Settlement API for Better Visibility into Daily Cash Movement
Today, we’re launching our Settlement API, a solution that provides you a clear view of cash movement and daily network activity, including data on interchange and network fees.
3DS Support for Better Fraud Management
Today, we are expanding our fraud offering with two new ways for Lithic card programs to participate in 3-D Secure (3DS) in beta.
Introducing Our Kotlin-based Lithic Client Library
We are excited to announce the development of our Kotlin-based client library, following up on our recent Java library release.
Disputes in Dashboard
Today, we’re excited to announce the launch of Disputes in the Lithic Dashboard. Lithic customers can now submit Disputes in the Lithic Dashboard in addition to via the API.
Fraud Detection 101
You know your fraudsters—the thieves, the con artists, the opportunists. You know how these bad actors steal and fabricate identities to take over accounts and unleash chaos. The question is how you can protect your organization and your customers from falling victim to fraud.
Fraud Detection 101 Q&A With Zach Pierce
This Q&A is part of the Fraud Fighters Manual, a collective set of stories from Fintech fraud fighters. Read Zach Pierce’s chapter on Fraud Detection 101 here, and download your copy of the Fraud Fighters Manual here to read the full version.
HMAC in ASA Headers for Enhanced Security
We've added HMAC headers to Authorization Stream Access (ASA) requests. This additional layer of security enables customers to verify the authenticity of information received via webhooks.
Lithic Launches Go Client Library for Easier Card Product Development
Lithic's Go client library is now available. It provides Go developers convenient access to the Lithic API so they can execute a wide range of common operations faster and more efficiently.
Network Risk Scores for Improved Fraud Monitoring and Prevention
Network Risk Scores is a tool that gives Lithic users access to the card network's advanced risk scoring solutions to determine the likelihood that a transaction is fraudulent in real-time.