VeriQuant leverages LLM agents and formal verification to address critical mathematical errors in financial models, targeting verifiable properties to support regulatory compliance and reduce multi-billion dollar losses from model failures.
Financial institutions face significant losses from mathematical errors in quantitative models. VeriQuant is developing LLM-powered formal verification technology to systematically address verifiable aspects of this critical industry challenge, while acknowledging the fundamental limits of what can be proven mathematically.
Researching AI agents to translate financial models into formal mathematical specifications, exploring solutions to costly model failures in quantitative finance.
Building mathematical proof systems to provide auditable evidence of model correctness, with potential applications to regulatory frameworks.
Starting with options pricing and derivatives models as initial use cases, with potential for broader applications across financial mathematics.
Planning APIs to integrate with quantitative workflows, targeting compatibility with Python, R, MATLAB, and established risk platforms.
Exploring systems for ongoing model validation as conditions change, working toward improved mathematical reliability in financial models.
Early market research indicates significant interest in mathematical verification solutions among financial institutions facing model risk challenges.
Understanding both the power and fundamental limitations of formal verification (Rice's theorem, Gödel's incompleteness theorems) to build trustworthy systems within provable boundaries.
The quantitative finance industry faces critical challenges with mathematical model validation, creating a multi-billion dollar market opportunity for automated verification solutions.
Global risk management software market expected to reach $47B by 2030
Quantitative finance software segment within risk management market
Target market for formal verification in top-tier financial institutions
Market demand for automated model validation solutions
Annual documented losses from quantitative model failures
Active market validation through industry engagement
Strong early interest from financial institutions
Manual review processes miss critical mathematical errors, leading to billion-dollar losses and regulatory violations.
Institutions spend millions on manual model validation to meet Basel III and CCAR requirements.
Unverified models create systemic risk exposure that threatens institutional stability.
Manual processes cannot scale with the increasing complexity and volume of quantitative models.
Our breakthrough approach combines Large Language Models with formal verification techniques, creating autonomous agents that can understand, analyze, and verify complex quantitative finance models with mathematical rigor.
Specialized LLM fine-tuned on mathematical proofs and quantitative finance literature, capable of understanding complex financial models and their underlying mathematical foundations.
Converts mathematical models into formal specifications using Lean 4 theorem prover, targeting verifiable properties with mathematical guarantees within the scope of what is provable.
Continuously monitors model execution, detects anomalies, and provides detailed analysis of potential mathematical inconsistencies or edge cases.
Ensures models meet regulatory requirements (Basel III, CCAR, IFRS 17) by validating against regulatory mathematical frameworks and generating compliance reports.
Building domain-specific LLM capabilities for mathematical reasoning in quantitative finance using reinforcement learning from formal verification feedback.
First-of-its-kind integration between LLMs and Lean 4 theorem prover for automated mathematical verification in finance.
Developing incremental verification algorithms targeting real-time validation of model changes with optimized response times.
Architecting RESTful APIs and planned connectors for major platforms (MATLAB, R, Python, Bloomberg Terminal, Refinitiv).
Mathematical consistency, arbitrage-free conditions, boundary behaviors, convergence properties, and compliance with specified constraints within formal model specifications.
Rice's theorem and Gödel's incompleteness theorems establish that some properties remain unprovable. We cannot verify market predictions, optimal parameter selection, or behavior under all real-world conditions.
Understanding these boundaries positions VeriQuant to build trustworthy systems focused on verifiable properties, avoiding overconfident claims that damage credibility in academic and industry settings.
Building minimum viable product with core verification capabilities
Integrating formal verification with theorem prover technology
Developing coordinated AI system for model verification
Working demonstration of binomial options pricing verification
VeriQuant is developing a unique approach combining LLM agents with formal verification for quantitative finance, targeting an underserved market where current solutions fall short of providing mathematical guarantees.
| Capability | VeriQuant (Target) | Traditional Risk Vendors | Academic Tools | AI/ML Platforms |
|---|---|---|---|---|
| Formal Mathematical Verification | ||||
| LLM Agent Integration | ||||
| Real-Time Verification | ||||
| Regulatory Compliance Automation | ||||
| Enterprise Integration |
Developing the first solution combining LLMs with formal verification for financial models. Early research shows promising potential for breakthrough capabilities.
Focusing specifically on quantitative finance verification creates opportunities for deep domain expertise and specialized solutions.
Platform designed to improve with each verification, potentially creating compound learning advantages over time.
Building deep understanding of regulatory requirements creates opportunities for compliance-focused differentiation.
Deep understanding of both capabilities and fundamental limitations (Rice's theorem, Gödel's incompleteness) builds credibility with sophisticated users where others make overconfident claims.
SAS, MATLAB, Moody's Analytics
Manual validation processes, no formal verification, limited AI integration
Lean 4, Coq, Isabelle/HOL
Require PhD-level expertise, no enterprise integration, limited finance domain knowledge
DataRobot, H2O.ai, Palantir
Focus on model deployment, not mathematical verification. No formal guarantees
Google DeepMind, OpenAI, Microsoft Research
General AI research, not finance-specific, no commercial product focus
VeriQuant is working to establish rigorous formal verification as a new standard for quantitative finance model validation, targeting a market gap where current solutions lack mathematical rigor while understanding fundamental theoretical limits.
Developing novel approach to LLM-formal verification integration
Creating prototype and proof-of-concept demonstrations
No current solutions provide formal mathematical verification
Our approach to formal verification is grounded in mathematical rigor and theoretical understanding. Here's what VeriQuant can and cannot prove.
Understanding these theoretical boundaries is precisely what sets VeriQuant apart. While other solutions make overconfident claims, we focus on what can actually be proven mathematically. This rigorous approach builds trust with sophisticated users and positions us as the technically credible solution in academic and enterprise environments where mathematical precision matters.
Our approach is grounded in computational complexity theory and formal methods research. We leverage insights from Rice's theorem (1953) about the undecidability of semantic properties and Gödel's incompleteness theorems (1931) about the limits of formal systems to build verification tools that are both powerful and honest about their scope.
VeriQuant is developing a SaaS platform for formal verification in quantitative finance. We're building our MVP and conducting market validation to establish product-market fit.
Build MVP with core verification capabilities. Validate technical approach with proof-of-concept demonstrations and early market research.
Partner with select financial institutions for pilot programs. Refine product based on real-world feedback and establish initial case studies.
Launch commercial platform targeting tier-1 financial institutions. Focus on building initial customer base and proving product-market fit.
Expand platform capabilities and customer base. Build enterprise sales team and explore international markets based on proven success.
Building core technology and validating market demand through research and prototype demonstrations
VeriQuant is an early-stage startup actively building a world-class team. We're seeking top talent in AI research, quantitative finance, and formal verification.
Startup Founders
Early-stage team with combined experience in quantitative finance, AI/ML research, and enterprise software development. Building the future of financial model verification.
• Quantitative finance background
• AI/ML and formal verification expertise
• Enterprise software experience
Join Our Team
Looking for exceptional talent in AI research, quantitative finance, formal verification, and enterprise software. Help us build the future of financial AI.
• Senior AI/ML Engineers
• Quantitative Finance Experts
• Formal Verification Specialists
• Product & Business Development
LLM & Reasoning
Deep learning, mathematical reasoning, AI agents
Theorem Proving
Lean, Coq, Isabelle/HOL verification
Model Experts
Risk management, derivatives, portfolio optimization
Product & Sales
Enterprise software, fintech sales
We prioritize deep expertise in quantitative finance, formal verification, and LLM research over generic software engineering skills.
Academic rigor meets startup speed. We value publication-quality work delivered at enterprise scale and timeline.
Our interdisciplinary approach requires team members who can bridge AI research, mathematics, and financial markets.
We prove our solutions work, not just demonstrate them. Every feature is mathematically verified.
We build solutions for real financial problems, validated through direct customer feedback and market research.
We prototype quickly and learn fast while maintaining the highest standards of mathematical correctness.
We're building mission-critical infrastructure for the finance industry. Quality and reliability are non-negotiable.
Experience the power of LLM agent-driven formal verification through our interactive demos and live product access. See how we're transforming quantitative finance validation today.
Explore our prototype demonstration showing how LLM agents can approach mathematical verification of quantitative finance models, including binomial options pricing with formal proof concepts.
Try our verification engine with sample quantitative models
Get early access to our development platform
Custom demonstration for your specific use case
Prototype Development & Validation
Our prototype successfully demonstrates the integration of LLM agents with Lean 4 formal verification for options pricing models, validating our core technical approach.
Industry Interest & Validation
Our market research reveals strong industry demand for mathematical verification solutions, with financial institutions actively seeking alternatives to manual model validation.
Help us validate market demand and be first to access VeriQuant's LLM-powered formal verification platform for quantitative finance.