Many banks still rely on COBOL code to compute all sorts of financial operations. COBOL is a reliable programming language, but the only issue is that it was designed more than 50 years ago. Now that all industries are looking at machine learning as the next big thing, banks are left behind.
At the same time, banks and financial firms also generate a ton of data. Those companies are already taking advantage of this data with traditional linear regression algorithms and simple criteria. And yet, you can significantly improve those models by switching to machine learning.
DreamQuark has been working on a product called Brain so that financial companies can make smarter decisions. There are many use cases for AI-powered algorithms in the finance industry.
Thanks to our comprehensive approach, we are faster than companies that specialize on fraud in one country in particular, for one channel in particular— Nicolas Meric
It’s a great way to assess risk, from fraud detection to anti-money laundering and credit scoring. It can also help you manage a portfolio by detecting early signs of market changes. And DreamQuark can also segment your customer base and detect some patterns. This way, you can promote financial products and improve retention rates.
“Thanks to our comprehensive approach, we are faster than companies that specialize on fraud in one country in particular, for one channel in particular,” co-founder and CEO Nicolas Meric told me. And it’s true that one client usually has multiple financial products — everything is connected. Your credit history could be a sign when it comes to anti-money laundering detection for instance.
DreamQuark also ticks all the right boxes when it comes to compliance as it can identify bias and explain each decision to comply with regulators and GDPR. And of course, DreamQuark doesn’t share data between different clients. You can let DreamQuark manage the service for you or install it in your own data center. But everything is segmented between each client and data is protected.
The company just raised $3.5 million (€3 million) from CapHorn Invest and Plug & Play. So far, DreamQuark has attracted around ten clients, including BNP Paribas and AG2R La Mondiale. And I’m sure each contract is worth a lot of money.
By focusing on financial services, the company can quickly deploy its solution and build models as many financial companies all share the same needs.
But building models is just step one. Financial companies can then use DreamQuark’s API to assess all future data. For instance, how many times a year do you have to call your bank because you made a suspicious purchase with your card but it was actually you?
The startup can detect those false positives much more accurately so that you don’t get locked out of your own bank account. And this isn’t a security compromise as DreamQuark also identifies fraud more accurately.
When it comes to competition, I thought banks themselves would be a serious threat as they have a ton of money. But it sounds like they already tried and failed to put together teams of data scientists to work on those issues. “They compare how much it costs to build internal projects and how successful they are,” Meric told me. “We provide services that you won’t find in any open source framework.”