Overview
Micro-loans have become increasingly popular amongst individuals and SMEs. These customers demand a loan product that is more flexible: much lower loan limits, near-instant loan decision, and more frequent withdraws. While many banks and fintech companies work hard to develop micro-loan products for new customers, their existing credit risk capabilities are unable to meet the new requirements: quickly roll out new risk models to support the new micro-loan product development.
Alibaba Cloud Credit Risk Management Solution allows lenders to easily apply machine learning and use their own dataset to develop, train, and deploy credit risk models. It comes with a scorecard template including workflow, algorithm, and test dataset that makes the variable transformation process highly efficient. Banks and fintech companies can use this solution to proactively manage credit risk, support new micro-loan products, and confidently meet regulatory requirement, such as Basel II.
Challenges and Solutions
For banks or fintech lenders alike, credit risk management has long been challenging in many aspects
Inefficient new risk model build
Credit score model needs to be updated quickly to support business growth. This is challenge due to constraints of tool: it is hard for the modeler to quickly identify the relevant data attribute, model validating needs a lot of manual work including data import, and result analysis; model deploying is time consuming because of manual process.
Real time scoring is needed
Micro-loan user expects to know their limits instantly after application. This requires the lender to invest in server and other compute resources and handle peak concurrent loan applications. However, it can be hard to predict the number of users, when the peak load would take place, and how much hardware to invest.
Inefficient reporting process
Risk management team needs to prepare various credit risk reports that are updated in a timely manner: acquire data from various systems, perform data consolidation, calculate the risk value at borrower level, aggregating it to portfolio level. Traditionally, this is done manually with spreadsheet-based reporting, which often overburdens analysts and IT.
Machine Learning for Model
Using machine learning and other AI technology will help financial organization to quickly identify the attributes which are relevant for credit risk, especially from alternative data where past experience is limited. The solution also comes with scorecard templates and embedded algorithms which are commonly used for credit risk evaluation.
Better On-going Risk Management
Using machine learning and AI saves lots of time of finding the correlation between a data attribute and default. This would speed up the upgrading process. Instead of reviewing the model once a year, it could be done more frequently to ensure the credit model can always capture the changes of the user behavior, help to make the best decisions for loan approval.
Report Credit Risks Efficiently
Cloud computing can process large volumes of data quickly and cost-effectively. This can help financial institutions to calculate risk exposure faster and more frequently at lower cost. By doing this, CROs can better communicate risks exposure to executives, help the organization to make better decisions, and meet regulatory requirement more confidently.
How It Works
Your Challenge
Having the right model is one of the most challenging tasks for credit risk management. How can I find the right tool and platform to help overcome those challenges.
Our Solution
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Alibaba Cloud Dataworks helps to consolidate data from different resource to a centralized platform.
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Platform for AI, which is also used by Ant Financial for its credit risk models and other fine-tuned models, can help modelers to build which are most suitable for the organization.
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Model report is available to clearly document the model structure which is required by regulator. Once deployed, the credit risk score calculation would be real time with MaxCompute, even at peak business demands when a large number of customers are applying in parallel.
Your Challenge
Legacy technologies and warehouse investments are no longer suitable for increasing demands for risk computation speed, frequency, and aggregation capabilities.
Our Solution
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An efficient risk aggregation infrastructure, built on Alibaba Cloud with DataWorks and Maxcompute, enables multiple requests for risk reports in multiple formats to be delivered quickly and smoothly.
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The solution provides organization advanced report design and generate tools to meet complex credit risk reporting requirements.
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View DetailEmpowering Innovations with Alibaba Cloud FinTech Support
Alibaba Cloud provides a secure and intelligent service specifically designed to empower financial services on the cloud. Submit your application today, and one of our dedicated account managers will get in touch with you soon.
FinTech Essential
Get started building and running your FinTech websites, apps, and applications for free. With 16 free-trial products and over 20 always-free products, this package can help you test and run light workloads on the cloud.
Start for FreeProduct Support
Free trial
Up to $1,000 in proof-of-concept credit
$1,500 proof-of-concept credit for big data services
Training Support
Clouder training course
DevOps learning path
Big Data learning path
Marketing & Sales Support
Invitation to exclusive local Alibaba Cloud events
Marketing support and co-marketing opportunities, plus a listing on www.alibabacloud.com as a FinTech Solution Provider
Account Support
24/7/365 customer support
Dedicated Account Manager with 1-on-1 consultation and preferred pricing plans
Cloud migration assistance
FinTech Pro
Get up to $1,000 in cloud credit to run your FinTech proof-of-concept project.
The $1,000 credit covers a large variety of products, including several of our computing, database, and security products. As part of this package, you also can take part in our DevOps training path and receive around-the-clock support for your cloud migration journey.
Apply for FinTech Pro PackageProduct Support
Free trial
Up to $1,000 in proof-of-concept credit
$1,500 proof-of-concept credit for big data services
Training Support
Clouder training course
DevOps learning path
Big Data learning path
Marketing & Sales Support
Invitation to exclusive local Alibaba Cloud events
Marketing support and co-marketing opportunities, plus a listing on www.alibabacloud.com as a FinTech Solution Provider
Account Support
24/7/365 customer support
Dedicated Account Manager with 1-on-1 consultation and preferred pricing plans
Cloud migration assistance
FinTech Big Data
Get up to $3,500 credit to run your FinTech proof-of-concept project.
Once approved, the credit includes $2,000 for elastic computing, database, and security products, and $1,500 for big data services. As part of this package, you also can take part in our DevOps and Big Data training paths and receive around-the-clock support for your cloud migration journey.
Apply for FinTech Big Data PackageProduct Support
Free trial
$2,000 proof-of-concept credit
$1,500 proof-of-concept credit for big data services
Training Support
Clouder training course
DevOps learning path
Big Data learning path
Marketing & Sales Support
Invitation to exclusive local Alibaba Cloud events
Marketing support and co-marketing opportunities, plus a listing on www.alibabacloud.com as a FinTech Solution Provider
Account Support
24/7/365 customer support
Dedicated Account Manager with 1-on-1 consultation and preferred pricing plans
Cloud migration assistance
TERMS AND CONDITIONS:
1. FinTech Support Plans are intended for new enterprise accounts in the financial industry only. Purchases must be made directly on www.alibabacloud.com to be eligible. Resellers, distributers, and offline customers are not eligible.
2. A specialist will get in touch with you shortly after submission. As part of the submission process, you will need to submit details of your proof-of-concept project for review and approval purposes.
3. The amount approved is subject to adjustment and may be only partially approved.
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