Systems and methods for converting discrete wavelets to tensor fields and using neural networks to process tensor fields

A system for training a deep field network to detect anomalies within a database comprising discrete wavelets, the system comprising:

• one or more memories storing instructions;
• and
• one or more processors configured to execute the instructions to:
• receive a plurality of transactions,
• convert each transaction to a corresponding wavelet,
• group the plurality of wavelets and corresponding tensors by coefficients included in the wavelets,
• train a neural network for each group independently of other groups, and
• integrate the neural networks into a deep field network.

A system for authorizing a transaction using cascading discrete wavelets, the system comprising:

• one or more memories storing instructions;
• and
• one or more processors configured to execute the instructions to:
• receive a new transaction,
• convert the new transaction to a wavelet,
• convert the wavelet to a tensor using an exponential smoothing average,
• calculate a difference field between the tensor and a field having one or more previous transactions represented as tensors,
• perform a weighted summation of the difference field to produce a difference vector,
• apply one or more models to the difference vector to determine a likelihood of the transaction being high risk, and
• authorize the new transaction when the likelihood is below a threshold.