the processing time per document dropped. The automation pipeline now handles over 100 pages documentations multiple times per day. This shift not only accelerated turnaround times but also significantly reduced operational strain and improved banking services compliance across the board.

The Banking Services Automation Flow now processes thousands of requests per month, freeing up operations teams, improving SLA adherence, and significantly reducing the risk of human error.

Key Technologies

• Google Cloud Vertex AI– LLM-powered document parsing
• RPA – Document retrieval and system integration
• Python Web Server – Prompt orchestration and API calls
• LLM Confidence Evaluation – Semantic similarity, confidence scoring
• Storage & Indexing – For Banking Services document history & audits

Challenges & Lessons Learned

During the development and testing, several key challenges emerged. One of the most persistent issues was LLM hallucination, which was mitigated by implementing request quotas and cross-validating responses using semantic similarity checks. As prompt complexity increased, we modularized them into smaller, more manageable components to avoid token overflow and streamline debugging.

To support broader adoption, we created test flows accessible to non-technical users through dedicated internal interfaces. Each user can create a set of test cases to test out the viability of their custom-generated solution.

Additionally, the scaling limitations imposed by Google’s LLM quota pushed us to adjust our architecture and secure reserved resources to maintain system reliability under load.

Conclusion

The Banking Services Flow Automation project exemplifies how LLM-based document understanding, combined with orchestrated backend automation, can reshape a core banking process. This is not just about AI—it’s about augmenting operations, accelerating compliance, and empowering humans to focus on exception handling, not repetition.

Industry Overview

Banks and financial institutions are increasingly turning to AI-driven solutions to manage the growing volume and complexity of document-centric banking services workflows. Processes such as document handling, KYC, credit scoring, and regulatory compliance often rely on unstructured document and texts and other documents that are traditionally processed manually by operations teams. This has led to the rise of Document Understanding (DU) platforms and other automation tools powered by machine learning and large language models (LLMs).

Several enterprise players have entered this space. Google Cloud’s Document AI, AWS Textract, and Microsoft Azure Form Recognizer offer robust OCR and DU capabilities, allowing organizations to extract structured data from scanned Banking Servicesdocuments. These tools are often integrated with RPA platforms like UiPath or Blue Prism, enabling straight-through processing of routine Banking Servicesand financial tasks. Additionally, RegTech provides domain-specific compliance automation tools with integrated NLP for regulatory document parsing.

Recently, LLMs like OpenAI’s GPT-4, Anthropic’s Claude, and Google Cloud’s Gemini have introduced new opportunities in semantic understanding, prompt-driven automation, and multi-step reasoning, which go far beyond static rule-based extraction. These models enable banks to move from simple document scanning to full contextual interpretation of banking content—such as understanding document logic, validating identities against internal records, or even generating appropriate actions based on obligations described in requests.

Despite these advancements, challenges remain. Hallucination, token limitations, and data privacy concerns require thoughtful design, modular prompt engineering, and the inclusion of human-in-the-loop review processes. However, as regulatory pressure mounts and operational efficiency becomes a competitive differentiator, more banks are investing in hybrid AI solutions that combine traditional automation (RPA) with advanced LLM-based understanding to improve accuracy, reduce costs, and accelerate response times in banking services workflows like documentation processing.

Terms of Understanding

LLM (Large Language Model)

A type of advanced AI model trained on massive amounts of text data to understand and generate human language. LLMs (like GPT, Claude, or Gemini) are capable of answering questions, summarizing documents, and generating text based on prompts.

RAG (Retrieval-Augmented Generation)

A technique that combines an LLM with a knowledge retrieval system. When asked a question, the model pulls relevant information from external sources (e.g., databases, document repositories) and uses it to generate accurate, grounded responses.

DU (Document Understanding)

The process of converting unstructured documents (like PDFs, court orders, or scanned forms) into structured data using AI. This involves extracting key entities, understanding document context, and enabling automated workflows.

OCR (Optical Character Recognition)

A technology that converts images or scanned documents into machine-readable text. It’s often used as a first step in document automation, enabling AI systems to “read” physical or digital documents.

AI Robots (RPA + AI)

Software bots that perform repetitive tasks by mimicking human actions, enhanced with AI for decision-making. When integrated with LLMs or DU tools, these bots can handle more complex tasks like interpreting banking documents or validating customer data.