Intro
The banking industry is under intense pressure to modernize. Rising operational costs, increasingly complex compliance requirements, and high customer expectations have exposed the limitations of traditional systems. To remain competitive, financial institutions are now embracing AI-powered automation to streamline operations, reduce manual effort, and improve data accuracy at scale.
In this case study, we explore how a major European financial institution partnered with Arnia Software to address internal inefficiencies using artificial intelligence, document processing, and workflow automation. Over 80 automation use cases were identified across key areas such as credit verification and compliance validation, with 12 high-impact scenarios in SME lending selected and automated as part of the pilot, currently in the testing phase.
The solution was designed and delivered by Arnia Software, a trusted custom software development partner known for delivering cutting-edge AI and automation solutions across diverse industries. With a proven track record of innovation, Arnia helps companies turn complex challenges into scalable, future-ready technologies. Whether you’re a CIO, operations lead, or innovation strategist, this article offers real-world insight into how AI can power tangible transformation in modern banking.
Table of Contents:
The Hidden Inefficiencies in Modern Banking Workflows
Despite ongoing investments in digital platforms, many banks still rely on manual, paper-heavy internal processes to manage core activities such as credit approval, risk assessment, compliance validation, and account management. These hidden workflows, often invisible to customers, create significant process slowdowns behind the scenes.
In particular, banking workflows that follow the Maker/Checker principle, where two users must approve every document or transaction, introduce:
- Redundant data entry and revalidation
- High dependency on human review
- Increased turnaround time for credit and back-office processes
- Greater risk of errors and compliance gaps
This manual validation model is especially resource-intensive when dealing with SME loan applications, mortgage approvals, or corporate credit line extensions, where each case can include dozens of non-standard documents requiring cross-checking.
For many banks, this operational drag not only limits scalability but also affects the customer experience, as delays in internal processing can slow down approvals and service delivery.
The opportunity? Automate these internal document workflows using AI, document intelligence, and smart orchestration, without compromising accuracy or control.
Project Overview – From Workshop to Intelligent Automation Pilot
In April 2025, a top-tier European financial institution partnered with Arnia Software to address its internal workflow inefficiencies through a custom AI-powered solution. The project began with a collaborative discovery phase, including business and technical workshops to map internal workflows, particularly those following the Maker/Checker model, and identify automation opportunities.
By mid-May, over 80 use cases were identified across key banking areas, including:
- SME and Corporate Lending
- Retail Credit (Personal and Mortgage)
- Large Corporate Finance
- Back-office operations such as account closures, settlements, card compliance requests
Twelve high-impact use cases related to SME credit verification were selected for the initial pilot phase (Release 1). This phase focused specifically on automating the “Checker” role, responsible for validating document packages against internal lending rules.
Pilot implementation was completed by the end of June 2025. The flow entered testing in July, with future expansion areas under evaluation.
Project Team & Delivery Model
The solution was implemented through a cross-functional delivery model. The core team from Arnia included:
- 3 AI/ML Engineers focused on document intelligence and model integration
- 1 Business Solution Architect leading the technical design and system alignment
- 1 Business Analyst defining process flows, compliance logic, and use cases
This agile, cross-functional approach ensured consistent alignment between business goals and technical execution.
How the AI-Powered Workflow Works – Architecture & Technology Stack
To bring this transformation to life, Arnia Software designed a modular, secure, and scalable architecture built specifically for complex banking environments. The solution integrates AI-based document intelligence, language models, and automated orchestration workflows into the bank’s existing IT ecosystem.
Key Components of the Architecture
- User Interaction Layer
A custom Chrome browser extension enables “Makers” to initiate document verification jobs with a single click, without leaving their core platform.
- Event-Driven Workflow
Submitted jobs are published into Apache Kafka, enabling asynchronous processing and high scalability.
- AI & Processing Layer
Azure Functions are triggered by Kafka events to extract job data and initiate document validation.
- Document Analysis Pipeline
Leveraging Azure AI Document Intelligence, the system extracts structured data from unstructured formats (PDFs, Word files), identifying names, IDs, contracts, and financial details.
- Validation via LLMs
A customized GPT-4.1 model validates document content against banking rules, using tailored prompts for accuracy and consistency.
- Results & Feedback Loop
Users are notified upon job completion. If validation fails, errors are flagged with explanations, and a feedback system allows for user input and traceability.
Technologies Used
- Azure OpenAI GPT-4.1 & Document Intelligence
- .NET 8.0 / ASP.NET Core
- Apache Kafka (messaging)
- Azure Functions (serverless compute)
- SQL Server, Docker, Elasticsearch (logging & observability)
- Active Directory + Kerberos (authentication & security)
- On-premises deployment (except AI services)
The system was designed to meet bank-grade compliance standards, with built-in observability, user-level authorization, and seamless integration into the bank’s existing APIs and internal orchestration workflows.
Key Use Cases Automated in Release 1
The pilot phase (Release 1) successfully automated 12 use cases related to document validation in SME credit processing. The focus was on replacing manual “Checker” activities with intelligent automation while preserving regulatory accuracy and control. The system was designed to handle a wide variety of scenarios involving inconsistent document structures, multi-source inputs, and high compliance requirements.
Core Capabilities Delivered
- Automatic document classification
Identification of uploaded document types, such as ID cards, bank statements, contracts, and financial attachments.
- Key data extraction
Extraction of borrower names, company representatives, guarantors, collaterals, and related entities.
- Compliance validation
Automated enforcement of internal banking rules to check document integrity and regulatory alignment.
- Cross-document consistency checks
Verification that information is consistent across multiple submitted documents.
- Data quality control
Detection of missing or mismatched data, scan quality issues, and support for feedback loops when validation fails.
Planned Use Cases in Future Releases
The architecture is built to support broader automation initiatives, including:
- Automation of the “Maker” role for credit initiation
- Expansion into Large Corporate credit lines, Retail lending, and credit renewals
- Back-office process automation: account closures, settlements, and card compliance
- Integration with external systems like contract lifecycle management (CLM)
Real Results from the Pilot – Speed, Scale, and Savings
Even in its initial pilot stage, the AI-powered automation delivered substantial, measurable value across multiple dimensions.
Efficiency Gains:
- Document processing time reduced from 10–15 minutes to ~46 seconds per case
- Initial team of 20 users now able to process 200+ cases daily using automation
Cost Savings:
- AI validation cost per case estimated at just $0.12, making it highly cost-effective for scaling
- Reduced manual workload enables staff to focus on higher-value tasks instead of repetitive document checks
Accuracy & Compliance:
- Error rate dramatically reduced due to LLM-powered validation aligned with banking rules
- Built-in logic ensures consistent enforcement of compliance without introducing human variability
Scalability:
- Architecture supports tripling user load and document volume with no performance drop
- Future expansion planned across Retail, Corporate, and Back Office workflows by year-end
Lessons Learned from the Pilot
1. Start with High-Pain, Low-Risk Use Cases
Focusing on the “Checker” role in SME credit validation offered immediate value without overexposing sensitive decision-making systems. It was the right blend of complexity and controllability.
2. Align AI with Business Rules, Not Just Data
Accuracy was achieved not just through LLM power, but by embedding internal compliance logic into validation prompts and AI workflows.
3. Keep Humans in the Loop
Even with full automation, user feedback loops and manual override options proved essential — both for user trust and compliance traceability.
4. Build for Integration, Not Isolation
The architecture’s success hinged on how well it plugged into existing systems (via APIs and SOATs), instead of forcing new platforms or interfaces.
5. Don’t Underestimate User Enablement
Training business users and technical teams in parallel was critical. The project included documentation, dashboards, and knowledge transfer to ensure long-term autonomy.
Future Roadmap: Scaling Intelligent Automation Across the Enterprise
Following the pilot’s success, the bank plans to:
- Expand automation to include the “Maker” role in credit workflows
- Deploy similar validation flows for Retail lending, Large Corporate credits, and Credit Line renewals
- Extend AI models to support contract lifecycle integration and dynamic rule updates
- Scale the solution to handle 3× the current user base and document volume during peak periods
The architecture is built to support long-term growth, compliance evolution, and hybrid deployment models, all while reducing operational friction.
Final Takeaway
Intelligent automation isn’t just a future goal, it’s a present-day advantage. This case shows how financial institutions can move fast, stay compliant, and scale securely with AI-powered workflows, when paired with the right partner and architecture.
Ready to Automate What Matters Most in Banking?
If your institution is looking to automate complex workflows such as credit processing, compliance checks, or document validation, Arnia Software offers the expertise to turn AI into real, scalable impact.
With experience in over 80 automated banking processes, Arnia delivers secure, tailored solutions built on cutting-edge technologies like Azure AI, OpenAI LLMs, and event-driven architectures.
Explore our Banking Software Solutions to see how we streamline operations across retail, SME, and corporate segments.
Learn more about our work in Digital Banking & Fintech Innovation and how AI is transforming compliance, onboarding, and customer experience.
Let’s talk automation. Whether you’re mapping out your AI strategy or planning a discovery session, our team is here to support your next move. Contact us today to start the conversation.