TapBase is releasing TapPay and TapWallet mobile applications for the iPhone as elements of its Tap mobile payments, loyalty and rewards platform (view press release). The TapPay app enables merchants to use the barcode scanner to manage a mobile database of products and services. Items can be added to a mobile shopping cart where discounts, charges or tips can be applied. Credit or debit cards can be swiped using a magnetic-stripe reader, or a bill can be sent to a customer’s smartphone. The TapWallet app allows consumers to carry payment card information, loyalty and membership cards. To make a payment, the user selects a payment card, signs the transaction on their phone and authorises payment. The merchant is notified that the transaction is complete, and is given a copy of the receipt.
TapBase plans to offer Android versions of the apps and further components of its platform are planned for Q4 including the option for merchants to issue their own virtual pre-paid and gift cards using QR and NFC technology. In addition, merchants will be able to offer rewards to customers in the form of TapPoints, a Tap Platform virtual currency.
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more