Sold by: MphasisÂ
Deployed on AWS
This solution analyses a corpus of text to predict whether a person is a potential lead for home, automobile or education loan.
Overview
Bank loan lead identifier model uses Natural Language Processing to identify potential leads for different types of loan portfolios. This model takes text input like individual tweets, comments or any other text expression to predict a potential lead for education, home or automobile loan.
Highlights
- This solution can be utilized by banks, automobile industry or real state companies for identifying leads and overall market demand for home, automobile and education loans.
- The model can take a maximum of 70kb ( ~1000 rows of text ) as input and identify rows of text data with a Lead potential.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Amazon SageMaker
Input
• Supported content types: text/csv • Sample input file:
Text confused whether to take loan for car or not? Can anyone help •This model works with any text data which could be in the form of a tweets, comments or any other text expression •The column containing the text data must be given the heading as “Text” •Maxium input file size : 70kb ( ~1000 rows of text)
Output
• Content type: text/csv • Output file will contain the original text along with loan potential score for each text - (Lead or Not Lead) • Output file also shows that given text belongs to which category - (Education, Automobile or Home) • Sample output file: (https://tinyurl.com/r7qadsf )
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://Lead_identifier_sample_data.csv --content-type text/csv --accept application/Lead_identifier_sample_output.csvSubstitute the following parameters:
- "endpoint-name" - name of the inference endpoint where the model is deployed
- Lead_identifier_sample_data.csv - input csv file to do the inference on
- text/csv - type of the given input text file
- Lead_identifier_sample_output.csv - filename where the inference results are written to
Resources
• [Lead identifier sample data] (https://tinyurl.com/rw6gn9d ) • [Sample Notebook] (https://tinyurl.com/tmll4aj )
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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The Mphasis AI for Industry Verticals service enables enterprises to build deeply customized AI solutions to address their industry specific needs. We leverage our patented AI/ML platforms and frameworks to engage with clients through assessment and implementation offerings across industry verticals such as retail, banking, insurance, hospitality, telecom, aviation, e-commerce, healthcare, etc. We help enterprises target impactful AI/ML interventions that can drive business benefits. Our Assessments, Workshops and Implementations identify the most relevant use cases and outline the potential benefits such as efficiency, cost, customer experience etc.
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