Recommendo

A Recommendation Engine cum Data Insight Tool

We Aim To Build

  • A tool which analyses customers' behavioural patterns example spending, transaction history, credit score etc.
  • It will recommend optimal plans based on the pattern.
  • And provides the bank an understanding about:
    • How a package is performing ?
    • How it can be improved ?

 

 

Methodology

  • Create a correlation between user patterns and bank’s policies.
  • Classify new user behaviour into existing behavioural patterns.
  • When a new cluster of usage pattern comes up as new data is generated, let the bank understand this new demographic. ​

Attributes that would feed the recommendation engines (​prototype​):

  • Number of products held by customer
  • Number of pre-authorized automatic payments
  • Credit score
  • Customer opening date (to derive years as customer)
  • Location information (country, region, state, district, county, zip code, etc.)
  • Customer branch associated (where the relationship is held/managed)
  • Customer income per annum (it can be an amount in INR or an income band such as INR (1,00,000 - INR 10,00,000)
  • Customer total asset value
  • Customer group (e.g. bank employee, student, newcomer)
  • Customer type (Individual or Organisation/SME Small and Medium Enterprise)
  • Customer segment (e.g. Preferred; Platinum/Gold/Silver)
  • Transaction history:
    • Merchant category
    • Location

Components/Design of the prototype:

  • Engine that stores and processes user data.
  • REST API for serving results of the engine.
  • User profile page (populated with suggestions, plans etc.)
  • Admin panel

 USE CASE: TAG BSES

  • total customer - 10000
  • users 1000
  • target 90000
  • person a
  • spends 10000 a month
  • given offer
  • spends 20000 a month
  • plan efficiency 100%

Product Design

Tech Stack

  • Python (NumPy For GaussianNB classification)
  • MongoDB
  • Flask (RESTful API)

 

Zafin Tech

By Anudit Verma

Zafin Tech

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