Anti-money laundering
AI Asia Fintech

Tookitaki: Why Collective Intelligence Is Key To AML Battle

Demand for AI-powered anti-money laundering solutions is expected to soar in 2021 as financial institutions battle the evolving trends of criminals in a post-Covid world. Regtech Tookitaki says next-gen technology and collaboration is key to sustainability.

During the Singapore FinTech Festival this week, regtech Tookitaki will showcase its award-winning solution, the Anti-Money Laundering Suite (AMLS), for addressing the pitfalls of the current anti-money laundering (AML) compliance ecosystem.

Tookitaki – which translates as hide-and-seek in Bengali – enjoyed success with its typology repository management (TRM) framework for tackling money laundering related to cryptocurrencies in the recent Saudi G20TechSprint hackathon-style competition.

A key component of AMLS, the TRM framework detects money laundering through collective intelligence and continuous learning. It enables financial institutions to capture changing customer behaviour and stop the bad actors with high accuracy and speed, improving process efficiency and risk coverage.

It helps identify suspicious cases, besides prioritising alerts with high accuracy, without the need to apply any personally identifiable information (PII), rules and thresholds.

Abhishek Chatterjee, Founder & CEO at Tookitaki
Abhishek Chatterjee, Founder & chief executive at Tookitaki

Abhishek Chatterjee, founder & CEO at Tookitaki, explains: “Winning in the international competition G20 TechSprint, that had more than 100 participants, was a proud moment for us. We believe the achievement will boost our value as an effective provider of AML solutions and aid in our international expansion.”

According to Tookitaki, the Covid-19 pandemic has elevated the money laundering risk as criminals are adapting their pitches to suit the situation and come up with many fraud schemes and money mule scams.

Yet, it says, many current transaction monitoring solutions fail to provide financial institutions a comprehensive AML risk coverage because they are fragmented and mostly rely on rules. Or they are augmented by traditional machine learning approaches that are not enough to keep pace with changing customer behaviour due to increased digitalisation and detect complex money laundering activities. Especially via new payment methods, such as P2P, cryptocurrencies and e-wallets.

“There are relatively new money laundering schemes emerging and existing AML solutions may not be able to identify them. The current solutions also generate ultra-high false positives, making the AML programmes ineffective and inefficient with increasing cost,” says Chatterjee.

Tookitaki, incorporated in November 2014 in Singapore, believes collaboration among various industry stakeholders, such as financial institutions, regulators and technology firms is vital for altering the current AML compliance picture and ‘to make the world a better place to live’.

Its TRM allows money laundering patterns to be automatically shared across banks globally (irrespective of their size) to facilitate collective intelligence and to ‘outsmart the criminals by identifying the suspicious money trails buried deep inside the mountain of legitimate transactions’.

Chatterjee comments: “In order to effectively tackle the crime, there should be a collective effort within the industry. We believe our TRM framework is a stepping stone for this collaboration. The platform enables financial institutions, regulators and AML experts to share their domain knowledge related to the crime for the benefit of the industry.

“We expect increased demand for AI-based AML solutions in the coming years, as financial institutions have started realising the efficiency and effectiveness improvements of these new-age technologies. At the same time, we see increasing support from regulators to test, develop and implement AI-based solutions.”

Author

  • Claire works across print and online as Editor for The Fintech Times.

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