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Join Now and stand a chance to win RM 5000 Grand Prize!
CHALLENGE EXTENDED TILL 8 NOVEMBER 2020!
Develop innovative automated digital lending solutions by leveraging on data sciences and artificial intelligence for automated credit decisioning.
To penetrate the market of micro SME which have limited financial/credit facilities history with new and innovative channels.
Make sure to include the features below in your solution
(AUTOMATE LOAN ON-BOARDING PROCESS - APPLICATION, APPROVAL & ACCEPTANCE)
An automated credit application solution combines the elements of the customer management module, financial analysis, and risk assessments.
Today's advanced load origination software has enhanced technology that, with appropriate permissions, allows the lender to interact via web portal with its commercial customer's systems.
While automating loan underwriting procedures can present some challenges, doing so can enhance the brand of the institution as an innovator and market leader among peers.
INNOVATIVE CREDIT DECISIONING SOLUTION
(AI AND MACHINE LEARNING)
To develop/design AI using data mining and machine learning to build credit scoring models that are able to access the credit worthiness of the micro SME customer and provide instantaneous loan approval decisions.
The AI engine is used to predict the customers' payment behavior and likelihood to default, thus able to provide early warning for customers that become NPL.
e-KYC is a digitized form of KYC (Know-Your-Customer) which is used to on-board the micro SME customers and speed up the process of authenticating their identity in merely a few minutes times through online and mobile channels supported by adoption of financial technology.
The e-KYC capabilities may include face detection, liveliness detection, ID document recognition, optical character recognition, video verification tool and many other features that are designed to execute regulatory guidelines and prevent criminal activities such as money laundering, fraud and other lawful act.
Click on the link to find out more
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