  Timber and forest products made from wood, like furniture, are valuable
commodities, and like the global trade of many highly-valued natural resources,
face challenges of corruption, fraud, and illegal harvesting. These grey and
black market activities in the wood and forest products sector are not limited
to the countries where the wood was harvested, but extend throughout the global
supply chain and have been tied to illicit financial flows, like trade-based
money laundering, document fraud, species mislabeling, and other illegal
activities. The task of finding such fraudulent activities using trade data, in
the absence of ground truth, can be modelled as an unsupervised anomaly
detection problem. However existing approaches suffer from certain shortcomings
in their applicability towards large scale trade data. Trade data is
heterogeneous, with both categorical and numerical attributes in a tabular
format. The overall challenge lies in the complexity, volume and velocity of
data, with large number of entities and lack of ground truth labels. To
mitigate these, we propose a novel unsupervised anomaly detection --
Contrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is
generally applicable for large-scale heterogeneous tabular data. We demonstrate
our model CHAD performs favorably against multiple comparable baselines for
public benchmark datasets, and outperforms them in the case of trade data. More
importantly we demonstrate our approach reduces assumptions and efforts
required hyperparameter tuning, which is a key challenging aspect in an
unsupervised training paradigm. Specifically, our overarching objective
pertains to detecting suspicious timber shipments and patterns using Bill of
Lading trade record data. Detecting anomalous transactions in shipment records
can enable further investigation by government agencies and supply chain
constituents.
