Sold by: MphasisÂ
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Lexicon based NLP solution analyses transcripts to identify consumer financial difficulty, potential vulnerability and forgetfulness.
Overview
DeepInsights is a cloud-based cognitive computing platform that offers data extraction and predictive analytics capabilities. Consumers of essential services like financial services need to be handled with care, as otherwise they may face potential harm. The consumer First 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
- There can be multiple sources of vulnerability for consumers like disability, long term sickness to self or dependents, or psychological issues like depression or memory loss etc, which make it very difficult for consumers to fulfill their financial obligations, and uncaring treatment by financial services providers has the potential to exacerbate financial difficulty and harm for such consumers. Relief can be provided to such consumers in the form of hardship/special payment plans that give breathing space to consumers to come to terms with their due payments.
- FCA makes improving outcomes for such consumers through empathetic and caring treatment and provision of appropriate solutions a core and high priority part of its mission. Other markets like the U.S. and the European Union also prioritize caring treatment for such vulnerable consumers and specify how financial difficulty is to be handled through hardship plans, and access to advisory or counselling services to minimize harm to consumers.
- Apart from vulnerability, consumers may also not fulfil financial dues due to difficulty like job loss, or change in financial circumstances, which do not lead to potential harm, but in the short term limit the ability of consumers to fulfil obligations. Such consumers can be helped by adjusting payment schedules and providing suitable minimum payment plans so that they do not incur additional fees. Consumers may also end up facing charges and additional interest simply because they forgot to pay the dues on time.
Details
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Delivery method
Latest version
Deployed on AWS
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Try this product free for 60 days according to the free trial terms set by the vendor.
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 | $0.50 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.50 |
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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
It is the first version of the algorithm. It requires well transcribed consumer utterances as input. Currently the solution doesn’t support speaker classification into consumer and agent.
Additional details
Inputs
- Summary
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”.
General instructions for consuming the service on Sagemaker:
- Access to AWS SageMaker and the model package.
- An S3 bucket to specify input/output.
- Role for AWS SageMaker to access input/output from S3.
Sample Notebook : https://tinyurl.com/y6pmhkt2Â Sample Input : https://tinyurl.com/y6r76tugÂ
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
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