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Singapore’s Tookitaki Drives Forward AI in Anti-Money Laundering

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Enterprise software specialist Tookitaki closed its Series A Funding Round at $19.2 million last week, following a further $11.7 million injected in November. The latest round was co-led by venture funds Viola Fintech and SIG Asia Investment, along with Nomura Incubation Investment. They join existing investors including Illuminate Financial, Jungle Ventures and SEEDs Capital, an investment arm of the Singapore government.

The firm will use the funding to enhance its AML and reconciliation product offerings and build out presence in the US, Europe and Asia-Pacific. It also plans an aggressive new R&D recruitment drive across its Singapore, Bangalore and US offices – starting with the recent hire of Subhas Samanta, former Director at LinkedIn, as Vice President of Research and Development.

Tookitaki has seen revenue jump by 300% over the past two years, and has expanded into offices in Charlotte, North Carolina and Bangalore, India. The name means hide-and-seek in Bengali, an Indo-Aryan language primarily spoken by the Bengalis in South Asia, and the firm claims to use machine learning to construct an automated model to simulate the hide/seek behaviour in humans, based on the idea that data has a “life of its own”.

Incorporated in 2014 in Singapore, the firm was initially a provider of data analytics, but in 2016 switched its focus to predictive analytics within the regulatory compliance space, specialising in explainable machine learning models. Founded by CEO Abhishek Chatterjee and COO Jeeta Bandopadhyay, the firm has built on Chatterjee’s experience handling AML legislation at JP Morgan during the 2008 financial crisis to identify the gaps in provision and the challenges that can be solved by automation.

Its Anti-Money Laundering Suite (AMLS) provides an end-to-end transaction monitoring and name screening system, combining both supervised and unsupervised machine learning techniques to to detect suspicious activities and identify high-risk clients faster and more accurately.

A 2018 pilot of Tookitaki’s AML suite by Singapore’s UOB Bank, independently verified by Deloitte, found that the software achieved a 50% drop in false positives for transaction monitoring processes, along with a 70% reduction in false positives for individual name screening processes and a 60% reduction in false positives for corporate names. The bank later partnered with Tookitaki to further optimise its machine learning algorithms with new transactional data, and enhance its AMLS with co-created machine learning features.

“We are excited to be one of the very few RegTech companies globally to operationalise a machine learning-powered anti-money laundering (AML) solution within a bank’s existing infrastructure,” Chatterjee commented. “[We] use a combination of distributed data-parallel architecture and machine learning to ensure scalability across a bank’s multiple business lines and complex layers of existing technologies and systems.”

However, he warned that the successful deployment of AML solutions in the production environment required a coordinated effort between the software vendor and the bank’s technology, AML compliance, internal audit and model validation teams.

Tookitaki is a 2017 graduate of The FinLab accelerator programme, a joint venture between UOB and SGInnovate, a Singapore government-owned deep technology development firm. It recently filed a new patent on explainable AI and machine learning framework and models to bring transparency into the validation process and output interpretability for banks and regulators.

The firm is part of a wave of innovative new AML solutions providers using machine learning and artificial intelligence to fill in the gaps within AML compliance. Other pioneers include Symphony AyasdiAI, which in September 2019 launched a new AML solution using unsupervised machine learning technology. Clients include HSBC, which has reduced its false positives and investigation volume by more than 20% using the Ayasdi solution.

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