telecom devices

The Challenge

  • The customer, a UK based telecom major accepted orders (mobile handsets, Wi-Fi routers, sim cards, switches etc.) from multiple channels, namely website, app, call, sms etc.
  • Fraudsters often hack into customer accounts through various means and place orders on their behalf
  • When there is a bill shock at the end of the month, the customers refuse to pay for these orders which becomes a lost for the telecom service provider
  • There is a team that randomly takes a few samples from the list of all orders placed and check for possibility of fraud, but due to their bandwidth limitation they are able to go through only 1-1.5% of all the orders placed per day
  • Out of the orders reviewed by the team, the historic success rate (i.e. fraud identification rate is close to 24%, which means 76% of the times their efforts yield no result
  • There are a number of fraudulent orders that get through due to this reason (i.e. insufficient bandwidth from the team) and the customer was looking for a solution to improve the number of frauds identified per day


  • To improve throughput rate of the fraud identification team from existing 24% to at least 50% +
  • To identify more frauds per day which would improve the customer experience
  • To use data analytics to replace rule-based approach of selecting orders to be reviewed by the fraud classification team


  • The data that was made available contained information around customer demography, usage behavior, service subscription, credit rating, historical fraudulent orders identified, Ip information.
  • The team ensured complete masking of any individually identifiable information before working with the data

The Solution

  • The team changed the process to the following – all the orders placed would pass through a machine learning algorithm which would identify the propensity of any order to be fraudulent
  • The most likely orders to be fraud would then be passed on to the fraud identification team for a round of manual review
  • The team would follow the SOP to determine whether these orders are actually fraudulent
  • The fraudulent orders would then be cancelled/ appropriately dealt with while the legitimate orders would be let through
  • The optimum model (after multiple rounds of improvement) was found to be an ensemble of logistic regression, decision tree, xgboost, random forest and svm
  • The model was hosted as a pl file in a server while the input data was fed as a Json
  • The output from the model was shared back to the fraud identification team as another Json
  • The team would refer to the hourly fraud reports from the model and review the same manually to identify the actual fraudulent orders


  • Customer satisfaction score/ customer experience
  • Throughput rate for the fraud identification team
  • No. of frauds identified per day
  • Revenue leakage reduced
  • Human effort reduced

The Benefits

  • Improvement of throughput rate to 76% from 24%
  • 200+ additional fraudulent cases identified per week, resulting in annual cost avoidance to the tunes of a minimum of GBP 450,000
  • Improvement in NPS score of more than 60 basis points