Retail & Consumer Goods
- Personalization and recommendation
- Dynamic pricing optimization
- Demand-driven inventory management
- Automated customer service at scale
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Predictive maintenance reduced unplanned downtime by 67% across 12 production lines
Financial services is where AI consulting firms prove themselves or expose themselves. The data is complex, the compliance requirements are non-negotiable, and the cost of a system failure is measured in regulatory action, not just lost productivity. We have delivered production AI systems inside risk frameworks at regional and global banks, asset managers, and insurance groups. We know what it takes to get a model through model risk management, and we know what data quality problems look like before they become project failures.
What we do for clients
We work across the full financial services AI stack:
- Credit analysis and underwriting automation - GenAI systems that draft credit memos, underwriting summaries, and risk assessments from structured loan data and internal policy documentation
- AML and transaction monitoring - ML models that identify suspicious transaction patterns with lower false positive rates than rules-based systems, designed for regulatory defensibility
- Regulatory reporting automation - LLM-powered systems that generate Basel III, IFRS 9, and MiFID II compliant reports from structured data sources, with audit trails built in
- Fraud detection and prevention - Real-time scoring systems for transaction fraud, identity fraud, and application fraud, with explainability outputs for compliance teams
- Trading desk intelligence - Market signal analysis, portfolio risk modeling, and execution support tools for institutional trading desks
- Document intelligence - Extraction, classification, and summarization of loan documents, contracts, prospectuses, and regulatory filings at scale
How we run a Financial Services engagement
Every financial services engagement follows the same three-phase structure, with a senior partner and a compliance-aware engineer at each stage:
- Diagnose. We begin with a data audit and a compliance mapping exercise run in parallel. Data quality problems and regulatory constraints are the two most common reasons financial services AI projects fail - we surface both in week one before they become build-time surprises. We interview your risk, compliance, and technology stakeholders to understand the approval chain a new AI system will need to pass through, and we design the architecture around that reality from day one.
- Build and validate. Every model we build in financial services goes through SR 11-7 aligned model risk management documentation, bias testing, and explainability validation before deployment. We don't hand a system to your model risk team at the end and hope it passes. We build to their standards from the start and involve them in the review process throughout the build.
- Deploy and monitor. Staged rollout to a controlled user group, with human-in-the-loop controls and override mechanisms built into every automated decision. Full audit logging from day one. We stay engaged through the first 30 days of live operation and produce a post-deployment performance report at the 90-day mark.
The best financial services AI systems are invisible to the regulator and indispensable to the team using them. That is the bar we build to.
— Dr. David Kim, Founder & CEO
Where we fit best
Most of our financial services engagements are with regional and mid-market banks, asset managers, and specialist lenders with complex data environments and meaningful AI budgets. We have worked with institutions ranging from $2B to $180B in assets under management.We are a particularly strong fit for:
- Credit and risk teams spending significant analyst time on documentation that could be automated without reducing judgment quality
- Compliance functions under pressure to reduce manual reporting burden while maintaining regulatory defensibility
- Technology leaders who have had a previous AI initiative fail model risk review and need a partner who understands why
- Trading and investment teams that want AI-augmented analysis without compromising the audit trail their compliance team requires
We work closely with your model risk, compliance, and legal teams throughout every engagement - and have a strong track record of getting AI systems through internal approval processes that have stopped other implementations cold.
