• :::Home
  • HK Technologies
  • Comonotone-Independence Bayes Classifier (CIBer): A Robust and Effective FinTech and InsurTech Tool

Comonotone-Independence Bayes Classifier (CIBer): A Robust and Effective FinTech and InsurTech Tool


Comonotone-Independence Bayes Classifier (CIBer), is a novel FinTech and InsurTech tool which models strong dependence structures among feature variables by comonotonicity. It improves the clustering of predictor variables and classification performance by processing all of them and efficiently modelling their dependency structure. It demonstrates superior performances compared to existing machine and deep learners on many finance and insurance datasets.

Problem addressed

In the era of big data, there are often a large scale of data available in the industry of Finance and Insurance. There are also immediate needs for companies to use the data to better classify the clients' risks and predict their needs. Our innovation can provide with a far better prediction results compared to the existing methods.

  • Existing Bayes classifiers assume all feature variables are conditional independent. CIBer can model the variables by comonotonicity and independence, resulting in a better performance.
  • We propose a novel joint encoding scheme, to encode the categorical feature variables in order to better extract their dependence structure.
  • CIBer can be extended to the context of regression, traditional bayes classifier is only limited to classification.
Key impact
  • CIBer can handle all discrete, continous and categorical feature variables well because of its unique feature engineering.
  • CIBer separates feature variables into different comontonic clusters, and the results are also interpretable while those from other classifiers are not.
  • With the better modelling of feature variables, CIBer often outperforms existing machine learning methods.
  • Our method can be easily implemented to many real-world dataset.
  • Silver award, the 48th Geneva International Exhibition of Inventions
  • The central issue in insurance and finance is customer risk classification, CIBer can be used to perform accurate classification in the presence of many categorical variables.
  • CIBer can be applied to companies' financial statements and predict their future returns.
  • As an explainable AI, CIBer may be preferred by companies as it generates outputs in alignment with compliance requirements.
  • There are also a lot of potential applications in other fields, for example, education, psychology and marketing, while CIBer can also be used in regression tasks besides classification.


  • US Provisional appn no. 63/487,282
The Chinese University of Hong Kong (CUHK)

Founded in 1963, The Chinese University of Hong Kong (CUHK) is a forward-looking comprehensive research university with a global vision and a mission to combine tradition with modernity, and to bring together China and the West. CUHK teachers and students hail from all around the world. Four Nobel laureates are associated with the university, and it is the only tertiary institution in Hong Kong with recipients of the Nobel Prize, Turing Award, Fields Medal and Veblen Prize sitting as faculty in residence. CUHK graduates are connected worldwide through an extensive alumni network. CUHK undertakes a wide range of research programmes in many subject areas, and strives to provide scope for all academic staff to undertake consultancy and collaborative projects with industry.