KLIs are an ideal input to our predictive models because they represent a holistic view of the account holder. However, KLIs also solve the most difficult and time consuming part of data science — cleansing and normalizing data.
Good data science and predictive modeling relies on cleansed and contextualized data. From describing the number of debit card swipes an account holder makes to distilling tens of millions of Amazon transaction variants into a single data tag, KLIs provide highly normalized and contextualized data labels for your customer data.
KLIs are the key ingredient for highly useful and accurate predictive modeling. We analyze account holder’s everyday purchase transactions and their utilization of banking products to assign Segmint’s proprietary data tags, KLIs. These data tags are the ideal input.