AI Case Study

UK Train Operating Companies automates 93% of claim processing and detects fraudulent payback claims for trains delayed by more than thirty minutes with 70% accuracy using machine learning

UK train operating companies have to pay back customers if the trains are delayed by more than 30 minutes and would receive tens of thousands of claims. But they were not able to verify if the person actually took the train. A machine learning algorithm would classify claims into "clear to pay" and "hold for investigation" based on monthly history of claims for each route speeding up processing for majority of customers.

Industry

Transportation

Other

Project Overview

In 2017, UK train operating companies were expected to pay back customer claims for trains delayed by more than 30 minutes with tens of thousands per month). But with no way of truly knowing which person was on what train. The AI engine was looking for fraud based on historic one month of claims for a given route and placed claims into 10 “risk of fraud” bands (1 being lowest risk and 10 highest risk). It would then be possible to set a threshold of which bands should be: “clear to repay” and which should be “hold for investigation”.

Reported Results

93% of all claims could be marked as “clear to pay” leading to faster payment
Around 70% accuracy in identifying fraudulent claims

Technology

"When fraudsters attempt to industrialise their activity to make more money, they re-use parts of identities or leave other traces to indicate the frauds are related. They also repeat behaviour, that leaves evidence of homogeneous behaviour across a network, indicative of a “controlling criminal mind”. By replicating your best investigators’ approach across networked data, the AI will not only alert far more accurately, but will also provide the investigator team with a picture and explanation of the fraud."

Function

Finance

Accounts Payable And Receivable

Background

Out of 1000s train delay claims, a good percentage are fraudulent.

Benefits

Cost - Fraud reduction

Data

"Shared identities, addresses, bank accounts, email addresses, web channel data, time correlated trips, relationships, transactions, providers, geographies, etc."