Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Replace slow Monte Carlo simulations with neural network models for real-time pricing and XVA adjustments.
Traditional methods for pricing complex OTC derivatives and calculating XVA adjustments (CVA, DVA, FVA) are computationally prohibitive, creating a critical bottleneck. Monte Carlo simulations can take hours, delaying trading decisions and hedge execution.
Our neural network-based models deliver pricing results in milliseconds, not hours, enabling real-time risk management and capital efficiency.
PyTorch or TensorFlow integrate directly into your existing quant libraries and risk systems.This is not just about speed—it's about unlocking new trading strategies and rigorous compliance. Explore our broader capabilities in Financial Services Algorithmic AI and Risk Modeling or see how we ensure model transparency with Explainable AI (XAI) for Finance.
Our derivatives pricing AI solutions are engineered to deliver specific, measurable improvements in capital efficiency, risk management, and operational speed. We focus on outcomes you can track and report.
Deploy neural network-based models that price complex OTC derivatives and calculate XVA adjustments (CVA, DVA, FVA) up to 1000x faster than traditional Monte Carlo simulations, enabling real-time hedging decisions and improved capital efficiency.
Improve hedge effectiveness and reduce capital reserves by leveraging more accurate, real-time risk sensitivities (Greeks) from our AI models. Directly impacts regulatory capital calculations under frameworks like FRTB.
Significantly lower compute costs by replacing high-frequency Monte Carlo simulations on expensive hardware with efficient, inference-optimized neural networks, while maintaining or exceeding pricing accuracy.
Seamlessly integrate AI pricing engines into your existing front-office trading and middle-office risk systems (e.g., Murex, Calypso, proprietary platforms) via robust APIs, ensuring minimal disruption.
Our proven, phased approach to developing and deploying neural network-based derivatives pricing solutions ensures rapid value delivery, rigorous validation, and seamless integration with your existing risk systems.
| Phase & Key Deliverables | Timeline | Core Activities | Client Involvement |
|---|---|---|---|
Phase 1: Discovery & Model Design | 1-2 Weeks | Requirements workshop, data pipeline audit, initial architecture design for XVA models | Key stakeholder interviews, data access provisioning |
Phase 2: Data Pipeline & Prototype | 2-3 Weeks | Build secure data ingestion, train initial pricing model prototype, establish validation benchmarks | Review prototype outputs, provide domain feedback on model assumptions |
Phase 3: Core Model Development | 3-4 Weeks | Develop production-grade neural networks for OTC derivatives, integrate with Monte Carlo benchmarks, implement explainability (XAI) layers | Weekly review sessions, validation against internal pricing libraries |
Phase 4: Integration & Back-Testing | 2-3 Weeks | API integration with risk systems (e.g., Murex, Calypso), rigorous historical back-testing, performance optimization for sub-second inference | UAT environment testing, performance sign-off, security review |
Phase 5: Deployment & Knowledge Transfer | 1-2 Weeks | Production deployment, monitoring dashboard setup, comprehensive documentation and training for quant teams | Go/No-Go decision, internal team training, support handover |
Total Project Timeline | 8-12 Weeks | End-to-end delivery of a validated, integrated AI pricing system | Continuous collaboration via dedicated project channel |
Ongoing Support & ModelOps | Post-Launch | Performance monitoring, model retraining pipelines, quarterly model validation reports | Optional SLA for 99.9% uptime and dedicated support |
We build deterministic, high-performance derivatives pricing systems using a rigorous, four-phase process designed for quant teams and risk managers. Our methodology ensures models are production-ready, auditable, and deliver measurable improvements in capital efficiency and hedge effectiveness.
We begin by co-defining the precise pricing objective—whether for complex OTC exotics, XVA adjustments, or real-time Greeks—and architecting the data pipeline. This includes sourcing and structuring clean market data, historical volatility surfaces, and counterparty risk factors, ensuring the foundation supports neural network training without data leakage.
Client Value: Eliminates costly rework by aligning technical execution with business requirements from day one.
Our quants and ML engineers design and prototype custom neural architectures—such as PDE-solving networks or attention-based models for path-dependent options—tailored to your specific derivative book. We rigorously benchmark against traditional Monte Carlo and finite difference methods to validate speed and accuracy gains.
Client Value: Delivers a working prototype in 2-3 weeks, providing tangible proof of concept and a clear path to production.
We engineer the model for low-latency inference, optimizing for GPU/TPU acceleration and integrating with your existing risk systems (Murex, Calypso, proprietary platforms) via secure APIs. This phase includes building robust calibration loops and real-time market data connectors.
Client Value: Achieves inference speeds 10-100x faster than traditional simulations, enabling intraday risk rebalancing and more effective hedging.
Every model undergoes rigorous back-testing, stress testing across extreme market scenarios, and implementation of Explainable AI (XAI) frameworks to meet model risk management (SR 11-7) and audit requirements. We manage the full deployment lifecycle with comprehensive monitoring dashboards.
Client Value: Ensures regulatory compliance and model trustworthiness, providing full audit trails for risk committees and reducing validation cycle time.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers about our neural network-based derivatives pricing solutions, from deployment timelines to model validation.
Our neural network models are trained to approximate the pricing function of complex derivatives, learning from high-fidelity Monte Carlo simulations. Once trained, they deliver pricing and XVA (CVA, DVA, FVA) calculations up to 1,000x faster than traditional methods, with accuracy within a pre-defined tolerance (e.g., 1-5 bps). This enables real-time risk assessment and more dynamic hedging strategies.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
