  Of late, in order to have better acceptability among various domain,
researchers have argued that machine intelligence algorithms must be able to
provide explanations that humans can understand causally. This aspect, also
known as causability, achieves a specific level of human-level explainability.
A specific class of algorithms known as counterfactuals may be able to provide
causability. In statistics, causality has been studied and applied for many
years, but not in great detail in artificial intelligence (AI). In a
first-of-its-kind study, we employed the principles of causal inference to
provide explainability for solving the analytical customer relationship
management (ACRM) problems. In the context of banking and insurance, current
research on interpretability tries to address causality-related questions like
why did this model make such decisions, and was the model's choice influenced
by a particular factor? We propose a solution in the form of an intervention,
wherein the effect of changing the distribution of features of ACRM datasets is
studied on the target feature. Subsequently, a set of counterfactuals is also
obtained that may be furnished to any customer who demands an explanation of
the decision taken by the bank/insurance company. Except for the credit card
churn prediction dataset, good quality counterfactuals were generated for the
loan default, insurance fraud detection, and credit card fraud detection
datasets, where changes in no more than three features are observed.
