TookiTaki, a startup that develops machine learning-based financial compliance software, announced today it has raised a $11.7 million in additional Series A funding, led by Viola Fintech and SIG Asia Investment, with participation from Normura Holdings. Existing investors Illuminate Financial, Jungle Ventures and SEEDs Capital also returned for the extension, which brings TookiTaki’s total Series A (first announced in March) to $19.2 million.The company is using the funding to enhance their anti-money laundering (AML) and reconciliation software, and to hire for its offices in the United States, Singapore and India. In a press statement, Viola Fintech general partner Tomer Michaeli said “With almost twenty years’ experience that Viola has in the AML sector, we found Tookitaki’s approach to be very unique. Its pragmatic way of creating an overlay on top of legacy AML systems helps increase accuracy and significantly lower operating costs for financial institutions. Moreover, its regulator-ready ‘glass box’ solution shows an innovative approach and a deep understanding of the challenges in the modern AML solutions market.” [gallery ids="1916308,1916307"] TookiTaki was co-founded by CEO Abhishek Chatterjee and COO Jeeta Bandopadhyay in 2012. When TechCrunch reported on its seed round in 2015, the company provided data analytics to marketers. But it decided to focus its machine-learning platform for predictive analytics on regulatory compliance in late 2016 after realizing that there is a bigger business opportunity for vertical AI than a horizontal platform play, the founders told TechCrunch in an email.
Chatterjee was an associate at JP Morgan during the 2008 financial crisis and worked with U.S. regulators to make sure the bank’s products complied with new regulations. During that time, he says he realized that current anti-money laundering solutions reduced the effectiveness of compliance programs, and also struggled to keep up with the growth of digital banking and online transactions. Many legacy AML software had high false positive rates, TookiTaki’s founders say, and also missed activity by more sophisticated money launderers. TookiTaki claims it reduces false positives for transaction monitoring by 50%, a result validated by Deloitte. Its software uses explainable machine learning models, which means their decisions are broken down in a way that can be easily understood by compliance staff, while providing them with the details they need for investigations. TookiTaki’s products can also help minimize costs by using a distributed computing framework, so it can be deployed in the cloud or on premise. The software has two main modules: one that looks for suspicious transactions across different systems, and names screening, which screens for high-risk individual and corporate customers. Other TookiTaki features include machine learning algorithms that are constantly 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.