American Express Travel Related Services Company, Inc.

Di W Xu

Kathleen Haggerty

Chao M Yuan

Prasanta Sahu

Aashish Prakash

Tirthankar Choudhuri

Amber Gupta

Anjali Dewan

Jeffrey A Chwast

Jing Wang

Shourya Roy

Himanshu Sharad Bhatt

Gabriella P Fitzgerald

Chetan Lohani

Shachindra Pandey

Lokesh Bhatnagar

Dewan Anjali

Manoj Bhokardole

Gunjan Panwar

Vinit Jain

A system provides an end-to-end solution for invoice processing which includes reading files (such as pdfs and images), extracting key relevant information from the files, organizing the relevant information in a structured template as a key-value pair, and comparing files based on the similarities between different file fields to identify potential duplicate files.

Data extraction and duplicate detection

A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer's spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.

Consumer household spend capacity

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Total structural risk model

The present disclosure generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present disclosure relates to rating consumer lenders based on the predicted spend capacity of their consumers.

Consumer behaviors at lender level

Estimating the spend capacity of consumer households

Growing community of inventors

Edison, NJ, United States of America

Di W Xu