AI for Fraud Prevention - Know What The Machine Knows
New fraud techniques emerge every day from increasingly adaptive cybergangs and criminals. Card-not-present fraud will cost retailers worldwide $130 billion between 2018 and 2023, a new report from Juniper Research predicts.
In order to stay ahead of the curve in combatting fraud, you need to be able to understand “why” a transaction is being predicted as fraudulent, if it represents an emerging trend, and adjust business rules to prevent new patterns of fraud from taking hold.
Similarity Based Explainable AI (XAI) is an Analytic Workbench for Fraud
Trust and transparency are vital in preventing fraud, adjusting business practices to combat new emerging fraud techniques and meeting increasing compliance requirements.
simMachines Provides Unparalleled Transparency Into AI Predictions
High precision with local prediction transparency
Single pass prediction and clustering function
Easy to use, with advanced capabilities for Data Scientists
Dynamically weighted factors by prediction
Used controlled cluster granularity
Predictive factors easily integrate into decision systems
Fraud Prevention Today Requires Detection and Understanding of Defining Factors with Constant Monitoring Through Automated Insights
Machine Learning for Fraud Prevention
Fraud and risk managers today need machine learning applications that provide full explainablility for each and every prediction, to understand fraud causes and their defining factors, enable investigation and review, detect new emerging patterns and constantly monitor changes and differences in fraud methods. simMachines XAI Analytic Workbench for fraud leverages proprietary algorithms to provide precise predictions and automated insights as speed and scale to accomplish these goals.
Easy to Use GUI with Advanced Features
Machine Driven Explainable Fraud Predictions
Dynamic Predictive Clustering for Fraud Insights
Anomalous Pattern Detection, Trending & Analysis
Flexible Deployment Options Including Restful APIs
Access to Data Scientists and Expert Customer Support
“Dynamic predictive segmentation (DPS) is the future. Marketers require analytics-driven tools and it’s no surprise that dynamic predictive segmentation is the fastest growing segmentation method. Perceived benefits of adopting DPS include discovery of new opportunities, ability to react more quickly to competitors, increased customer engagement, and improved customer experience.”
Capture The Customer Moment With Dynamic Predictive Segmentation, a January 2018 commissioned study conducted by Forrester Consulting on behalf of simMachines.
simMachines supports use cases around fraud detection and prevention, chargeback reduction, risk modeling, and 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.
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.
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.
Client needed to reduce charge backs in order to avoid triggering monthly order caps based on charge back volume.
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.
Digital Identity Resolution
Large scale DMP needed to improve linkages across devices associated with single users.