AI Reconciliation Strategy
Our current rules engine is evolving into a hybrid model combining BERT and XGBoost for enhanced data reconciliation.
Current Rules Engine
We start with deterministic matching using business rules, fuzzy string matching, and pattern recognition to handle the complexity of medspa transactions.
ML Evolution Path
- Hybrid BERT + XGBoost model for enhanced matching
- Synthetic data bootstrapping for training
- Active learning with human feedback loops
- Safety rails to prevent incorrect matches
Data Strategy
- Multi-source integration (POS, processors, loyalty programs)
- Real-time reconciliation: 95% automated match rate (99.5% with one-click review)
- Audit-ready transaction trails
- Continuous model improvement
See It In Action
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