This guide offers insights into what to look for in a finance operations solution that employs generative AI. Here, you'll find essential questions to ask providers about their technology and features, so you can choose the solution that best fits your needs.
Generative AI is reshaping finance operations
AI has been used in finance for years to automate tasks, detect anomalies, and surface insights. Now, generative AI is taking these capabilities further—enabling systems to understand unstructured data, generate responses, and handle complex workflows that previously required human judgment.
But not all GenAI solutions are created equal. The difference between a tool that delivers ROI and one that creates risk lies in the details: how models are trained, how data is secured, and how the system explains its decisions.
"Generative AI represents a fundamental shift in how finance teams can operate—moving from rule-based automation to intelligent systems that understand context and adapt to complexity."
Four critical components to evaluate
When assessing GenAI solutions for finance, focus on these four areas:
1. AI and machine learning capabilities
The foundation matters. Look for solutions built on models trained specifically for finance documents—invoices, receipts, contracts, expense reports. Generic AI models struggle with the nuances of financial data. AppZen's Mastermind Platform is pre-trained on millions of financial documents across industries.
2. Trustworthiness and explainability
Finance requires auditability. Every AI decision should come with clear reasoning that auditors can review. Black-box AI creates compliance risk. Ask vendors: Can you show exactly why the system flagged this transaction?
3. Data security and privacy
Your financial data is sensitive. Ensure the solution doesn't use your data to train models for other customers. Verify SOC 2 Type II compliance, data encryption standards, and clear data retention policies.
4. Integration and deployment
AI that doesn't connect to your existing systems creates silos. Look for pre-built integrations with your ERP (SAP, Oracle, Workday, NetSuite) and expense systems (Concur, Coupa). AppZen Inbox works with any email client including Gmail and Outlook.
Questions to ask GenAI vendors
Use these questions to evaluate any finance AI solution:
About the AI model
- What data was the model trained on? Is it specific to finance?
- How do you handle documents in multiple languages and currencies?
- What's your accuracy rate on first-pass processing?
- How does the system learn from corrections?
About trust and compliance
- Can you provide audit trails for every AI decision?
- How do you prevent and detect hallucinations?
- What certifications do you hold (SOC 2, ISO 27001, GDPR)?
- How do you handle PII and sensitive financial data?
About implementation
- What's the typical time to value?
- Do you offer a pilot program with our actual data?
- How does pricing scale with volume?
- What support is included?
"The best AI vendors welcome tough questions about their technology. Evasive answers about training data, accuracy metrics, or security practices are red flags."
Warning signs to watch for
Be cautious of vendors who:
- Promise 100% accuracy — No AI system is perfect. Honest vendors discuss error rates and how they handle exceptions.
- Can't explain decisions — If they can't show why the AI made a choice, auditors won't accept it either.
- Use your data for training — Your financial data shouldn't improve models for competitors.
- Require extensive rule-building — True AI learns from data, not from you programming hundreds of rules.
- Lack finance-specific experience — Generic AI platforms often struggle with invoice formats, expense policies, and compliance requirements.
GenAI glossary for finance leaders
Key terms to know when evaluating solutions:
Large Language Model (LLM) — AI systems trained on vast text data that can understand and generate human-like text. Used for reading unstructured documents like emails and contracts.
Hallucination — When AI generates plausible-sounding but incorrect information. Critical to prevent in finance where accuracy matters.
RAG (Retrieval-Augmented Generation) — A technique that grounds AI responses in actual data rather than just training knowledge, reducing hallucinations.
Fine-tuning — Customizing a general AI model with domain-specific data (like finance documents) to improve accuracy.
Straight-through processing (STP) — Transactions processed without human intervention. AppZen Autonomous AP achieves 80%+ STP rates.
Making the right choice
The right GenAI solution for finance should:
- Understand financial documents natively, not through bolt-on AI
- Provide clear explanations for every decision
- Protect your data with enterprise-grade security
- Integrate seamlessly with your existing systems
- Deliver measurable ROI within months, not years
AppZen's Mastermind Platform powers expense audit, card audit, and autonomous AP solutions trusted by leading enterprises worldwide. Our AI is built specifically for finance—trained on millions of invoices, receipts, and financial documents to deliver accuracy that generic AI can't match.
Ready to see how AppZen's GenAI handles your actual documents? Explore AI Analytics or request a demo to process your real invoices and expenses.