Rachel Dettmer
Artificial Intelligence (AI), once perceived as a distant future phenomenon, has firmly planted its roots in various sectors, revolutionizing their operations.
AI is changing the financial services industry and compliance function – we’re focusing on how it can support investigations, giving a high-level overview of how it works, and what the future holds as we continue to leverage its capabilities.
Effective Compliance: Closing the Gaps with AI
While human intelligence is still critical, it can be supported by adopting and integrating AI-powered solutions.
For large banks, there can be billions of customer transactions every month.
The sheer magnitude of this data outstrips the capacity of individuals to review manually, and it can quickly become an overwhelming amount of information to manage, process, and govern.
AI-powered solutions can solve this big data challenge and underpin a successful management strategy.
By tapping into a holistic view of your transactional data, the AI model directs you to the highest weighted money laundering risks by examining transactions, accounts, customer relationships, company, and other data to identify patterns and anomalies that deviate from normal behavior – prompting further investigation.
The ability of AI to understand specific risks in context can help reduce false positives and alerts, which can then be assessed at a deeper level while reducing the risk of errors.
Banks can learn from their historical data, enabling early detection and prevention of financial crime. Likewise, algorithms can learn, grow, develop, and adapt by themselves alongside emerging risks, helping identify and prevent financial crime.
This allows financial organizations to automate, optimize, enhance, scale, and benefit from significant efficiencies and cost savings.
Can AI Learn a New Red Flag or Typology?
The answer is simple: Yes.
By combining historical data and machine learning techniques, a well-trained AI model will identify, categorize, and understand various flags or typologies.
It can look at transactions and determine what is or isn’t normal behavior.
When there is an area that it hasn’t experienced before, this will be classified as an outlier for the statistical system. The model will report its uncertainty which can be driven back to the expert to review and analyze.
This also speeds up and automates what is normally a very laborious process of identifying and applying a new red flag or typology.
Having an AI-machine-learning-based technology able to quickly see new patterns that criminals are engaged in, with an ability to communicate ‘this looks risky, you should investigate this,’ bypasses the multi-year process of getting a rule written, approved, and engaged before the criminals have moved beyond that typology.
What if AI gives me the wrong result?
Nothing is absolved from errors; human or machine.
In compliance, when investigators or analysts make mistakes, they are likely caught by some form of a quality control review process.
As the nature of work changes and lower-level investigations are replaced by AI models, the human interaction of the investigation process will focus on providing expertise, insight, and becoming a part of the Q&A review process rather than just tactically executing the exercise.
In addition, AI models genuinely learn from their mistakes, and errors can be corrected with new data and more training.
“When a machine makes a mistake, and the human assumes the role of Q&A and becomes part of the review process, that goes back into the training of that machine so that those mistakes are fewer and more infrequent than they were in the past. It’s a virtuous cycle.” – David McLaughlin, EVP, Technology Sales, AML RightSource.
What Does the Future of AI and Compliance Look Like?
As we progress from conventional rule-based detection to advanced AI models, the role of the financial crime compliance analyst will undergo a significant transformation process.
At present, AI models serve as pivotal sources of information, propelling critical insights to the investigators. This information not only accelerates their investigations but also augments their accuracy, enabling them to make informed determinations.
Moving forward, while humans will continue to be an integral part of this equation, their interaction with artificial intelligence will evolve. Rather than passively receiving information, investigators will be able to actively engage and query their data with the model, tailoring their interactions to best suit their needs.
This is the future we are steadily approaching.
As AI continues evolving and becoming more deeply intertwined with our daily lives, we can expect marked improvements in productivity, efficiency, and convenience.
Final thoughts
Banks and financial institutions have long grappled with compliance processes' slow and cumbersome nature, often impacting efficiency and the bottom line.
While AI offers significant advantages for organizations striving to achieve and maintain compliance, the ongoing concern that technology is replacing decisions that, for decades, have been made by humans, is still a valid one. That said, the narrative is not entirely correct.
Banks and financial institutions should continue to proactively explore AI's potential while respecting the importance of human judgment and experience. AI won’t and should not replace compliance teams; instead, it should be viewed as a tool to enhance efficiency and reduce risk.
In an era of complex regulations, heightened scrutiny, and fast-moving financial crime, embracing AI solutions will make compliance more cost-effective and straightforward, reducing alert fatigue levels across operation teams.
Want to hear more? Listen to our podcast ‘Leveraging AI for Compliance’ featuring our EVP of Technology Sales, David McLaughlin, and Principal Data Scientist, Leandro Loss.
Or, if you want to find out more about how we currently use the latest and finest technology to fight financial crime, fill out our contact form, and let’s start the conversation.