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.

Learn more about XAI

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

Review & Investigation

Manual review prior to order fulfillment for e-commerce transactions as well as audit checks and investigative analysis demands transparency to understand causes and factors behind illicit activity

Explainable Detection

An expanding range of fraud techniques driven by more sophisticated methods and increasing vulnerabilities requires explainable fraud detection technologies.

Patterns & Trends

New fraud techniques can emerge quickly and scale fast, taxing the most sophisticated detection systems that now must constantly monitor for new patterns to enable preventative action

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

Easy to manage user workflow and interface enables use of basic or advanced features to build, validate, test and deploy models in hours.

Machine Driven Explainable Fraud Predictions

Similarity combined with metric distance functions provide state of the art precision with full explainability at a local level including nearest neighbor objects and multiple drill down capabilities.

Dynamic Predictive Clustering for Fraud Insights

Individual fraud predictions are automatically clustered into dynamic predictive segments that reveal weighted features in order of importance for fast, deep and rich analysis. Export as needed to preferred BI tools.

Anomalous Pattern Detection, Trending & Analysis

Similarity provides the ideal tool for identifying anomalous behavior. Similarity’s inherent explainability enables investigation, understanding, analysis and communication of unusual activity.

Flexible Deployment Options Including Restful APIs

Software can be supported in on-premise, private cloud or hosted cloud environments for production or development applications.

Access to Data Scientists and Expert Customer Support

Tier II support from our expert data scientist and client support resources ensure you have quick and easy access to the resources you may need to solve problems and innovate solutions.

“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.

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Use cases

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.

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Chargeback Prevention
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.

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