Qxplain
Model Risk Solutions
AI in Finance
Advisory
Financial institutions, fintechs, and technology providers engage us as advanced AI or quantitative models move toward production, particularly where systems are high-impact, complex, or expected to face internal, audit, or regulatory scrutiny.
We support clients with:
Independent model risk management and validation for ML and GenAI
Explainability framework for transparent and defensible decisions
AI governance and control frameworks fit for audit, board, and regulatory engagement
Risk readiness from prototype to production
Independent. Technically deep. Trusted in high-stakes environments.
Qxplain helps financial institutions manage the risks of advanced AI, combining independent model risk expertise with GenAI and agentic solutions to govern, explain, and deploy intelligent systems at scale.
Research and Development
Our research develops practical capabilities for governing advanced AI at scale, across the model lifecycle and in portfolio decision environments.
The focus is on turning complex, high-dimensional models into transparent, auditable, and decision-ready intelligence.
Representative Engagements
Tier-1 Global Bank — Independent validation framework for GenAI use cases
Top Data Vendor - Model risk management and governance for AI-driven analytics
US based portfolio analytics platform: Causal inference framework for performance attribution
Technology start-up: Assessment of verification and robustification techniques to address fragility of high-dimensional models
Swiss investment firm: Test and evaluation of a multi-agent, multi-modal decision system including reasoning and adversarial critique
RegTech: Validation of agentic orchestration of data feed for AML platform
Core Expertise
Model Risk Management
AI/ML model risk lies as much in how they are evaluated including the assumptions and limits of the validation approach as in the models themselves
“Bringing Models Back Under Control”
High Dimensionality
When decisions become high-dimensional, value comes from preserving signals in the interaction terms that computational simplification tends to filter out
Explainability
Justified trust depends not only on consistent accuracy of prediction but on ability to understand them
Gen AI and Agentic AI in Finance
In GenAI and agentic systems, risk shifts from model error to autonomous actions, making oversight, guardrails, and accountability essential
“Automation With Accountability”
Showcase
Portfolio Intelligence & Analytics (PIA)
A causal framework for factor attribution and regime-aware portfolio intelligence
“Built for Non-Stationary Markets”
MOdel Risk Reasoning and Interpretabilty System (MORRIS)
How to trust the models through lifecycle
“Designed for Explainability, Traceability and Accountability”
Events & Trainings
Events
FCA Supercharged Sandbox Showcase
Model Risk Management Conference
