Artifical Intelligence in Financial Crime

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Artificial Intelligence within Financial Crime

Financial crime is a major threat to financial institutions (FIs) today. Criminal networks are employing financial crime to underpin their activities, from organised crime, to terrorism, and drug and human trafficking. As such, banks have been put on the front lines of crime prevention. FIs have continued to increase their spending to support Anti-Money-Laundering (AML), Know Your Customer (KYC), and other financial crime compliance activities, to address a host of new challenges.
If the frequency of high-profile financial crime incidents and the amount of losses and regulatory penalties are any indication, financial institutions across the globe are dealing with systemic threats when it comes to financial crime. From fraud and money laundering to insider trading, these offences can have a significant impact not only on organisations, but also on individuals and economies. They can fuel criminal enterprises and activities, including human trafficking, terrorism and drug trade.
Penalties from the Office of Foreign Assets Control (OFAC) of the U.S. Department of the Treasury have risen dramatically. In the first five months of 2019 alone, penalties have been higher than the last four years combined, with approximately USD 1,228 million versus 812 million. In 2017, it was predicted that fines for misconduct were expected to exceed USD 400 billion by year 2020. From 2010 to 2015, banks paid over $300 billion in fines related to non-compliance.

America AI Integration

In December of 2018, five US government agencies, including the Federal Reserve, Federal Deposit Insurance Corporation (FDIC), Financial Crimes Enforcement Network (FinCEN), National Credit Union Administration and Office of the Comptroller of the Currency (OCC), issued the Joint Statement on Innovative Efforts to Combat Money Laundering and Terrorism Financing. The document encourages banks to implement innovative approaches, specifically referencing AI.
Regulatory encouragement is also seen from the Australian Transaction Reports and Analysis Centre (AUSTRAC), which launched an initiative to facilitate collaboration across the Australian banks that includes the application of advanced analytics to improve the investigation of suspicious activities. UK Financial Conduct Authority (FCA) has held multiple public workshops, bringing together FinTech’s and established financial institutions to experiment with various new technologies that improve the identification and management of potential financial crimes.
Currently, traditional FI processes are largely manual and incapable of scaling to meet the new challenges. It is not uncommon to have high false positive rates that is, notifications of potential suspicious activity that do not result in the filing of a suspicious activity or suspicious transaction report, well above 90 percent. In fact, for AML alerts, high false positives are the norm. The reason for this is a combination of dated technology and incomplete and inaccurate data. Advancements in areas such as artificial intelligence (AI) and machine learning (ML) can help transform FIs, making them more efficient, agile, and better able to detect financial crime. AI can augment the investigation process and provide the analyst with the most likely results, driving faster and more informed decisions with less effort.

Critical use cases where AI can be prioritised:

• Detect and focus on suspicious behaviour with advanced analytics and machine learning
• Discover new money laundering typologies through network analysis
• Enhance segmentation through data mining techniques
• Enhance customer and payment screening using text mining
• Improve operational efficiency via predictive modelling
By utilising Artificial Intelligence, machine learning and advanced analytical techniques, FI can achieve prominent outcomes includes:
• Improved quality of alerts and reduced false positives
• Speed and accuracy of complex transaction monitoring investigations
• Reduction in overall alert volumes, and better detection of complex cases
• Automated handling of simple alerts
• Reduced escalation into SAR from Transaction monitoring operations
• Faster responses to emerging risk
• Simplified investigation and audit reporting
• Detection of payment fraud faster
Financial crime and corruption are at epidemic levels and many countries are unable to significantly reduce corruption. Regulators and financial institutions are looking to innovative AI technology to fix problems that have grown beyond their ability to solve with existing tools alone. A misunderstanding about AI is the belief that it will replace employees. However, the financial crime analyst is, and should always be, an essential part of this process. AI, process automation and advanced analytics are tools that can perform analyses and tasks in a fraction of the time it would take an employee. Yet, the ultimate decision-making power still lies with analysts, investigators and compliance officers for whom this technology provides greater insight and eliminates tedious task work.
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