The latest reporting indicated that some of transfers were directly linked to Indonesian military officials. An Indonesian official noted that transactions could be considered as money laundering if the assets were transferred overseas to evade taxes and the investigation will check if corruption played a role in the transfer.
According to Yustinus Prastowo, executive director of the Centre of Indonesia Taxation Analysis (CITA): “Normally, there are three reasons for funds being stashed in tax havens: government officials hiding money [gained] from corruption, business people avoiding taxes and drug dealers keeping their money safe.”
There is a plethora of potential financial crime and anti-money laundering (AML) concerns evident in this case, such as tax avoidance/evasion, corruption, money laundering, politically exposed persons (PEP), and high-risk jurisdictions.
Mitigating financial crime risk is challenging by any definition, but managing the risk effectively and efficiently is now becoming easier through new applications of artificial intelligence (AI) and machine learning solutions that complement existing transaction monitoring systems (TMS) in use by financial institutions.
If the aforementioned banks had utilized an AI and machine learning solution to analyze the suspicious transfers during the above-cited investigation, then the private banking accounts would have been holistically examined with related correspondent account activity as part of an enterprise financial crime mitigation approach. An AI and machine learning solution would have quickly identified groups of suspicious transactions flowing from Guernsey to Singapore rather than relying on static rules-based TMS.
Additionally, a perpetual know-your-customer (KYC) solution could have been utilized with predictive analytics on the customers’ past, present, and future transactional activity. The perpetual KYC function would also incorporate instant adverse media/PEP checks rather than waiting for a yearly to five-year KYC update.
By supplementing the traditional review of transactional and customer data, financial institutions can greatly enhance their reputational, operational, and legal risk exposure by deploying advanced data science and AI techniques that are available today. Other techniques include entity resolution and verification, UBO analysis, deep Web analytics, NLP (Natural Language Processing), Web scraping, network analysis, and volumes and values analysis.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?