AI Case Study

Monzo decreased pre-paid card fraud to 0.1% and false positive rate to 25% using machine learning

Monzo's machine learning system predicts which online banking and card transactions are potentially fraudulent. Upon detection, extra security is required to verify user identity. This has led to a decrease in pre-paid card fraud to 0.1% and false positive rate to 25%.

Industry

Financial Services

Banking

Project Overview

"When a Monzo card is topped up with money from another debit card, Monzo is the acquiring merchant for that transaction. We don’t want to require all of our users to go through 3D Secure every time they want to top up because it’s a poor experience. Instead, our fraud engine makes a decision based on how risky it thinks a particular top up is and only puts a small percentage of top ups through 3D Secure."

Reported Results

Reduced "the fraud rate on its pre-paid cards down from 0.85% in June 2016 to less than 0.1% by January 2017." Monzo's fraud rate is "lower than the financial services industry average". Decreased false positives for fraud from over 60% to 25%.

Technology

In-house; "a combination of rules and machine learning based fraud systems. Our fraud prediction model is built using Google’s Tensorflow library and analyses a large number of metrics including links between users and behavioural patterns."

Function

Risk

Audit

Background

Monzo is an online banking app and prepaid card provider. "A criminal can purchase stolen card details online, top up a Monzo card, and then spend that money that originally came from a stolen card in a shop or withdraw cash from an ATM. We will then later receive a chargeback from the real cardholder and are therefore out of pocket for that amount. Prepaid card schemes are especially attractive to criminals for this reason — it allows them to convert stolen card details into a physical card very easily."

Benefits

Data