  Digital payment volume has proliferated in recent years with the rapid growth
of small businesses and online shops. When processing these digital
transactions, recognizing each merchant's real identity (i.e., business type)
is vital to ensure the integrity of payment processing systems. Conventionally,
this problem is formulated as a time series classification problem solely using
the merchant transaction history. However, with the large scale of the data,
and changing behaviors of merchants and consumers over time, it is extremely
challenging to achieve satisfying performance from off-the-shelf classification
methods. In this work, we approach this problem from a multi-modal learning
perspective, where we use not only the merchant time series data but also the
information of merchant-merchant relationship (i.e., affinity) to verify the
self-reported business type (i.e., merchant category) of a given merchant.
Specifically, we design two individual encoders, where one is responsible for
encoding temporal information and the other is responsible for affinity
information, and a mechanism to fuse the outputs of the two encoders to
accomplish the identification task. Our experiments on real-world credit card
transaction data between 71,668 merchants and 433,772,755 customers have
demonstrated the effectiveness and efficiency of the proposed model.

Machine Learning;Artificial Intelligence;Information Retrieval