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Protect all cashless payments from fraud

Outsmart fraudsters with generative AI IBM® Safer Payments helps you create custom, user-friendly decision models so you can adapt to emerging threats faster and detect fraud with greater speed and accuracy, all without vendor or data scientist dependencies. IBM Safer Payments significantly accelerates modeling optimization by providing the analytics and simulation tools needed to continuously monitor business performance and adapt to emerging and modified fraud patterns.
Benefits Delivers high performance results

Clients report high detection at ultra-low false-positive rates after deploying our solution.

Brings agility to fraud management

Build, test, validate and deploy machine-learning models in days versus months without reliance on vendors.

Proven in production

Monitor thousands of payments per second. Enterprise-grade solution delivers 99.999% availability and high throughput.

Open source data science

With an open platform, import detection models, model components and IP while using a rich interface to build new models.

Join the AI revolution IBM Safer Payments recommends the best decision models for your data and the threats you encounter most, while continuously monitoring business performance to adapt to emerging and modified fraud patterns. Next generation approach

Enables you to use any data science, machine learning or artificial intelligence technique –from any vendor or open source –to outsmart fraudsters.

All channel solution

Includes specific functionality to protect cards, online banking, and real-time payments. Profiles behavior and payment flow through all channels for lower false positives and reduced fraud losses.

Freely configurable

Everything in IBM Safer Payments is easily configurable. Full adaptation to customer data model. New data feeds can be configured in minutes.

Bring your own model

Build neural networks, random forests, decision trees, and regressions, using your preferred tools. Combine models of different types into ensembles, to leverage the best of each modeling technology.

In-memory NoSQL database

Purpose-built dual-access database technology delivers peak performances of multiple thousand transactions per second that runs on commodity hardware and any cloud environment.

How it's used

Real-time payments fraud prevention Criminals structure payments and "smurf" them through mule accounts to disguise the flow of money. IBM Safer Payments profiles each payment originator and beneficiary as both sender and recipient of payments in the past.

Model factory To enable the combination of the best data science techniques, we created the "model factory" concept. It allows IBM Safer Payments users to pick the combination of techniques best suited to solve their fraud problem.

Online and mobile banking IBM Safer Payments uses data from actual online and mobile banking sessions. Built-in device ID capability, with device behavior profiling and a device reputation database and biometric parameter recognition hardens this channel.

Utilize open source data science IBM Safer Payments is the most open data science platform for payment fraud prevention currently available in the market. Export and import models in portable PMML format and ingest models or feature extractions as Python code.

Multitenancy and multichannel IBM Safer Payments provides hierarchical multitenancy. It efficiently manages hundreds of tenants, each customized. Inheritance allows maintenance of central models and configurations.

Featured resources IBM Safer Payments solutions brief

Learn how fraudsters are outwitting obsolete fraud prevention systems, stealing more from payment channels and eroding confidence. It’s time for a better approach.

Preventing fraud across all payment channels

Discover the industry’s first true cognitive fraud prevention solution to payment processing that protects some of the most complex payment portfolios in the world.

Technical background for IBM Safer Payments

Learn more about IBM Safer Payments’ architecture, technology and scaling considerations.

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