Public vs Private LLMs in Treasury

Why This Matters Right Now?

The AI explosion has created two real options for everyone: public LLMs accessed through APIs (like ChatGPT, Gemini, etc) and private LLMs running in private infrastructure (personal PC, company infrastructure, etc).

Private LLM doesn’t mean building a massive AI model from scratch with a team of PhD data scientists. Most of enterprise implementations mean taking an existing open-source model (like Ollama, Mistral etc), customizing it with your specific data, and adding own security controls. Think of it like customizing your ERP system rather than building Oracle from scratch.

Public LLM (API/SaaS Model): You access a commercial AI service (ChatGPT, Claude, etc.) through the internet. Your prompts and data travel to the vendor’s servers, get processed in their cloud infrastructure, and results come back to you. Even if some vendors claim they offer “low retention” policies where they don’t store your conversations, your data still temporarily passes through their systems.

Private LLM: An AI model that runs entirely within your company’s controlled environment, either on your own servers (on-premises) or in your dedicated cloud space (VPC). You control every aspect: the data pipeline, security measures, model updates, and infrastructure monitoring.

What “Training” Actually Means in Enterprise: In treasury contexts, “training an LLM” typically means fine-tuning an existing model with your specific documents, procedures, and formats, plus building a Retrieval-Augmented Generation (RAG) system for your knowledge base. You’re not creating a new AI brain, you’re teaching an existing one your company’s treasury language and processes.

The Decision Matrix: When Each Approach Makes Sense

Public LLM is Your Best Bet When:

• Time-to-value is critical and you lack MLOps expertise: You need results in weeks, not months, and don’t have a dedicated technology team. Perfect for professionals who want to test AI capabilities without massive infrastructure investments.
• Compliance requirements are satisfied by vendor policies: The provider offers EU data residency( for example), zero data retention policies, and compliance certifications that meet your regulatory requirements.
• Variable, unpredictable usage patterns: Your AI needs fluctuate dramatically – intensive during month-end close, minimal during steady-state operations. Pay-per-token pricing makes more economic sense than maintaining dedicated infrastructure that sits idle.
• Access to cutting-edge model capabilities: You need the most sophisticated reasoning abilities for complex financial analysis, market research, or regulatory interpretation. Public models typically incorporate the latest AI advances faster than private deployments.
• Real-world treasury example: Using public APIs to analyze market commentary, research new banking regulations, draft initial policy frameworks, or perform general financial calculations where you’re not sharing confidential internal data.

Private LLM Investment Makes Sense When:

• Highly sensitive data that cannot leave your environment: You’re processing confidential cash positions, M&A transaction details, proprietary trading strategies, or customer-specific financial arrangements. Based on treasury security best practices, this data should not traverse external networks.
• Strict latency requirements for real-time operations: You need sub-200ms response times for foreign exchange trading support, real-time cash position optimization, or automated payment processing decisions.
• High-volume, predictable usage patterns: You’re consistently processing 50-100+ million tokens monthly. I would speculate that, at this scale, the total cost of ownership typically favors private infrastructure, though exact break-even points vary significantly.
• Specialized domain knowledge requiring frequent updates: Your treasury processes are highly specialized, you need to incorporate proprietary models, or you must update the AI’s knowledge base frequently with internal procedures, counterparty information, or market data.
• Regulatory air-gap requirements: Certain financial institutions or government treasury operations require complete network isolation for specific functions, making external API calls impossible.
• Granular auditability needs: You need detailed tracking of every prompt, context retrieval, model version, and output for compliance or forensic purposes that external providers cannot satisfy.

The Pragmatic Decision Framework

If you don’t have dedicated MLOps and SecOps teams, then start with public APIs. The operational complexity of private LLMs is significant and often underestimated. If you have sensitive data + budget for operations + strict regulatory requirements , then private deployment makes sense. But ensure you have the technical capabilities to operate it properly.

Companies could sometimes benefit from adopting a hybrid approach: public LLMs for general reasoning and research, private LLMs for processes involving confidential data.

Total Cost of Ownership

Public LLM Economics

• Direct costs: (input tokens + output tokens) × price per token. Current rates range from $0.50 to $30 per million tokens depending on model sophistication and provider.
• Hidden costs: Data preparation time, API integration development, potential vendor lock-in, and premium pricing for enterprise features.
• Scaling characteristics: Perfectly elastic – costs scale linearly with usage, but you have no control over pricing changes.

Private LLM Economics

• Infrastructure costs: GPU/CPU servers, storage systems, networking equipment, power consumption, and facility costs.
• Operational costs: MLOps engineers, security specialists, system administrators, model updating procedures, and backup/disaster recovery systems.
• Hidden costs: Model version management, security patches, compliance auditing, monitoring tools, and the opportunity cost of internal teams managing AI infrastructure instead of treasury operations.
• Break-even analysis: (Speculation) Some say crossover typically occurs around 50-100 million tokens monthly, but varies dramatically based on your internal IT costs and security requirements.

Performance and Risk Considerations

• Latency patterns: Public APIs can experience traffic-based delays and regional variations. Private deployments give you control but require proper capacity planning and autoscaling.
• Scalability approaches: Public services offer instant elasticity. Private deployments require forecasting and resource planning, though you can implement autoscaling and model sharding for large workloads.
• Vendor risk: Public APIs create dependency on external roadmaps and pricing decisions. Private deployments create dependency on your internal team’s capabilities and model maintenance.
• Model quality: We could consider (or speculate) top-tier public models currently maintain an edge in general reasoning capabilities, while private deployments excel in specialized domain knowledge and confidentiality.

Security, GDPR, and Governance: The European Perspective

PII and Data Loss Prevention

• Automated detection and anonymization: Implement systems that identify and mask sensitive data before any AI interaction. This includes account numbers, counterparty names, transaction amounts, and customer information.
• Policy enforcement: clear data retention policies, access controls, and approval workflows for AI usage. Never embed API keys or credentials directly in prompts.
• Content filtering: safeguards against prompt injection attacks where malicious inputs attempt to extract confidential information from your AI systems.

GDPR and Cross-Border Data Considerations

• Data residency requirements: Clarify exactly where your data will be processed, stored, and backed up. This includes primary servers, disaster recovery sites, and any temporary processing locations.
• Legal frameworks: Ensure proper Data Processing Agreements (DPAs), Standard Contractual Clauses (SCCs), and Schrems II compliance for any cross-border data transfers.
• Right to explanation: Implement audit trails that document the decision-making process, data sources, and model versions used for any AI-generated output that affects business decisions.

Audit and Compliance Architecture

• Structured logging: Maintain detailed records showing user identity, timestamp, input data, retrieved documents, model version, and generated outputs in a standardized format.
• Version control: Track all changes to models, prompts, data sources, and configuration settings with proper approval workflows and rollback capabilities.
• Red-teaming programs: Regularly test your AI systems for bias, hallucinations, security vulnerabilities, and policy violations using adversarial scenarios specific to treasury operations.

Systematic Evaluation: How to Compare Solutions Objectively

Custom Benchmarks for Treasury Use Cases

• Don’t rely on generic AI benchmarks. Create evaluation sets specific to your treasury operations.
• Document extraction accuracy: Test Q&A capabilities on your internal documentation with exact match and F1 scoring on critical fields.
• Structured data processing: Evaluate extraction of specific elements from SWIFT messages, bank statements, or internal reports with field-level accuracy measurements.
• Workflow assistance effectiveness: Measure end-to-end task completion rates, time savings, and error reduction in real treasury processes.
• Hallucination detection: Use adversarial test sets to measure how often the AI generates plausible but incorrect information, particularly critical for financial data.
• Performance consistency: Track latency percentiles, cost per interaction, and stability across different model versions and data volumes.

Continuous Evaluation Framework

• Baseline establishment: Document current performance before AI implementation to measure actual improvement.
• Automated testing: Re-run evaluation suites after every model update, data refresh, or system change to catch performance degradation early.
• Human validation: Implement sampling-based human review of AI outputs with clear accuracy criteria and feedback loops.

Practical Example – SWIFT MT103 Payment Message Extraction

Let’s say you want your private AI to extract payment details from SWIFT messages. Here’s how you’d measure accuracy:

Input Document (SWIFT MT103):

:20:REFERENCE123456
:23B:CRED
:32A:250920EUR50000,00
:50K:/RO49AAAA1B2C3D4E5F6G7H8I9J0K
COMPANY ABC SRL
BUCHAREST, ROMANIA
:59:/DE89370400440532013000
SUPPLIER GMBH
MUNICH, GERMANY
:70:INVOICE INV-2025-001
PAYMENT FOR SERVICES

What you want the AI to extract:

{
  "reference": "REFERENCE123456",
  "amount": "50000.00",
  "currency": "EUR",
  "value_date": "2025-09-20",
  "sender_iban": "RO49AAAA1B2C3D4E5F6G7H8I9J0K",
  "receiver_iban": "DE89370400440532013000",
  "purpose": "INVOICE INV-2024-001 PAYMENT FOR SERVICES"
}

Testing Different AI Responses:

AI Response A (Perfect):

{
  "reference": "REFERENCE123456",
  "amount": "50000.00", 
  "currency": "EUR",
  "value_date": "2025-09-20",
  "sender_iban": "RO49AAAA1B2C3D4E5F6G7H8I9J0K",
  "receiver_iban": "DE89370400440532013000",
  "purpose": "INVOICE INV-2024-001 PAYMENT FOR SERVICES"
}

Exact Match Score: 6/6 = 100% (all fields perfectly correct)

AI Response B (Some errors):

{
  "reference": "REFERENCE123456",
  "amount": "50,000.00",  ← Wrong format (comma instead of period)
  "currency": "EUR",
  "value_date": "2025-09-20",
  "sender_iban": "RO49AAAA1B2C3D4E5F6G7H8I9J0K",
  "receiver_iban": "DE89370400440532013000",
  "purpose": "INVOICE INV-2024-001"  ← Incomplete (missing "PAYMENT FOR SERVICES")
}

Exact Match Score: 4/6 = 67% (only 4 fields exactly correct)

AI Response C (Partial extraction):

{
  "reference": "REFERENCE123456",
  "amount": "50000.00",
  "currency": "EUR",
  "sender_iban": "RO49AAAA1B2C3D4E5F6G7H8I9J0K"
  // Missing: value_date, receiver_iban, purpose
}

F1 Scoring Example:

For Response C, let’s calculate F1 score:

• Precision: Of the 4 fields the AI provided, 4 were correct = 4/4 = 100%
• Recall: Of the 6 total required fields, AI found 4 = 4/6 = 67%
• F1 Score: we can create a formula like 2 × (Precision × Recall) / (Precision + Recall) = 2 × (1.0 × 0.67) / (1.0 + 0.67) = 80%

Why This Matters for Treasury:

Exact Match is critical for fields like:

• IBANs (one wrong character = payment failure)
• Amounts (obviously critical for financial accuracy)
• Reference numbers (needed for reconciliation)

F1 Score helps you understand if the AI is:

• Missing information (low recall)
• Making up information (low precision)
• Balanced in both (F1 gives you the overall picture)

How to Actually “Train” a Private Treasury LLM: The Realistic Blueprint

This is the practical approach to fine-tuning plus knowledge base integration, not building from scratch.

Step 0: Define Specific Objectives and Success Criteria

Choose 1-3 focused use cases rather than trying to solve everything at once:

• “Extract payment details from SWIFT MT103 messages with 95%+ field accuracy”
• “Answer policy questions with proper citations from internal procedures”
• “Analyze cash flow forecast variances and provide structured explanations”

Write measurable success criteria: Avoid vague goals like “improve efficiency.” Instead: “>95% exact match on critical fields,” “<2% hallucination rate on verified outputs,” “60% reduction in manual lookup time.”

Step 1: Select Your Foundation Model

Choose based on your constraints: 7-8 billion parameter models for efficiency and lower infrastructure costs, 12-14 billion parameters for better reasoning capabilities, 32+ billion parameters if you have substantial computing resources. Parameter Count Explanation:

• 7-8 billion parameters:

• • Model size: ~4-8 GB RAM
  • Good for: Basic document extraction, simple Q&A, policy lookups
  • Infrastructure: Single GPU or even CPU-only deployment
  • Think: Smart assistant that handles routine treasury tasks accurately
• 12-14 billion parameters:

• • Model size: ~8-16 GB RAM
  • Good for: Complex reasoning, multi-step analysis, nuanced financial interpretation
  • Infrastructure: Mid-range GPU required
  • Think: Experienced analyst that can handle complex treasury scenarios
• 32+ billion parameters:

• • Model size: 32+ GB RAM
  • Good for: Advanced reasoning, complex multi-document analysis, sophisticated financial modeling
  • Infrastructure: High-end GPU cluster required
  • Think: Senior treasury expert with deep analytical capabilities

More parameters = smarter responses but exponentially higher infrastructure costs. Most treasury use cases work well with 7-14B parameter models.

Verify commercial licensing: Ensure the model allows commercial use and understand any restrictions or attribution requirements.

Context length considerations: Select models that support longer contexts (32k+ tokens) if you need to process lengthy treasury documents or multiple data sources simultaneously. Context Length Explanation:

• What are “tokens”?

• • Roughly 1 token = 0.75 words in English
  • 32k tokens ≈ 24,000 words ≈ 50-80 pages of text
• Why context length matters for treasury:
  • Short context (4k-8k tokens):

• • • Good for: Single document analysis, simple Q&A
    • Limitation: Can only “see” 3,000-6,000 words at once
    • Example: Analyzing one SWIFT message or short policy section
  • Medium context (16k tokens):

• • • Good for: Multi-document comparison, longer procedures
    • Can process: ~12,000 words simultaneously
    • Example: Comparing multiple bank statements or policy documents
  • Long context (32k+ tokens):

• • • Good for: Complex analysis across multiple large documents
    • Can process: 24,000+ words simultaneously
    • Example: Analyzing entire treasury manual + current regulations + historical precedents in one query

If you want the AI to answer “How does our new cash management policy compare to last year’s procedures while considering current regulatory requirements?” – you need long context to feed it all three document sets at once. Longer context = higher processing costs and slower response times.

Step 2: Data Collection and Preparation (The Make-or-Break Phase)

Gather treasury-specific docs: Internal policies, procedure manuals, SWIFT message examples, account mapping rules, regulatory guidelines, historical analysis reports, and counterparty documentation.

Critical security step: Remove all real confidential data if you use any API based model. Replace actual account numbers with realistic placeholders, anonymize counterparty names, mask sensitive amounts, and ensure no production data leaks into training sets. If model is 100% local, then fine.

Format for instruction-following: Convert your knowledge into structured instruction-response pairs using JSONL format:

{"instruction": "Extract payment fields from this SWIFT MT103 message in JSON format", 
 "input": "<SWIFT message with anonymized data>", 
 "output": "{\"PaymentAmount\": \"50000.00\", \"Currency\": \"EUR\", \"ReceiverBank\": \"DEUTDEFF\", \"Reference\": \"TXN123456\"}"}

{"instruction": "Answer this policy question and cite the relevant section", 
 "input": "POLICY: Article 3.2: Payments >50k EUR require dual approval...\nQUESTION: When is dual approval required?", 
 "output": "Dual approval is required for payments exceeding 50,000 EUR. (Source: Article 3.2)"}

Data quality standards: Maintain consistent formatting across all examples. Create separate datasets for training, development testing, and final evaluation using truly unseen data.

Labeling guidelines: Establish clear standards for correct responses, include negative examples (what NOT to do), and document edge cases and exceptions.

Step 3: Efficient Fine-Tuning with LoRA/QLoRA

Why efficient fine-tuning: LoRA (Low-Rank Adaptation) teaches the model your specific vocabulary and formats without expensive full retraining. It’s like teaching a multilingual person your company’s internal dialect rather than teaching them an entirely new language.

Mixed training approach: Combine your treasury-specific data with general business examples to prevent “catastrophic forgetting” – where the model loses its general capabilities while learning your specific tasks.

Hyperparameter optimization: Start with conservative learning rates and gradually adjust based on development set performance. Monitor for overfitting on your specific examples.

Regularization techniques: Use techniques that prevent the model from memorizing your exact training examples while still learning the underlying patterns.

Step 4: Retrieval-Augmented Generation (RAG) Implementation

Why RAG is essential in enterprise: RAG allows your AI to access and cite current information from your document repositories without requiring constant model retraining. When treasury procedures change, you update the knowledge base, not the entire model.

Document processing pipeline:

• Ingestion: Automated processing of PDFs, Word documents, and structured data files
• Chunking: Break documents into semantically meaningful sections with appropriate overlap
• Embedding: Convert text chunks into numerical representations for similarity search
• Metadata tracking: Maintain document source, version, creation date, and access controls

Retrieval optimization: Implement hybrid search combining semantic similarity with traditional keyword matching for robust document finding.

Citation and verification: Ensure every AI response includes specific document references, enabling users to verify information and providing audit trails.

Step 5: Safety and Alignment Implementation

Constitutional rules for treasury: Define specific behavioral guidelines like “Never generate fictional transaction references,” “Always require source documentation for policy statements,” and “Flag uncertainty when confidence is low.”

Content filtering: Implement input and output filters to prevent inappropriate content, protect against prompt injection attacks, and maintain professional standards.

Prompt engineering: Develop system-level instructions that set appropriate tone, enforce citation requirements, and handle edge cases gracefully.

Uncertainty handling: Train the model to explicitly state when it lacks sufficient information rather than generating plausible but potentially incorrect responses.

Step 6: Deployment and Operational Infrastructure

Serving optimization: Use specialized software that makes your AI run faster and cheaper on the same hardware. Think of it like using a high-performance engine in your car : same destination, less fuel, faster arrival.

Scalability architecture: Set up your system to automatically handle busy periods (like month-end close) by adding more computing power when needed, then scaling back down during quiet periods. Like having temporary staff during peak seasons.

Monitoring and observability: Track how your AI is performing in real-time: how fast it responds, how much it costs per query, how often it makes mistakes, and whether users are satisfied. Set up alerts so you know immediately if something goes wrong.

Canary deployments: When you update your AI model, test it with just 5-10% of users first. If everything works well, gradually roll it out to everyone. If problems arise, automatically switch back to the previous version. Like testing a new treasury procedure with one team before company-wide implementation.

Step 7: Continuous Governance and Improvement

Model registry: Maintain detailed records of all model versions, training data, hyperparameters, and performance metrics with proper change management.

Data lineage tracking: Document the source, licensing, and transformation of all training data for compliance and auditing purposes.

Periodic retraining schedule: Plan regular model updates when new procedures are implemented, regulatory changes occur, or performance metrics indicate drift.

Incident response procedures: Establish clear protocols for handling incorrect outputs, security issues, or compliance violations with post-incident reviews and system improvements.

Real Treasury Implementation Case Study

Challenge: Automated Bank Statement Reconciliation

Business problem: Let’s imagine treasury analysts spend 15-20 hours weekly manually reconciling bank statement exceptions and researching discrepancies, with frequent delays in month-end close processes.

Technical solution: Private LLM (14 billion parameters) deployed in local environment with specialized fine-tuning for financial document processing and RAG integration with internal procedures, account mapping rules, and historical reconciliation patterns.

Implementation approach:

• Data preparation: 10,000+ anonymized bank statements with manually verified reconciliation results
• Fine-tuning: LoRA adaptation for structured data extraction and exception categorization
• RAG integration: Knowledge base containing accounting policies, historical exception patterns, and counterparty information
• Safety measures: Output validation rules, confidence scoring, and human review triggers for high-impact discrepancies

Measured results:

• Field extraction accuracy: at least 90% exact match on critical fields (account numbers, amounts, dates)
• Processing time reduction: at least 60% decrease in average resolution time for standard exceptions
• Quality improvement: less than 1.5% hallucination rate on verified outputs
• User adoption: minimum 90% of treasury analysts using the system daily within 3 months

Public vs Private LLM Decision Matrix

In the end, it’s a matter of choice which model is best fit, based on multiple aspects.

Choose Public LLM if:

• [ ] You have sensitive data concerns that can be addressed through vendor policies and data sanitization
• [ ] You need rapid implementation without significant infrastructure investment
• [ ] Your AI usage is experimental, variable, or seasonal
• [ ] You want access to the most advanced AI capabilities for general analysis
• [ ] You lack dedicated MLOps and security operations teams
• [ ] Your compliance requirements can be met through vendor certifications and agreements

Choose Private LLM if:

• [ ] You handle highly confidential treasury data that cannot leave your environment
• [ ] You have strict latency requirements for real-time applications
• [ ] Your usage volume is high and predictable (50M+ tokens monthly)
• [ ] You have technical teams capable of managing AI infrastructure
• [ ] You need granular audit trails and complete control over data processing
• [ ] Regulatory requirements mandate air-gap or on-premises deployment

Private LLM Readiness Checklist

Business requirements:

• [ ] Specific use cases defined with measurable success criteria
• [ ] Budget approved for infrastructure, tooling, and personnel
• [ ] Executive sponsorship and change management plan
• [ ] Compliance and legal requirements clearly documented

Technical capabilities:

• [ ] MLOps team available or contracted
• [ ] Security operations expertise for AI systems
• [ ] Infrastructure capacity planning completed
• [ ] Data governance and lineage tracking systems

Data preparation:

• [ ] Training corpus identified and collected
• [ ] PII scrubbing and anonymization procedures implemented
• [ ] Data quality standards and validation processes established
• [ ] Legal clearance for all training data sources

Operational readiness:

• [ ] Monitoring and alerting systems designed
• [ ] Incident response procedures documented
• [ ] Model versioning and rollback capabilities
• [ ] User training and adoption plan

Tackling the Difficult Questions Your Executives Will Ask

“What happens when our AI usage doubles in 6 months?”

• Public LLM response: Costs scale linearly with usage, providing perfect elasticity but potentially creating budget surprises. Implement usage monitoring and automatic spending alerts.
• Private LLM response: You need capacity planning and potentially additional infrastructure investment, but per-token costs decrease with scale. Plan for autoscaling and load balancing.

“How do we know if our AI vendor changes their model and performance degrades?”

• Detection strategy: Implement automated evaluation pipelines that run your standard test sets against the API regularly. Track metrics like accuracy, latency, and response quality over time.
• Mitigation approach: Maintain baseline performance data and contractual SLAs where possible. Consider multi-vendor strategies for critical applications.

“What if our company requires that our AI system be completely isolated from the internet?”

• Reality check: This necessitates private deployment with on-premises infrastructure. Ensure you have the technical capabilities and budget for completely isolated systems.
• Alternative solutions: Some hybrid approaches allow private deployment in cloud environments with dedicated networks and encryption that may satisfy security requirements while reducing operational complexity.

“Who’s responsible when the AI gives wrong financial advice?”

• Governance framework: Establish clear human oversight requirements, approval workflows for high-impact decisions, and audit trails for all AI-assisted processes.
• Liability management: Treat AI as a decision support tool, not a decision maker. Maintain human accountability and review processes for all critical treasury operations.

“How do we measure ROI on AI investment?”

• Quantitative metrics: Time savings, error reduction, process automation rates, and cost per transaction comparisons.
• Qualitative benefits: Improved analyst satisfaction, faster month-end close, enhanced decision-making capabilities, and competitive advantage in treasury operations.

The No-BS Conclusion: What Actually Works

There is no universally “better” option between public and private LLMs. The right choice depends on your specific combination of data sensitivity, technical capabilities, regulatory requirements, and business objectives.

Public LLMs excel at: Speed to value, access to cutting-edge capabilities, minimal operational overhead, and elastic scaling for variable workloads.

Private LLMs excel at: Data control, customization depth, regulatory compliance, and total cost optimization at high usage volumes.

The winning strategy for most enterprise treasury departments: Start with hybrid implementation using systematic evaluation and scale what works.

Implementation reality: Begin with public APIs for general use cases while building internal capabilities for sensitive data processing. This approach allows you to learn the technology, understand the value proposition, and develop expertise before making major infrastructure investments.

Critical success factors: Regardless of your choice, invest in proper data governance, systematic evaluation, and change management. The best AI strategy is one your team actually adopts and uses consistently.

Remember: Your goal isn’t to have the most sophisticated AI implementation. It’s to improve treasury operations while maintaining security, compliance, and operational excellence standards. A simple, well-executed solution that processes bank statements accurately and provides reliable policy guidance is infinitely more valuable than a complex system that sits unused because it’s too difficult to operate or trust.

Visual Decision Matrix: Public vs Private LLM

Quick Decision Guide Based on This Matrix:

Choose Public if: You score high on cost sensitivity, speed requirements, and operational simplicity while having manageable data sensitivity and compliance needs.

Choose Private if: You score high on data security, compliance control, and customization needs while having the budget and technical capabilities for complex operations.

Choose Hybrid if: You want the best of both worlds – public for general tasks, private for sensitive operations.

Private LLM Implementation Pipeline

┌─────────────────────────────────────────────────────────────────────────┐
│                          PRIVATE LLM PIPELINE                           │
└─────────────────────────────────────────────────────────────────────────┘

┌───────────────┐    ┌────────────────┐    ┌─────────────────┐    ┌──────────────┐
│   DATA        │    │   PREPARATION  │    │   FINE-TUNING   │    │     RAG      │
│  COLLECTION   │───▶│   & CLEANING   │───▶│   (LoRA/QLoRA)  │───▶│  INTEGRATION │
└───────────────┘    └────────────────┘    └─────────────────┘    └──────────────┘
│                    │                    │                     │
│ • Treasury docs    │ • PII scrubbing   │ • Base model       │ • Vector DB
│ • SWIFT examples   │ • Anonymization   │   (Llama/Mistral)  │ • Document
│ • Internal         │ • Format          │ • LoRA adapters    │   chunking
│   policies         │   standardization │ • Instruction      │ • Embedding
│ • Procedures       │ • Quality         │   tuning           │   models
│ • Historical       │   validation      │ • Eval on dev/test │ • Metadata
│   data             │ • JSONL creation  │   sets             │   tracking
                                                               
┌──────────────┐    ┌────────────────┐    ┌─────────────────┐    ┌──────────────┐
│   SAFETY     │    │   DEPLOYMENT   │    │   MONITORING    │    │ GOVERNANCE   │
│ & ALIGNMENT  │───▶│   & SERVING    │───▶│ & OPERATIONS    │───▶│ & UPDATES    │
└──────────────┘    └────────────────┘    └─────────────────┘    └──────────────┘
│                   │                     │                     │
│ • Content filters │ • vLLM/Triton      │ • Performance       │ • Version
│ • Prompt          │   inference        │   metrics           │   control
│   policies        │ • Quantization     │ • Error tracking    │ • Audit logs
│ • Constitutional  │   (INT8/4)         │ • Usage analytics   │ • Retraining
│   rules           │ • Load balancing   │ • Cost monitoring   │   schedules
│ • Bias testing    │ • Auto-scaling     │ • Quality drift     │ • Compliance
│ • Red teaming     │ • API gateway      │   detection         │   reviews

                              ┌─────────────────┐
                              │   FEEDBACK      │
                              │     LOOP        │◀──────────────┐
                              └─────────────────┘               │
                              │                                 │
                              │ • User feedback                 │
                              │ • Error correction              │
                              │ • Performance optimization      │
                              │ • Data updates                  │
                              │ • Model improvements            │
                              └─────────────────────────────────┘

Pipeline Stage Details:

Stage 1 – Data Collection 

• Gather treasury-specific documents and examples
• Ensure legal clearance for all data sources
• Maintain data lineage and version control

Stage 2 – Preparation & Cleaning 

• Critical PII removal and anonymization
• Format standardization and quality validation
• Creation of training/dev/test splits

Stage 3 – Fine-tuning 

• LoRA/QLoRA efficient adaptation
• Systematic evaluation on held-out data
• Hyperparameter optimization

Stage 4 – RAG Integration

• Document processing and chunking
• Vector database setup and optimization
• Retrieval quality testing

Stage 5 – Safety & Alignment

• Content filtering and policy enforcement
• Red teaming and adversarial testing
• Bias detection and mitigation

Stage 6 – Deployment & Serving 

• Production infrastructure setup
• Performance optimization and scaling
• API development and integration

Stage 7 – Monitoring & Operations (Ongoing)

• Real-time performance tracking
• Cost optimization and capacity planning
• Incident response and troubleshooting

Stage 8 – Governance & Updates (Ongoing)

• Regular model evaluation and updates
• Compliance auditing and documentation
• Continuous improvement processes

Questions That Will Make or Break Your Budget Request

Keep in mind some important questions to ask yourself before starting a project

“What’s total cost of ownership over 3 years, and when do we break even?” Your answer should include for example:

• Public LLM: Token costs × projected usage + integration costs + opportunity costs
• Private LLM: Infrastructure + personnel + operational costs over 36 months
• Break-even analysis: Possible up to 12, 24 months (+/-) for private deployment at high usage volumes
• ROI calculation: Time savings × hourly rates + error reduction costs + process automation value

“What happens if AI usage triples next year?”

“How does this compare to hiring additional treasury analysts?”

“What’s our exposure if this AI system gets hacked or leaks confidential data?” And here you could asses:

• Public LLM risks: Data transmission vulnerabilities, vendor security breaches, prompt injection attacks
• Private LLM risks: Internal infrastructure vulnerabilities, insider threats, operational security gaps
• Mitigation strategies: Encryption, access controls, audit logging, regular security testing
• Insurance and liability: Coverage gaps and vendor indemnification terms

“How do we ensure GDPR compliance and avoid regulatory fines?” Possible checklist:

• Data residency: All processing within EU boundaries
• Consent management: Clear policies for data usage in AI systems
• Right to explanation: Audit trails for all AI-assisted decisions
• Data minimization: Only processing necessary information
• Vendor agreements: Proper DPAs and contractual protections

“What if our AI gives wrong financial advice that costs us money?” A hard one.

• Human oversight requirements: All high-impact decisions require human approval
• Audit trails: Complete logging of inputs, processing, and outputs
• Insurance coverage: Professional liability and errors & omissions policies
• Incident response: Clear procedures for identifying and correcting AI errors

“Do we have the internal expertise to manage this, or do we need external consultants?”. Capability assessment:

• Required skills: MLOps engineering, AI security, prompt engineering, system integration
• Current team gaps: Honest assessment of internal capabilities
• External support options: Consulting costs, managed services, hybrid approaches
• Training investment: Upskilling existing team vs. hiring specialists

“How does this fit into our broader digital transformation strategy?”

• Treasury automation roadmap: AI as part of broader process digitization
• Enterprise AI governance: Consistency with company-wide AI policies
• Technology stack integration: Compatibility with existing ERP, banking systems
• Competitive advantage: First-mover benefits vs. fast-follower approach

“What’s our exit strategy if this doesn’t work out?” 

• Public LLM: Easy to discontinue with minimal sunk costs
• Private LLM: Infrastructure repurposing options, model portability
• Hybrid approach: Gradual scaling down of unsuccessful components
• Success metrics: Clear KPIs for go/no-go decisions at milestones

“How do we measure success beyond ‘it seems to work better’?”

• Accuracy: Field extraction precision, policy Q&A correctness
• Efficiency: Processing time reduction, manual task elimination
• Quality: Error rates, hallucination frequency, user satisfaction scores
• Financial impact: Cost per transaction, time-to-close improvements

“What happens when our business processes change?”

• Public LLM: Prompt updates, new instruction examples
• Private LLM: Model retraining, knowledge base updates, version management
• Change management: User retraining, process documentation updates
• Maintenance costs: Ongoing adaptation and optimization expenses

“How do we ensure this scales with our growth?”

• Technical architecture: Auto-scaling capabilities, performance bottlenecks
• Cost scaling: Linear vs. fixed cost components over volume ranges
• Operational scaling: Team size requirements, process standardization
• Integration complexity: API rate limits, system dependencies

“Why should we do this now instead of waiting for better technology in 2 years?”

• Competitive advantage: Early adoption benefits in treasury efficiency
• Learning curve: Building internal AI expertise takes time
• Risk management: Better to learn with controlled experiments than be forced to adopt quickly
• Cost trends: Infrastructure and model costs generally decreasing over time
• Regulatory landscape: Proactive compliance vs. reactive scrambling

DISCLAIMER

Data and Methodological Notes: This article contains inferences and speculations about AI implementation, performance characteristics, and industry adoption patterns based on publicly available information and general technology trends, plus personal experiences in working with public and private LLMs. Specific costs, performance metrics, and implementation outcomes will vary significantly based on organizational requirements, technical infrastructure, and operational capabilities. Always consult with your IT security team, compliance officers, and technology partners before implementing AI solutions with sensitive financial data.

The technical implementation details provided are simplified for accessibility and should be adapted based on your specific technical environment and security requirements. Consider engaging technology partners or cloud infrastructure specialists for implementation support, though recognize that AI in treasury is an emerging field where most expertise is being developed in real-time alongside early adopters.