DreamQuark is currently developing a product known as Brain. This is a one-stop software solution that can help financial firms make smarter business decisions.
DreamQuark is a French startup with big dreams and aspirations for the wider financial sector, from banks to insurance firms and even asset management companies.
The tech company that wants to help these and other types of financial service entities with all their artificial intelligence (AI) needs. DreamQuark applies AI as a business enabler to crunch your business data, come up with models based on machine learning and then apply the resultant solutions on all of your company’s past, present and future data points.
Currently, banks are still relying on COBOL for most of their financial computations. Although the programming language may appear reliable, it is over 50 years old and hasn’t been able to fully adapt to the modern financial space.
So, now that machine learning is undoubtedly the next big thing and most other industries are adopting or getting ready to adapt, why shouldn’t the banking sector do the same? Banks and financial companies naturally generate lots of data, and they are already applying traditional linear regression algorithms and various other simple criteria in order to benefit from this sea of data. But now with machine learning, they can tremendously enhance their existing models to take advantage of their massive data even more.
DreamQuark is currently developing a product known as Brain. This is a one-stop software solution that can help financial firms make smarter business decisions. Indeed, the finance industry has examples of many use-cases for AI-powered algorithms. DreamQuark helps you assess your business risks, detect and mitigate against financial fraud and money laundering, as well as improve your credit scoring. It can also enhance the management of your business portfolio and help you adapt to any new market dynamics in a timely manner by detecting early signs of change in your niche or industry. They are also able to segment and detect patterns in your customer base, help you better identify your niche and thus enable you to promote your financial products and enhance your retention rates.
DreamQuark Co-founder and CEO Nicolas Meric takes pride in the Paris-based startup’s comprehensive approach to offering solutions, adding that DreamQuark is faster than firms that specialize, for instance, on fraud in one particular country and one particular channel. This is because a single client will normally have multiple financial products linked to one another. For example, when one is being investigated for money laundering, their credit history can be an important sign.
Concerning matters compliance, DreamQuark also ticks all the appropriate boxes since they can identify bias and explain all decisions in order to comply with regulators and GDPR. And, for DreamQuark data confidentiality is key. Whether they install their service in your data center or just manage it for you, everything is segmented between different clients and each one’s data is safeguarded. So far,DreamQuark has raised US$3.5 million from two venture capitalists, CapHorn Investand Plug & Play, and attracted about 10 clients that include AG2R LaMondiale and BNP Paribas.
Obviously, these contracts must be worth some good money for the startup. By targeting financial services, DreamQuark can quickly deploy their solution and create workable models since most financial entities share similar needs. However, building models is only the first step. Financial firms can further use the DreamQuark API to assess future data. For example, how many times does a customer have to contact their bank after making a suspicious purchase with their credit card yet it was actually them? DreamQuark can help detect such false positives in a much more accurate and timely manner so that you don’t find yourself being locked out of your own account. And, this won’t be a compromise on security since the startup is also capable of identifying fraud more accurately.
As far as competition is concerned, many would expect banks themselves to be a serious threat since they have lots of money in their possession. But it seems as if they already tried and failed to bring together data scientist teams to deal with such issues. They compare how much it would cost to build internal projects with how successful they are. But DreamQuark comes to their rescue by offering solutions that you won’t be able to find in any existing open source framework.