Sold by: MphasisÂ
Deployed on AWS
This solution analyses a corpus of text to predict whether a person is a potential lead for home loan.
Overview
Bank home loan lead identifier model uses Natural Language Processing to identify potential leads for Home loan. This model takes text input like individual tweets, comments or any other text expression to predict a potential lead.
Highlights
- This solution can be utilized by banks for identifying leads and overall market demand for home loans.
- The model can take a maximum of ( ~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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge 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
- This solution 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)
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/HyperGraf-Home-Loan-Lead-Identifier/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/HyperGraf-Home-Loan-Lead-Identifier/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
TEXT | This is the only required input feature, it contains any text data which could be in the form of a tweets, comments or any other text expression | Type: FreeText | Yes |
Resources
Vendor resources
Support
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Customer reviews
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1 external reviews
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Tiwari S.
HyperGraf Home Loan Lead Review
Reviewed on Oct 12, 2025
Review provided by G2
What do you like best about the product?
What stands out most to me about HyperGraf Home Loan Lead Identifier is its impressive AI-driven accuracy in pinpointing high-quality leads. By evaluating a range of data points such as financial behavior, credit scores, and customer intent, it provides actionable insights that make it easier to focus on the most promising prospects. The platform’s real-time analytics dashboard, along with its smooth integration with CRM systems, greatly streamlines lead management. I also value how it minimizes manual work, increases conversion rates, and ensures that sales teams dedicate their efforts to leads with the highest likelihood of converting.
What do you dislike about the product?
What I find challenging about HyperGraf Home Loan Lead Identifier is that, despite its robust capabilities, the setup and customization process can be quite complex, especially when tailoring it to unique business workflows. Integrating data for the first time often necessitates technical assistance, particularly if you need to connect with older CRM or loan management systems. I have also noticed that the real-time data refresh rates could be improved, which is especially important for organizations managing a high volume of leads. Enhancing the user interface and providing more comprehensive onboarding documentation would help make the tool more approachable for teams without technical backgrounds.
What problems is the product solving and how is that benefiting you?
HyperGraf Home Loan Lead Identifier addresses the challenge of pinpointing, qualifying, and prioritizing genuine home loan leads from a large pool of potential customers. In the past, significant time and resources were spent pursuing leads that were either unqualified or had low intent. Now, thanks to its advanced AI and predictive analytics, the platform offers a clear understanding of customer intent and creditworthiness, enabling sales teams to concentrate on the most promising prospects. As a result, conversion rates improve, acquisition costs decrease, and loan approvals happen more quickly. Additionally, the insights provided allow for more personalized customer engagement, which enhances overall satisfaction and drives revenue growth through more effective targeting.