Quantitative Analytics

Helping businesses use their own data to better understand their customers

Quantitative Analytics is a powerful tool that enables businesses to utilize their own data to extract valuable insights about their customers by analyzing actual behavioral patterns; expressed through customer interactions with the business over a period of time. BFA provides such analysis to our providers to assist them in their product development, go-to-market strategy and distribution channels.

We leverage a suite of proprietary analytical modules that combine traditional statistical analysis, machine learning and data visualization that is capable of handling large volumes of unstructured financial data.

 

Quantitative Analytics is highly specialized for the following purposes:

 

  • We mine billions of transactions pertaining to millions of customers over years using applied statistics, segmentation, clustering, machine learning, natural language processing etc.  Data sources include banks, other financial institutions, utilities, digital financial services providers, and even informal savings and credit groups. This allows us to precisely identify customer preferences based on their actual usage behavior.
  • We utilize various statistical and machine learning-based modeling techniques to make predictions based on descriptive and historical data.  This includes credit scoring, propensity for cross-sell, future portfolio performance and the financial impact of business decisions to name a few.
  • We triangulate evidence with other practice areas in BFA to obtain a holistic image of a customer or a provider.  For example, behavioral pattern discovery informs Customer Insights about preferred customer behaviors, while transactional segmentation allows the Business Insights team to ascertain the financial performance of products, channels, and customer segments.
Updated January 2017