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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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Mphasis HyperGraf Bank Product Advisor

Latest Version:
3.5
Machine Learning solution to identify potential home loan candidates and their risk profiles from customers with an existing loan portfolio.

    Product Overview

    Bank Product Advisor is designed to assist the home loan department to shortlist potential candidates from customers with an existing loan portfolio with the bank e.g. Student Loan, Vehicle Loan, etc. It is divided into 2 modules: Module 1: Identifies potential home loan customers using demographics and other loan related parameters as given in the usage instructions. Module 2 [Optional]: Categorises risk of customer spends and investments to further shortlist the candidates from recommendations generated by module 1.

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Recommendation engine built on exhaustive set of demographic, account, income and existing loan portfolio related features.

    • Additional risk categorisation module built using customer's spend and investment pattern to help shortlist results from the main recommendation engine. It helps the loan approver to adapt to bank specific loan targets and external environment factors.

    • Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$8.00/hr

    running on ml.m5.large

    Model Batch Transform$16.00/hr

    running on ml.m5.large

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$0.115/host/hr

    running on ml.m5.large

    SageMaker Batch Transform$0.115/host/hr

    running on ml.m5.large

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.m4.4xlarge
    $8.00
    ml.g4dn.4xlarge
    $8.00
    ml.m5.4xlarge
    $8.00
    ml.m4.16xlarge
    $8.00
    ml.m5.2xlarge
    $8.00
    ml.p3.16xlarge
    $8.00
    ml.r5.large
    $8.00
    ml.g4dn.2xlarge
    $8.00
    ml.m4.2xlarge
    $8.00
    ml.r5.12xlarge
    $8.00
    ml.c5.2xlarge
    $8.00
    ml.r5.xlarge
    $8.00
    ml.p3.2xlarge
    $8.00
    ml.c4.2xlarge
    $8.00
    ml.g4dn.12xlarge
    $8.00
    ml.m4.10xlarge
    $8.00
    ml.c4.xlarge
    $8.00
    ml.m5.24xlarge
    $8.00
    ml.c5.xlarge
    $8.00
    ml.g4dn.xlarge
    $8.00
    ml.r5.24xlarge
    $8.00
    ml.p2.xlarge
    $8.00
    ml.m5.12xlarge
    $8.00
    ml.g4dn.16xlarge
    $8.00
    ml.p2.16xlarge
    $8.00
    ml.c4.4xlarge
    $8.00
    ml.r5.4xlarge
    $8.00
    ml.c5.large
    $8.00
    ml.m5.xlarge
    $8.00
    ml.c5.9xlarge
    $8.00
    ml.m4.xlarge
    $8.00
    ml.c5.4xlarge
    $8.00
    ml.p3.8xlarge
    $8.00
    ml.c4.large
    $8.00
    ml.m5.large
    Vendor Recommended
    $8.00
    ml.c4.8xlarge
    $8.00
    ml.p2.8xlarge
    $8.00
    ml.g4dn.8xlarge
    $8.00
    ml.t2.xlarge
    $8.00
    ml.c5.18xlarge
    $8.00
    ml.t2.large
    $8.00
    ml.r5.2xlarge
    $8.00
    ml.t2.medium
    $8.00
    ml.t2.2xlarge
    $8.00

    Usage Information

    Fulfillment Methods

    Amazon SageMaker

    Input

    Supported content types: text/csv

    The solution is divided into the following modules: Module 1: Based on the parameters listed below, this module helps identify the potential home loan customers with an existing loan portfolio. The data required contains demographic, account and other existing loan related variables as follows: "CUSTOMER_ID" - Unique ID of the customer "ACCOUNT_TYPE" - Type of Account held by the customer "GENDER"- Gender of the customer "AGE" - Age of the customer "MAX_BALANCE_MTD" - Max balance maintained over the customer lifecycle "MIN_BALANCE_MTD" - Min balance maintained over the customer lifecycle "TWL_TAG"- If the customer has an active two wheeler loan "PL_TAG"- If the customer has an active personal loan
    "EDU_TAG" - If the customer has an active education loan "TL_TAG" - If the customer has an active term loan
    "OTHER_LOANS_TAG" - If the customer has any other active loan "EOP_BAL_MON_01" - End of period balance for last 3 months "AMB_MON_01" - Average monthly balance for last 3 months "CUSTOMER_PROFESSION"- Customer Designation "METRO_CITY" - If the customer address falls in a metropolitan city "LAST_3MTHS_INCOME"- If any credits have been made in the last 3 months to the customer account "SAL_MON_01","SAL_MON_02","SAL_MON_03" - Salary for last 3 months credited to the bank account "CRED_NEED_SCORE" - Credit requirement score as assessed by the marketing team

    Module 2 [Optional]: Module 2 considers customer spend and investment patterns and provides a risk categorization matrix with the following categories: "RISKY INVESTMENTS" "SAFE INVESTMENTS" "ESSENTIALS" "NON-ESSENTIALS" The module 2 further help shortlist the potential HL candidates from the recommendations generated by Module 1.Module 2 helps the loan approver to adapt to the loan targets and external environment factors.

    Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Mphasis HyperGraf Bank Product Advisor

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