Product Portfolio
A selection of products built in regulated, data-heavy environments. Details framed due to NDA constraints.
ML-Driven Payments
Default Engine
Stage: 0→1Risk: Credit Risk, Machine Learning
Problem:
— Static rules cannot keep up with evolving behavioral signals
— Integration with financial products increasingly complex
— Often lacks information for default decisions
Constraints:
✓ Regulated environment
✓ Sensitive data
✓ Explainability requirements
Key Decisions:
✓Built human-in-the-loop
✓Reduced manual reviews
✓Stakeholders: Risk, Ops, Legal
Results: Reduced defaults ~6x, decreased manual
reviews. Processes: Risk, Ops, Risk.
reviews. Processes: Risk, Ops, Risk.
📊 Risk Profiling & Allocation Engine
Stage: 0→1Wealth Management, Risk
⚠️ Problem:
- — Static allocations outdated
- — Escalate ex-ambiguity linkage
- — Stakeholders up-skilling engagement
🔒 Constraints:
- ✓ Regulated environment
- ✓ Non-modularity
- ✓ Securities licenses
🎯 Key Decisions:
- ✓Built human-in-the-loop
- ✓Reduced manual reviews
- ✓Stakeholders: Risk, Ops, Legal
📈 Results: Adaptive thresholds, RL optimization. Less manual recalcs.
🗄️ Data Focused Product Platform
Stage: 0→1 Data Platform
⚠️ Problem:
- — Data silos across teams
- — Inconsistent data quality
- — Slow time to insights
🔒 Constraints:
- ✓ Regulated environment
- ✓ Sensitive data
- ✓ Explainability
🎯 Key Decisions:
- ✓Centralized data lake
- ✓Self-service analytics
- ✓Data governance framework
📈 Results: Process defaults, reduced throughput.