The Business Issue

The client developed and implemented a data modeling approach for fraud detection and wanted to understand how other organizations employed modeled algorithms to drive improvement in fraud detection systems.

The Solution

10EQS conducted a rapid, iterative evaluation of cutting-edge machine learning techniques to ascertain technical safeguards, prevent fraud and evaluated key algorithms that produce the highest level of accuracy in detecting fraud.

The Result

10EQS provided detailed approaches to build appropriate machine learning models that best detected fraud. The methodology included: Subject matter review & data cleansing, supervised machine learning, and unsupervised / semi-supervised machine learning as supplementary approach.

Client

Global Bank

Project Type

Benchmarking & Technology Scan

Project Team

1 Collaboration Manager
10 Industry Experts
10EQS Delivery Operations

Time Frame

6 weeks