Money laundering and reconciliation management have become the major concerns of organizations working in the financial services sector. While they continually battle against the money launderers to sustain and maintain their reputations and avoid legal issues, complying with the new regulatory standards has become another major challenge, with regulators increasingly tightening their norms and standards.
Incorporated in 2014 in Singapore, Tookitaki, an international company led by a core team with cumulative 30 years’ experience in machine learning and finance, addresses these issues by providing sustainable compliance programs and applications catering to anti-money laundering (AML) and reconciliation for the financial services sector.
Tookitaki started off its journey building a machine learning-based intelligent Decision Support System (DSS) that could offer higher accuracy and faster time-to-market for any predictive analytics platform. “The DSS was meant to empower businesses to go beyond the barriers of existing statistical packages creating one-off solutions by offering production-ready, automated predictive modelling,” says Abhishek Chatterjee, the Founder & CEO of the company.
However, the company soon realized the role machine learning could play in helping banks comply with regulatory standards without being subjected to increasing costs. Thereafter, the company was determined to solve the problems of the financial services industry by developing sustainable compliance programs for banks and building applications catering to anti-money laundering (AML) and reconciliation. The company’s AML and Reconciliation Suites are built on top of its intelligent Decision Support System (DSS) based on artificial intelligence (AI).
“As a mission, we aim to improve risk and efficiency through machine learning-enabled software solutions, which are actionable, explainable and scalable”, says Chatterjee, the young entrepreneur, who founded Tookitaki with some core values such as commitment to clients, care for both employees and customers, collaboration and continuous innovation, and the passion to constantly learn and pursue excellence.
To talk about Tookitaki’s products in brief, the Anti-Money Laundering Suite is a one-stop solution to handle transaction monitoring, names screening and KYC/customer risk ratings. The focused modules on transaction monitoring and names screening help prioritize alerts/hits and subsequently bring in a significant reduction in false alerts/hits. In addition, Tookitaki complements existing systems and applies machine learning to continuously learn patterns from historic cases, recent transactions and sanctions list updates to create a sustainable alerts management framework. “Our AML Suite improves operational efficiency by 40%”, boasts Chatterjee. He further explains that most of the recon solutions in the market are RPA providers or legacy rules-based solutions. RPA or rules-based applications fail to handle exceptions as they are based on a set of conditions/rules, while exception-handling requires human judgment beyond the basic set of rules.
On the contrary, Tookitaki’s Reconciliation Suite is a machine learning-powered end-to-end enterprise reconciliation software. It has modules on matching, exception handling and adjustment amount recommendation. It learns from historical patterns and can recommend, matching, exception resolution and adjustment amount, without any human intervention. “The suite offers more than 90% accuracy in matching and exceptions predictions,” adds Chatterjee.
About the growth of the company, Jeeta Bandopadhyay, the Co-founder and COO of Tookitaki, says: “Tookitaki has grown 200% year on year, starting FY2016-17”. The backbone of this growth is mainly client satisfaction. In her words, “Clients who used our solutions are quite happy because we matched and sometimes exceeded their expectations.”
For instance, a premier European multi-national banking and financial services company deployed Tookitaki’s Reconciliation Suite for GL reconciliation. Tookitaki’s system was implemented as complementary to their in-house matching engine. “We applied machine learning to improve exception handling. We predicted exceptions with 99% accuracy. In addition, we generated rules to help investigators understand exceptions in detail and such ‘explainability’ reduced investigation time by 40%. The automatic learning reduced time and subjectivity, bringing significant operational efficiency in GL reconciliation,” explains Bandopadhyay.
In another instance, a leading regional bank in the US approached Tookitaki for its Anti-Money Laundering Suite. The bank wanted to improve the efficiency of the current transaction monitoring system that used to generate 95%-plus false alerts. Tookitaki’s AML suite was on a try with its pilot on transaction monitoring module. The objective was to reduce false alerts by 30% and explain prediction results to the regulator during the periodic review process. Bandopadhyay gladly shares: “We did the pilot on the commercial banking segment and were able to show a 40% reduction in false alerts on test data. We also gave a demo of our solution and could explain the prediction results successfully to the regulator.”
These success-stories encourage Tookitaki to dream big. The company positions itself as an enterprise play bringing machine learning in regulatory compliance for the banking and financial services industry. Since developing sustainable compliance programs is the key to banks, Tookitaki provides its ML-powered intelligent solutions focused on ensuring sustainability in compliance programs with accurate predictions, efficient processes, and improved financial and reputational risks and reduced compliance costs. “Our deep technology coupled with domain expertise in AML and Reconciliation has helped us build actionable, explainable and scalable software packages,” affirms Chatterjee.
This fast-growing startup has already tasted success as it was considered as one of the selected twenty-one companies which got accreditation from the Government of Singapore for stringent technology, finance and operations due diligence. SGD accredited in Nov 2017. Besides, it won the first place in the MAS FinTech Awards 2016 (Singapore SME) in the regulatory compliance space from the Monetary Authority of Singapore for its approach to make the workflows in AML and Reconciliation scalable and highly auditable (beyond ML-based black box approach). In November of 2017, the company graduated from Nomura’s corporate innovation program where it successfully delivered its product— Recon Suite – in a pilot environment.
The company, going forward, has set a target of US$10 million ARR to reach by the end of financial year 2019-20. “And our team at Tookitaki is committed to pushing its boundaries of efficacy to reach the objectives,” conclude Bandopadhyay and Chatterjee.
QUOTES
“Tookitaki soon realized the role machine learning could play in helping banks comply with regulatory standards without being subjected to increasing costs”
“Tookitaki positions itself as an enterprise play bringing machine learning in regulatory compliance for the banking and financial services industry”.