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Data Mining
Black Box
Transparency
Accuracy & Reliability
It integrates multiple models and algorithms to address the “black box” issue of conventional machine learning in the industry, helping banks improve model transparency as required by regulators, enhance model precision and make decisions more reliable
According to the needs of financial clients, Ruihedata can either build interpretable machine learning models from scratch, or insert ex-post analysis into legacy models.
While ensuring high precision of models, the solution makes in-depth interpretation for various application scenarios, helping clients reduce compliance risks in their models.
The interpretable machine learning model is easy to build and deploy, without the need of complex pre-processing of data. With low resources consumption for machine training, the model can run ultra fast.
The solution has been applied to multiple financial marketing scenarios such as precise product recommendation, customer mining, and early warning of customer churn. Excellent results show the strength of close integration between AI and financial service.
It helps financial clients remove business bottlenecks, deliver personalized service, and make operation more efficient.