Reconciliation Suite (RS)
Tookitaki's machine learning (ML)-powered Reconciliation Suite (RS) offers automatic exception/break management and adjustment amount recommendation with high accuracy. Our engine uses machine learning models to understand and learn from patterns (historic reconciliation cases) and then predicts both known and unknown exception cases without any human investigation.
We use semi-supervised learning where we combine the best of supervised and unsupervised models to not only predict known reconciliation breaks/exceptions but also suggest new and unknown cases. This approach is built on top of our DSS (machine learning engine), which is built on cutting-edge cluster computing infrastructure and distributed data parallel architecture design.
Key components of Tookitaki RS are as follows:
- Matching Engine
- Exception Handling Engine
- Adjustment Recommendation Engine
Key benefits of Tookitaki RS are as follows:
- 50% reduction in investigation time
- >95% accuracy in break resolution
- Dramatic improvement in existing match rates