Anti-money laundering software machine startup TookiTaki raises $11.7 million in additional Series A funding

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TookiTaki, a startup that has created machine learning-based financial compliance software, has recently announced that it had raised an $11.7 million in additional Series A funding.

 The initiative is led by Viola Fintech and SIG Asia Investment, with participation from Nomura Holding. Other investors like Illuminate Financial, Jungle Ventures, and SEEDs Capital have also funded TookiTaki’s total Series, thus raising the gross amount to 19.2 million dollars.

The company had plans to utilize the funding to enhance anti-money laundering (AML), to reconcile the software and to hire more staff for its office in the United States, India, and Singapore.

Viola Fintech’s general partner, Tomer Michaeli, informed the media that – “With almost twenty years experience that Viola has in the AML sector, we sought Tookitaki’s approach to be unique. 

Its realistic way of creating an overlay on top of legacy AML systems will help to increase accuracy and significantly lower operating costs for financial institutions.

Handle of door to bank vault safe

 Moreover, its regulator ready glass box solution shows an innovative approach and a deep understanding of the challenges that the modern AML solutions market faces.”

Abhishek Chatterjee and Jeeta Bandopadhyay cofounded TookiTaki in the year 2012. When Tech Crunch reported on its seed round in 2015, the company provided data analytics to the marketers. 

But it decided to centralize the attention on its machine-learning platform for predictive analytics on regulatory compliance in late 2016 after realizing that there is a more significant business opportunity for vertical AI than a horizontal platform play, the founders told TechCrunch in an email.

The software has two modules – the first one that looks for suspicious transactions in different systems, and names screening, which screens for high-risk individual and corporate customers. 

Other one TookiTaki features accumulate machine learning algorithms that are continually updating for new money laundering patterns and dividing alerts into low, medium and high-risk, making it easier for companies to figure out how to prioritize investigations.