Sold by: MphasisÂ
Deployed on AWS
This solution identifies finance related tables and extracts information from financial statements.
Overview
Financial Spreading Information extraction solution detects finance related tables like balance sheet, cash flow, income statement etc. and extracts the content as key value pairs. The solution uses text matching, NLP and Machine Learning to localize and identify financial tables and extract information. This will help banks, and other financial institutions to rapidly analyze borrowers'/clients' financial standings and associated risks.
Highlights
- Automate financial spreading information extraction from financial statements. This reduces manual effort and improves the productivity of financial analysts, insurance brokers, data entry operators etc.
- This solution uses key word matching, NLP and Machine Learning to accurately identify tables in digital documents. It can recognize assets/liabilities, income sources and expense line items. The output will contain list of pages with content, financial periods and key metrics mapped to respective financial terms.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $10.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
this is version 3
Additional details
Inputs
- Summary
Usage Methodology for the algorithm:
- The input must be 'Input.zip' file.
- The zip file should contain Input file which has a .pdf file.
- Name of the folder inside the zip file should be “Input” which is case-sensitive
- check the instructions and sample endpoint in the sample jupyter file provided.
- Limitations for input type
- only one pdf in a zip file
- Input MIME type
- text/csv, application/json, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Financial-Spreading-Info-Extraction/blob/main/Input/Input.zip
https://github.com/Mphasis-ML-Marketplace/Financial-Spreading-Info-Extraction/blob/main/Input/Input.zip
Resources
Vendor resources
Support
Vendor support
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