The impact of AI on the workforce

By Daoud Abdel Hadi, Lead Data Scientist, Eastnets and Seun Sotuminu, Data Scientist, PDM, Eastnets
Despite the rise in automation and increasing adoption of AI, human involvement remains crucial in fighting financial crime

Forbes reports that around 18% of global work could be automated by AI. The financial crime sector is no exception. For quite some time now, financial institutions have been trailing behind in terms of technological innovations. However, the past few years has seen a concerted effort by banks to improve their practices by embracing more data-driven techniques such as AI.

As a result, compliance teams and fraud examiners are facing a significant evolution in their roles, requiring a shift in skills. However, despite the rise in automation, human involvement remains crucial in fighting financial crime. The combination of human expertise and AI can form a strong defence against financial fraud.

This article explores why AI is here to stay and the need for change. It also discusses how compliance officers and fraud examiners must adapt, and the importance of attracting the right talent to ensure such teams run as effectively as possible.

The need for change

Challenges with efficiency and accuracy ring especially true in compliance and anti-fraud departments. Most banks still rely heavily on rules-based transaction monitoring systems to detect criminal activities. The digital revolution within the financial services sector has significantly amplified transaction volumes. Simultaneously, it has also opened up new avenues for complex fraudulent activities. Rules-based systems are not equipped to handle such a landscape due to their simple, imprecise nature. This, combined with the low risk appetite of banks, have resulted in a massive false alerts problem.

With the risk of fraudulent transactions slipping through, heavy regulatory fines, and reputational damage, compliance teams have no choice but to laboriously sift through huge numbers of alerts, most of which as false alerts. This negatively impacts bank in the following ways:

  1. High resource costs: Compliance teams have been scaling up their efficiency by recruiting additional personnel. This is a big cost on banks to maintain large teams.
  2. Time wasted: Investigators are required to perform their due diligence on every alert generated. However, a large majority of time and effort is wasted on alerts that are false.
  3. Lack of thoroughness: Due to time wasted on false alerts, investigators do not have the luxury of time to carefully examine more complex cases.

AI and the shift in workforce

Scaling down compliance teams

AI is a revolutionary force that's making waves in the modern workforce. AI's primary ability is to automate tasks. This automation not only boosts process efficiency but also enhances accuracy, leading to a more streamlined, error-free system. This effectively means significantly fewer alerts, and more importantly, fewer false alerts. Inevitably, this will lead to some job loss. Banks will be able to cut down on bloated teams and instead foster smaller more specialised teams focused on complex cases.

The advent of AI has not diminished the importance of human roles. Instead, it has enhanced them. The tasks that can now be offloaded to AI systems are those that often consume a great deal of time and resources. By automating these tasks, compliance teams and fraud examiners can now focus on more strategic, high-value tasks that require critical thinking.

The future isn't about AI vs. humans; it’s about AI and humans. The marriage of human intuition with AI's precision can create a powerful, efficient workforce.

Scaling up analytical roles

AI systems come with their own set of challenges. Unlike rules, they factor in tens, sometimes hundreds, of factors when making predictions. Investigators are required to understand how the AI came to its conclusions and validate any suspicious activities of the entity in question.

This will be a significant shift in the roles of compliance teams and fraud examiners. Mundane tasks are now being efficiently handled by AI, freeing up investigators to focus on more analytical and strategic aspects of their work.

Their responsibilities will extend beyond routine tasks. They are expected to interpret the results generated by AI systems - such as charts, visualisations, and network graphs - and make informed decisions based on these interpretations. Furthermore, they are tasked with devising effective strategies to address potential compliance issues and mitigate fraud risks.

While AI systems can detect fraud patterns and suspicious activities, interpreting these patterns, understanding the root cause, and strategizing the next course of action require human intervention. Therefore, the potential for a more efficient and effective approach is achieved not by replacing humans with AI but through collaboration.

Retaining and attracting the right people

Transitioning to data-driven systems necessitates a team equipped with data skills, especially in the field of financial crime. Here, investigating a case often involves analysing large volumes of transactions to identify trends and patterns.

Enlisting the expertise of data-proficient professionals, such as data analysts and data scientists, can significantly enhance the specialized domain knowledge of compliance officers and fraud examiners. These domain experts can leverage their extensive experience and knowledge to identify potential areas of concern. Following this, data analysts can delve into the data, extracting valuable insights to assist the experts in conducting a comprehensive analysis and making the most informed decisions.

Conclusion

In conclusion, the influence of AI on the workforce is revolutionary, especially for compliance teams and fraud investigators. The enhanced efficiency and precision provided by AI will enable banks to reduce their compliance teams without completely eliminating human involvement. Humans will continue to play a significant role, with the emphasis shifting towards smaller, more analytically-driven teams dedicated to probing intricate cases.

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