Fraud Prevention

Machine Learning Use Cases in Fraud Prevention

simMachines supports use cases around fraud detection and prevention, charge back reduction, risk modeling and other related applications.

XAI Fraud Platform Automation

Large financial services firm uses a rule-based system for fraud detection globally and wanted to further automate and scale the process using machine learning.

The client’s rule-based system provides explainability but requires manual maintenance that makes changes and updates problematic.  Explainability is critical to managing fraud detection as part of their core platform.

simMachines XAI analytic workbench provides automation and continuous learning to update and manage fraud detection capabilities with less manual labor.

Automation with precision was achieved, enabling the client to maintain explainability, improve results and reduce time and cost associated with managing fraud detection algorithms.

E-Commerce Fraud Prevention

Top 3 financial institution wanted to speed up ability to implement ML fraud detection solutions for e-commerce clients, enable continuous learning, and expose the factors driving the fraud.

ML methods did not continuously learn or expose the Why factors. Increased competition was creating a need to upgrade speed and service value to client.

Ensemble approach using gradient boosting and similarity-based machine learning.

70% increase over current fraud detection performance. Time to implement reduced from 6 weeks to 2 weeks.  Continuous learning enabled and the Why factors reported/available behind every prediction.

Rewards Program Fraud Prevention

Large global retailer wanted to control its own fraud models for its rewards program, integrating with a 3rd party data platform.

The data platform provider’s model building tools could not extend sufficient access to its client’s data science team.

simMachines branded its XAI Analytic Workbench as an extension to the partner’s data platform to enable client access for model building.

After 1/2 day training, a single non-data scientist client resource created 8 models in 8 weeks that all matched or outperformed open source ML being tested internally. Transparency is a core value to the client and the XAI Analytics Workbench allows them to understand and explain their models’ behavior and enables investigation and detection of emerging fraud patterns.

Chargeback Prevention

Client needed to reduce charge backs in order to avoid triggering monthly order caps based on charge back volume.

The current algorithms weren’t performing at the level required and lack of data science resources internally made it challenging to address effectively.

simMachines installed its XAI Analytic Workbench at the client and assisted in replacing current models with new charge back detection algorithms to stop predicted charge back order processing before it occurred, enabling more good orders to flow through.

The XAI Analytic Workbench algorithm reduced the charge back rate enough to drive an immediate 2%+ increase in incremental order volume.  simMachines trained an analyst at the client to use the software and develop new models for additional applications.

Digital Identity Resolution

Large scale DMP needed to improve linkages across devices associated with single users.

Client had millions of unconnected devices it needed to accurately connect for improving campaign reach and frequency objectives.

The solution developed used similarity search to analyze a high volume of records quickly to pair two or more devices together with a high degree of accuracy quickly.

The solution was able to comb through over a quintillion pairing combinations in 48 hours and pair over 800,000 out of 1 million device records together with ~95% accuracy compared to known ground truth.  Daily updates can be run on a growing volume of records in under 2 hours.

Credit Risk Modeling

Large financial services firm uses a regression models for managing credit risk modeling and needs explainable machine learning to automate the process.

Automation and performance lift with explainability made it difficult to find relevant machine learning alternatives.

simMachines XAI analytic workbench provides automation, explainability and achieves required performance levels against highly tuned regression models operating on an extensive data set.

Automation with precision that matched required performance levels was achieved, in addition to being user friendly for modeling teams in build and deployment scenarios.

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