According to the FCA, fraud costs the UK £190 billion per year and constitutes around 33% of all crimes that we encounter as individuals. Similarly, money laundering costs the UK £37 billion annually. Over the years, financial institutions with less advanced technologies and weaker compliance governance have been targeted by criminals.
There are several challenges and issues that Financial Institutions are currently facing in combatting financial crime. Westpac recently incurred civil penalties issued by AUSTRAC for failing to comply with Anti-Money Laundering and Counter Terrorism Financing regulations in Australia. The US Department of Justice recently fined the Industrial Bank of Korea a total of $86 million for violating sanctions relating to Iran and Standard Chartered Bank was fined $1.1 billion in 2019 for inadequate Anti-Money Laundering measures and a further £20.47 million this year for violating sanctions relating to Russia.
Financial Institutions and regulators, including the UK’s FCA, are experimenting with the latest technologies such as predictive analytics, machine learning and AI to fight against and prevent money laundering and other illicit activity. Technology can be misused for laundering the proceeds of crime but when used in the right manner can help prevent criminal activity.
Investment in the RegTech industry has grown in the recent years, with approx. $4 billion(£3.25 billion) invested globally in the first half of the year 2019 alone. Worldwide investment in RegTech companies between the years 2014-2018 has totalled over $9.5 billion (£7.7 billion); 62.5% of this investment has been made in the KYC and AML space.
Advanced technologies in the market can help bridge the gap and help firms remain compliant. Automated Client Life Cycle Management (CLM) tools, when implemented correctly and with the right policy and risk mappings in place, can help improve operational efficiency.
These systems are highly capable of auto
mating KYC information and help to provide a fine balance between improved CLM efficiency and reduced costs. Transaction monitoring systems that use advanced technologies such as Artificial Intelligence (AI) and machine learning to track and investigate suspicious transactions, and technologies such as sandbox simulator, which helps test rules and scenarios before being deployed in operation, are powerful technologies used to investigate transaction monitoring alerts.
Availability of automation and advanced technologies help in effectively reporting Management Information and can be used to produce KPIs tailored specifically to KYC and AML. These technologies are capable of fetching data via an API automatically from a CLM tool to produce real time performance indicators that can be used to track progress, throughput, quality, and other key performances.
Senior Consultant at Lysis Group
I have a technology background and a degree in engineering and, over the past 8 years, I have worked as a consultant in the AML/CTF space assisting clients to remain compliant with money laundering regulations.
I have been involved in CLM systems implementations, rules mapping, tuning and the implementation of risk rating engines, transaction monitoring systems and designing automated MI tools as part of my offering to clients. Over the next few months, I will be discussing the following topics and the benefits of technology usage in the Anti-Money Laundering space.
CLM systems: policy and rules mapping (August Insight)
Risk Rating Engine: tuning and testing (September Insight)
Transaction Monitoring: tuning and testing (October Insight)
Management Information: advanced analytics and automation (November Insight)
Screening: logics, algorithms, and false positives (December Insight)