Cloudwalk has made a number of significant functional breakthroughs in this field:
The model can automatically search and mine features. With the search strategy manually configured, the model is able to automatically process and screen features to find the most effective feature combination, and apply the automatic feature engineering to mine feature combinations and bin the features, without manually selecting the features and adjusting the binning. Users only need to define the search range of model parameters, and the algorithm can automatically search and adjust the optimal model parameters. Using the decision-making tree for feature extraction, this model is characterized by strong nonlinear feature fitting ability. Experiments on multiple datasets show that with an effect similar to that of other models established based on the decision-making tree algorithm, e.g. LGB and XGB, this model can output the risk value corresponding to each feature and feature combination and then deploy these values through the decision-making engine.
Background and challenges
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Breaking through the difficulties and bottlenecks faced by traditional payment, continuously innovating payment products and services -
Taking the layout of merchants and consumer experience as a breakthrough point, meeting the diverse, intelligent, and convenient payment needs of users, and improving the stickiness of user payment product usage; -
The policy level factors of the People's Bank of China have promoted the emergence of smart, digital, and experiential facial payment.
