How to Address Present-day Sanctions Screening Pain Points with AI
Sanctions risk of financial institutions is evolving in line with the global social, economic and political changes. As seen in recent news, governments across the globe are increasingly relying on sanctions as an important measure for political foreign policy. They,...
Tookitaki Got Selected for FinTech Program: Asia meets Tokyo
Tookitaki has been listed among 8 Fintech startups in Asia for FinTech Program: Asia meets Tokyo, an accelerator launched by the Tokyo Metropolitan Government (TMG). We have been selected for our anti-money laundering (AML) solution having a unique machine learning model that automatically selects risk indicators from ever-changing customer behavior and detects suspicious transactions without using personal information or setting specific thresholds. Tookitaki has developed a Typology Repository Management (TRM) solution, which provides a new way of detecting money laundering through collective intelligence and continuous learning. TRM complements Tookitaki’s automated machine learning approach, which builds detection models based on his...
AML Alert Management: How AI can Augment Your Compliance Efficiency
We live in a digital world where the threat of money laundering is both increasing and evolving in unprecedented ways and scale. Financial institutions are finding it tough to compete with financial criminals and their sophisticated schemes. In order to establish a robust anti-money laundering (AML) compliance program, it is important for financial institutions to have a first line of defence that monitor, investigate and report suspicious activities. The setup normally involves a combination of technology and staff. Financial institutions rely on a single solution or multiple solutions that monitor and screen transactions, accounts and customers, and generate alerts based on defined rules and thresholds. Once alerts are generated, AML investigators use documented risk-based policies and human judgement to determine if an alert is truly risky.
In present times when AML systems generate several thousands of alerts every month and most of the alerts being identified as no-value alerts (often referred to as false positives), it is becoming increasingly difficult for the compliance team to both keep deadlines and correctly identify cases that matter. Research says that banks are wasting more USD 3.5 billion per year chasing false leads because of outdated AML systems that rely on stale rules and scenarios and generate millions of false positives.
Having stated the problem, we are trying to explain here the potential of artificial intelligence (AI) in alerts management with a real-life use case.
Why is AML Alerts Management Important f...
Tookitaki Recognised for Innovative Use of AI & Machine Learning
Tookitaki has won the Regtech Award for AI & Machine Learning and was highly commended in the solutions category for AML/CTF Compliance, in the 3rd Regulation Asia Awards for Excellence 2020 in an online ceremony on 15 December 2020.
Tookitaki has developed a Typology Repository Management (TRM) solution, which provides a new way of detecting money laundering through collective intelligence and continuous learning. TRM complements Tookitaki’s automated machine learning approach, which builds detection models based on historical learnings and nuances within the given universe of data.
The approach represents a move away from financial institutions having to manually hard code typologies into traditional transaction monitoring solutions, which today takes significant time, effort and investment to implement. Tookitaki’s TRM provides access to typologies from regulators, financial institutions, NGOs and other bodies in a machine-readable format, effectively creating thousands of risk indicators and establishing a federated ecosystem where firms can generate individual learnings in a decentralised way.
During the Covid-19 pandemic, while most financial institutions generated a large number of false positives as a result of high volumes of ATM withdrawals, Tookitaki’s systems were instead able to recognise similar behaviours occurring across customer groups and update its models accordingly, thereby avoiding ATM activity being flagged as suspicious activity and reducing the manual efforts that would be required to investigate and close each case.
“Today’s AML monitoring solutions are largely rules-based. Even when machine learning te...
Our UOB Success Tale: Setting a New Benchmark for AI-based AML Comp...
Tookitaki achieved a rare and historic milestone as our Anti-Money Laundering Suite (AMLS) solution went live within the premises of United Overseas Bank (UOB), one of the top 3 banks in Singapore.
Tookitaki Wins 2020 WITSA Digital Innovation Award
Tookitaki has won the 2020 Digital Innovation Award from the World Information Technology and Services Alliance (WITSA) as part of the industry body’s Global ICT Excellence Awards. Nominated by Singapore’s SGTech, Tookitaki won the recognition for its adv...
