SENTIENT Case Study

Application of ML in preventing credit card fraud detection

Customer

An online payment network

Industry

Banking & Finance

Function

Fraud Detection

Problem

Fraud losses on credit, debit, and prepaid cards issued worldwide totaled to $21.84 billion in 2015. By 2020, Bloomberg reports, they have grown by 45%. With online payments becoming more and more frictionless, banks are constantly struggling to balance the demands of convenience and ease-of-payments with the need for better security to prevent fraud.

Solution

The company adopted a ML-driven software solution which establishes a customer’s digital identity by collecting more than 50 data points about a potential customer such as location, email, phone, passport number and technology devices used. This digital model of the customer keeps evolving through the ML algorithms that learn from the customer’s recent online activities such as social media, payment behavior, device activity, geolocation, billing address etc.
The company used this solution to automate its KYC (Know Your Customer), anti money laundering and fraud prevention activities. The KYC profile is first filled out with the information provided by the potential client. The software then gets activated to search third party organization and online information associated with that individual to cross-verify the information. If this leads to any suspicions of the potential customer having used another person’s data, the system will red-flag the applicant.

Result

Since using the application, the company has achieved ‘hockey-stick’ growth, gone public with $2 billion in client assets and approved new customers from more than 150 countries.

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2263, KAUST Tech Park Headquarters, 

4700 KAUST, Thuwal, Saudi Arabia 22935

Contact

Call : +966 54 312 3734

Email : info@unitx.tech

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