Advanced Analytics Data Factory

Factory approach to analytics where appropriate data, algorithms, compute power and analytical tools are available on a flexible mode

With banks today being extremely customer focused, the need for knowing customers intimately, analyzing costs with surgical precision and evaluating risk scenarios has become paramount. However, analyzing the right data at the right time from the right source in the right measure continues to be a key challenge. Meeting this sub optimally would mean risking the business through wrongly premised decisions.

The need of the hour is a factory approach to analytics where appropriate data, algorithms, compute power and  analytical tools are available on a flexible mode, resulting in models that have been devised , tested and validated on smaller samples prior to being rolled out as a service.

Our Approach

Our approach focuses on starting small, be it shortlisting one function like AML, Fraud, LTCV, share of wallet etc., or identifying an area based on stakeholder preference. Subsequently we do the following.

  • Evolve program objectives and detail business needs and initial focus areas
  • Build a data store – physical or virtual, to address business needs. This involves data analysis from multiple versions of truth as well as discovery of embedded relationships, rules and logic
  • Develop PoCs aimed at fine tuning stakeholder expectation. These may range from using advanced algorithms to estimate risk scenarios, to portals for on demand visualizations
  • Design of Experiments (DoE) to evaluate outcomes with smaller business segments
  • Based on the DoE results, scale out the solution as DIU portals, recommendation engines and automated reports as appropriate
  • Measure the outcome and finalize the matured approach leading to institutionalization across the organization and scale the approach to include other focus areas

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