Sold by: MphasisÂ
Deployed on AWS
Lexicon based NLP solution which analyses customer conversations to identify financial difficulty, vulnerability and forgetfulness.
Overview
The Behavioral AI solution utilizes transcripts of collections-based conversations between contact center agents and consumers to identify and flag financial difficulty, forgetfulness and potential vulnerability, enabling firms to treat consumers with care and meet regulatory requirements.
Highlights
- The solution improves consumer financial outcomes by using NLP to identify when a customer interaction requires extra empathy, care and support, and when other measures may be more suitable. It classifies three conditions for non-payment of credit card dues, namely potential vulnerability, financial difficulty, and forgetfulness. We provide scores for all three measures through a lexicon based analysis of contact center conversation transcripts.
- The solution can be utilized to check agent compliance with guidelines and identify shortcomings which can then be addressed to make agent-consumer interactions more effective. The solution can also be utilized as a training tool to showcase these conditions to novice agents and help them understand concepts of care-oriented consumer interactions.
- 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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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.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large 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
Input:
Following are the mandatory inputs for predictions made by the algorithm:
- The algorithm works with consumer utterances made in conversation with collection contact center agents.
- The input can be provided as a text file (.txt), with agent and customer utterances being provided as separate rows.
- The solution currently doesn’t distinguish between agent and customer so the utterances in the transcript need to be tagged properly as “Agent” or “Customer”.
- Supported content types: 'text/plain'.
Output:
Instructions for score interpretation:
- Higher the score, more serious the condition, so higher the vulnerability score, more serious the vulnerability.
- Score 0 - 0.75 = to be considered as not significant, may be ignored
- Score 0.75 - 2 = Should be interpreted as significant, should lead to probing for details
- Score 2 and above = Should be interpreted as serious, should lead to suggestions of remedy
- Supported content types: 'application/json'.
Invoking endpoint:
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://input.txt --content-type text/plain --accept application/json out.json
Resources:
Illustration:
If we get Financial Difficulty score = 1.5, Vulnerability score = 2.6, Forgetfulness score = 0.4. Then, it means serious vulnerability with significant financial difficulties, and should be considered for hardship plans, forgetfulness may be ignored.
Disclaimer: The personal details like names, dates of birth, account number, addresses in the sample transcript provided are fictitious, and any resemblance to actual persons or their details is purely coincidental and unintentional.
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
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AWS infrastructure support
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