On Thursday, startup Bright Money announced its launch armed with a fund raise of $31 million from Sequoia Capital India, Falcon Edge Capital, and Hummingbird Ventures.
The startup has also assembled a strong set of angel investors, including Naveen Tewari and Abhay Singhal of InMobi, Kunal Shah of CRED, Jitendra Gupta of Jupiter, Gunjan Soni of Zalora, Pradeep Parameswaran of Uber, and Ram Shriram, a technology investor who is one of the first investors in Google and currently on the board of Alphabet.
Bright Money aims to use data science to build and scale a “consumer fintech startup for the world from India”, and will use the funding to expand its teams and product suite across markets.
Not an Ad: If y'all want to pay down credit card's faster, and avoid late fees try Bright Money!!! It's been so helpful for me! When you buy something they will take out a small amount put it into a separate account, and then they automatically pay your cards for you. #Finances
— Mia Satya 石美亞 (@MiaTuMutch) May 18, 2021
“Bright Money has invested in building a unique technology-led solution to help consumers manage their money and reduce debt. The business of consumer debt and savings is ripe for innovation, to deliver real value and simplicity to users looking to improve their financial lives,” said Ram Shriram, Founder and Managing Partner, Sherpalo Ventures.
The Bright Money founding team also includes Petko Plachkov, a financial services veteran, formerly with Mckinsey, and Alex Seyfert, a consumer tech product leader from Amazon.
MoneyScience is a patented AI platform, and uses thousands of data points on each consumer’s financial life and 34 algorithms to build highly customised financial plans for users.
The MoneyScience system was built over two years by leading AI and machine learning experts from the ground up, combining fundamental AI technology from other industries (adtech, entertainment, robotics, industrial automation) with personal finance best practices.
The outcomes are simple, understandable, and high impact plans, which are uniquely tailored to each individual. Currently, such hyper-personalised planning is only available from professional financial planners who charge a high price.
Speaking of how they got the idea, Varun said, “Avi and I have been working with data problems at InMobi, and we applied it to radical domains. We wanted to build a data-science first product for a real problem.”