MoolahSense, the leading digital lending platform for SMEs in Singapore, today announced the launch of MoolahSense Active Intelligence (M.A.I.). M.A.I. is the first blockchain and cloud based artificial intelligence platform that monitors loans and borrower business activities in real-time in order to prevent and anticipate delinquency, fraud, and default.
As the first platform to receive Capital Markets Services license from MAS with more than S$70M loan disbursed, MoolahSense is drawing on valuable insights and experience from our loan data to build an intelligent engine of TRUST.
At MoolahSense, we recognise and believe that Trust is the currency in lending. And that approving or rejecting a loan request is based on the level of trust a lender gives to a borrower.
For any lender, the perennial challenge in lending decision making is their ability to distinguish between perception and reality where validation of truth is in the form of successful loan repayment.
Today, as a lender, you are constantly challenged by these questions:
How do I trust the information and disclosures from the borrower?
How do I know before everyone else if the borrower will encounter problems keeping to their repayment obligations, and with that knowledge, how can I anticipate and mitigate risks?
How do I know whether a borrower’s delinquency is temporal or structural?
How can I dynamically adjust my credit model through self-learning and be responsive to new information in a scalable manner so as to maximise my loan performance while increasing origination opportunities?
MoolahSense has the answer for you with M.A.I.
MoolahSense Active Intelligence (M.A.I.) is based on Blockchain, AI & Machine Learning technologies and it runs on top of a microservices architecture.
M.A.I. uses proprietary techniques to actively monitor borrowers business performance in real-time while sending customisable alerts to lenders.
M.A.I. improves the effectiveness, consistency and accuracy of credit assessment processes with unique machine learning algorithms able to identify new credit decision patterns inside any lender origination and loan book datasets.
M.A.I. is the first A.I. to deliver descriptive, predictive and prescriptive analytics in lending industry.
M.A.I. helps lenders identify new origination opportunities within their loan book and create new credit products adapted to unique niches and/or vertical markets.
By running on top of a decentralized network of origination datasets powered by Hyperledger Fabric blockchain, M.A.I. enables the first highly secured, encrypted, and consolidated global SMEs lending data points accessible by any lenders participating on the network.
M.A.I. will officially launch during the FinTech Festival 2018 in Singapore.
Join us for a demo (Kiosk: #ZS02)
“Having observed and experienced the opportunities and challenges in digital SME lending in the past 3 years, we are excited to launch MAI to significantly transform the sector.
From day 1, our focus has been to simplify and speed up access to working capital for SMEs. With M.A.I., we envision a nimble and responsive credit engine that is self-learning and self-correcting to provide on-tap financing to SMEs at their point of need. With M.A.I., we envisage improving the robustness and resilience of SMEs with a consistent and cherished relationship, bridging TRUST to enrol lenders to participate in this space.” said Lawrence Yong, Founder and Chief Executive Officer, MoolahSense.
- November 2018(26)
- October 2018(76)
- September 2018(55)
- August 2018(63)
- July 2018(74)
- June 2018(64)
- May 2018(65)
- April 2018(76)
- March 2018(82)
- February 2018(65)
- January 2018(80)
- December 2017(71)
- November 2017(72)
- October 2017(75)
- September 2017(65)
- August 2017(97)
- July 2017(111)
- June 2017(87)
- May 2017(105)
- April 2017(113)
- March 2017(108)
- February 2017(112)
- January 2017(109)
- December 2016(110)
- November 2016(121)
- October 2016(111)
- September 2016(123)
- August 2016(169)
- July 2016(142)
- June 2016(152)
- May 2016(118)
- April 2016(60)
- March 2016(86)
- February 2016(154)
- January 2016(3)
- December 2015(150)