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.
Mphasis HyperGraf Bank Product Advisor
By:
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
Version
By
Type
Model Package
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.
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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.
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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 PricingWith 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
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
Additional Resources
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Support Information
Mphasis HyperGraf Bank Product Advisor
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