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World Congress 2026 Europe • Jul 10, 2026 • Session details

Hard Problems Hide in Boring Places: Turning Accounting Workflows into AI Products

Oleksandr Korotkykh , Tolga Sümer

How do you deploy LLMs in fintech without failing strict compliance audits? Discover how Pliant’s dual-LLM architecture balances real-world reasoning with fully traceable, fintech-grade rule enforcement.

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#1 about 4 min

Managing corporate payments with programmable virtual cards

Treating corporate credit cards like scoped API tokens enables granular control over merchant spending and expiration limits.

#2 about 3 min

Extracting compliance rules from unstructured expense policies

Company expense policies are typically long, obfuscated legal documents that hide complex compliance requirements deep in text.

#3 about 2 min

Analyzing structured transaction data and invoice line items

Transactions arrive as complex JSON payloads containing detailed accounting metadata and OCR-extracted receipt items for evaluation.

#4 about 3 min

Evaluating deterministic rule engines for transaction compliance

Traditional rule engines offer processing speed and transparency but struggle to scale with rapidly changing bespoke customer configurations.

#5 about 2 min

Replacing manual code creation with naive LLM prompts

Directing an artificial intelligence model to blindly assess compliance handles natural ambiguity well but completely destroys system auditability.

#6 about 2 min

Compiling natural language policies into strict deterministic rules

Using underlying language models to generate executable code initially seems like the perfect structural balance of flexibility and transparency.

#7 about 4 min

Failing to catch real-world nuance with fixed lists

Code-based pipelines fail when receipt items require generalized contextual knowledge, forcing engineering requirements to mandate flexible judgment alongside auditability.

#8 about 3 min

Designing an auditable language model evaluation architecture

Extracting simplified rules linked directly to original source documents allows dynamic evaluation while enforcing strict boundaries and human-in-the-loop approvals.

#9 about 2 min

Surfacing system reasoning and rule violations visually

Returning specific violated policy IDs and grounded reasoning ensures user interfaces can explicitly map decisions back to trusted documentation.

#10 about 3 min

Trading operational latency and reproducibility for actual accuracy

Utilizing large language models in functional paths requires processing queues in periodic batches and ultimately accepting non-deterministic execution states.

#11 about 1 min

Exposing hidden failures using messy production data immediately

Constructing robust AI features necessitates testing against raw, unstructured real-world data instead of sanitized inputs to discover actual limitations early.

Matching moments

4:32 min

Testing AI limits in enterprise software design

David Tielke David Tielke · WWC Europe 2026

2:11 min

Securing heterogeneous legacy payment infrastructure against AI

Michele Zuccala Michele Zuccala +4 · WWC Europe 2026

3:05 min

Transitioning from demos to real business processes

Deivids Vilkinsons Deivids Vilkinsons +3 · WWC Europe 2026

1:55 min

Identifying and fixing over-engineered AI calls through observability

diabhey diabhey · WWC 2025

44 sec

Addressing corporate compliance challenges with secure enterprise AI

Hissan Usmani Hissan Usmani · WWC 2025

5:20 min

Shifting from AI hype to enterprise operations

Deivids Vilkinsons Deivids Vilkinsons +3 · WWC Europe 2026

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