Under Construction — Launching Soon
The intersection of LLMs and Financial Crimes
As financial institutions pivot toward a zero-trust posture, the downstream layer of Fraud and AML detection has become the ultimate control surface. But how reliable are today's leading AI models at holding this line? Our latest study of four frontier LLMs reveals a sobering gap between AI potential and operational reality. With a median failure rate of nearly 38% on fraud scenarios and significant inconsistency across providers, the research suggests that LLM reliability is not yet at the precision bar required for operational readiness. Our FinCrimeBench™ initial research report provides a comprehensive look at these structural weaknesses, mapping out 100+ expert-curated scenarios across nine fraud sub-threats. It serves as a vital benchmark for any organization currently piloting AI for fraud augmentation or frontier LLM providers looking to train models, offering a clear roadmap for moving from experimental "partial capability" to defensive-grade reliability.
The report will be available on Wednesday May 27th with options to request a summary, a full white paper or an executive briefing. Stay tuned!
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© 2026 CoveLabs.ai LLC. All rights reserved. FinCrimeBench™ is a proprietary benchmark developed and owned by CoveLabs.ai LLC.
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Our Approach
How We Enable LLMs for Financial Crime
For Financial Services & Fintech Leaders
Evidence-based AI deployment guidance
Independent, benchmark-grade evaluation of where today's leading models are operationally ready and where they are not.
Institution-specific testing
Customized benchmarking of frontier models against your threats and use cases, giving you defensible pre-deployment evidence not vendor claims.
Infrastructure to accelerate your own AI strategy
Our threat library, scenario authoring tools, and evaluation pipelines can be adapted to support your training programs, platform development & AI governance work.
Agentic AI architecture and design partnership
Capability analysis and design support across the full arc of agentic AI deployment for fraud and AML.
Standing access to forward benchmark results
FinCrimeBench results delivered as the comprehensive Fraud and AML benchmark builds out, keeping your AI evaluation calibrated to the current state of the technology.
For LLM Researchers
Domain ground truth for post-training and evals
Tagged scenarios with locked acceptance criteria and expert-authored reference answers tied to authoritative sources.
Capability gap visibility
Performance and failure-mode data across typologies surfacing exactly where SFT, RL, and reasoning-alignment investments would close capability gaps.
Threat-driven data scaffolding
Our frameworks, expert-authored scenarios, and structured parameter tags allow data generation that preserves domain validity at scale.
Reasoning fidelity, not just answer accuracy
Constraints aligned with authoritative guidance and industry practices support training signals that drive consistent, auditable reasoning.
Direct collaboration with domain expertise
A standing partnership with practitioners who have designed fraud and AML programs at scale to inform model post-training, dataset curation, and capability evaluation work.
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The Problem
Financial Crime Is a Trillion-Dollar Crisis
Legacy detection systems were not designed for modern, cross-border, multi-layered financial crimes - now exacerbated by bad actors leveraging AI tools. Rule-based engines generate overwhelming false positives, investigators are understaffed, and sophisticated typologies evolve faster than compliance teams can respond.
$500B
Annual Global Fraud Losses
Estimated annual losses from fraud across all financial sectors worldwide
$1–2T
Money Laundered Annually
Global estimate of illicit funds laundered through the financial system each year
Sources: Interpol 2026, TransUnion 2025, UNODC
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Our Expertise
Deep Financial Crimes Domain Knowledge
Our team brings an average of 15+ years of hands-on experience in financial crimes — spanning fraud, AML, sanctions, and regulatory compliance — across the full spectrum of financial institutions.
Banks & Credit Unions
Strategy and analytics for enterprise-scale fraud and AML programs at top-tier global banks
Fintechs
Fraud and compliance program design and enhanced detection for high-growth platforms
Wealth & Insurance
Risk and compliance frameworks and implementation for wealth management, insurance, and investment firms
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Technology Focus
LLMs Trained for Financial Crime
While frontier LLMs can be extremely useful tools, Cove Labs' research has shown that LLM models do not have adequate training for financial crime. Cove Labs applies Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to large language models — enabling them to detect, investigate, and explain complex financial crime typologies with far greater precision than traditional systems.
This methodology ensures models are not just accurate, but observable, explainable, auditable, and aligned with regulatory expectations.
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Why Cove Labs
The Right Team at the Right Moment
The convergence of mature LLM capabilities and persistent gaps in financial crime detection creates a rare window. Cove Labs is uniquely positioned — with the domain credibility, technical depth, and institutional relationships — to lead this transformation.
Proven Track Record
20+ fraud and AML engagements executed across major financial institutions
AI-Native by Design
Built from the ground up for LLMs — not a retrofit of legacy rule engines
Operator Credibility
Former executives from BofA, FICO, Discover, and HSBC who have lived the problem
Experience Across the Industry
Experience spanning institution and fintech sizes and types, customer segments, and regions.
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Get Involved
Let's Build the Future of Financial Crime Detection
We're engaging with frontier LLMs, banks, fintechs, regulators, and research partners who share our conviction that LLMs can fundamentally transform how financial crime is detected and investigated. Reach out to start the conversation.
Partnership Inquiries
Open to collaborations with financial institutions, data providers, and AI research teams
Stay Updated
Check back soon — our full platform and research publications are launching shortly
Hiring
Building a team of passionate Financial Crime experts and practitioners
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Leadership
Built by Industry Veterans
Our founding team includes former executives from some of the most recognized names in financial services and fraud technology — bringing operational credibility and deep institutional knowledge to every engagement.
Edward Aluise
Former Bank of America Executive
Payments leadership roles across the Consumer & Small Business and Commercial & Corporate businesses; US and global responsibilities
Ram Krishnamurthy
Former Data Science Executive at FICO
Credit card and payments modeling and machine learning at scale for fraud detection, as well as account opening and account takeovers
Brian Hughes (Advisor)
Former Executive at Discover & HSBC
Senior business and risk executive with experience owning Fraud & AML, including global risk strategy and sanctions compliance
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Use Cases
Targeting High-Impact Fraud & AML Scenarios
Fraud Detection
- Identity Theft & Synthetic Identify Fraud
- Account Takeover
- Payment Fraud (all payment types)
- First Party Fraud
- Real-time Transaction Monitoring
AML & Financial Intelligence
- Transaction Monitoring & SAR Generation
- Sanctions Screening & Name Matching
- Customer Due Diligence (CDD / EDD)
- Network Analysis
- Typology Detection
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Legal
Privacy Policy
Last updated: May 2026
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