In recent years, the banking industry has increasingly relied on artificial intelligence (AI) and machine learning (ML) techniques to combat fraud and money laundering. These techniques, when combined with natural language processing (NLP), can provide banks with powerful tools for detecting and preventing illegal financial activities. However, there are significant challenges to implementing these technologies effectively.
One of the main challenges of AI and ML in banking fraud detection is the need for large and diverse datasets. To train models to accurately identify fraudulent activities, banks must have access to data from a wide range of sources and transactions. This data can be difficult to obtain, particularly for smaller banks or those with limited resources.
Another challenge is the complexity of financial transactions. Money laundering and fraud can take many forms, and it can be difficult for even the most sophisticated machine-learning models to detect all of them. Additionally, fraudsters are constantly evolving their tactics, which means that models must be continuously updated and refined to stay effective.
The implementation of these technologies also requires significant investment in IT infrastructure and expertise. Banks must have the resources to develop and maintain the necessary software, hardware, and data storage systems. They also need to employ data scientists and AI experts who can work with the technologies and ensure they are used optimally tuned.
In addition to these technical challenges, there are also legal and ethical considerations to consider. Banks must ensure they are complying with relevant regulations, such as the General Data Protection Regulation (GDPR) and the Bank Secrecy Act (BSA). They must also be transparent with customers about how their data is being used and ensure that the technologies they use do not perpetuate biases or discriminate against certain groups.
Despite these challenges, AI, ML, and NLP are increasingly being used in banking fraud and anti-money laundering detection, with the potential to significantly improve banks' countermeasures and to ensure the integrity of the financial system. However, it will be essential for banks to invest in the necessary resources and expertise to implement these technologies effectively and responsibly.
Several potential issues can arise when implementing AI, ML, and NLP technologies. Some of the most significant include:
Biases and discrimination: one of the biggest challenges with AI, ML, and NLP is the risk of biases and discrimination. These technologies are only as good as the data they are trained on, and if that data is biased or incomplete, it can lead to inaccurate or unfair results. For example, if a system is trained on data that reflects historical biases against certain groups, it may perpetuate those biases when making decisions.
Lack of transparency: AI, ML, and NLP models can be complex and difficult to understand, which can make it hard to explain how decisions are being made. This lack of transparency can be a concern in industries like finance, where customers may want to know how decisions about their money are being made.
Overreliance on technology: it's important to remember that AI, ML, and NLP are just tools, and they should not replace human judgment entirely. Overreliance on technology can lead to a lack of critical thinking or oversight, which can cause problems down the line.
Data privacy and security: AI, ML, and NLP require access to large amounts of data, which can be a potential target for hackers or other bad actors. It's important to ensure that appropriate security measures are in place to protect sensitive data.
Lack of standardisation: there are many different AI, ML, and NLP tools and techniques, and they are not always standardised or interoperable. This can make it difficult to integrate different systems or compare results across different models.
Technical skills requirements: AI, ML, and NLP are complex technologies that require significant expertise to develop and maintain. Implementing these technologies can require significant investments in IT infrastructure and talent and may not be feasible for smaller organisations.
The gap between data scientists and front-end operators (developing a positive feedback loop): data scientists usually develop and train the ML model based on initial requirements. After they deploy the ML model, there is no formal cadence to update it. On the other hand, the masterminds of financial crimes keep evolving. Front-end operators are, fortunately, often up to date on the new tactics, but there is often no feedback loop between these operators and data scientists that would make the ML model relevant and effective.
Preparing the System: training both parties on how to apply real-world activities in such a way as to help the ML models learn effectively and quickly will boost the cost-to-performance ratio. Learning activities include breaking down a use case into steps, identifying the critical data elements and expressing how each piece of data is used (or generated) by the bad actor will better inform the system as it evolves. This takes practice.
For example, when looking at payment activities over a long period, each segment, or group, will exhibit a series of behavioural oscillations as repeat payment types, amounts, and frequencies involving third parties develop a ‘moving average’ of sorts. Interestingly, it is the customers who never oscillate, whose behaviours run counter to the group norm and who seem too quiet are often too quiet for a reason. Teaching the ML model to look for the inverted anomaly can quickly reveal suspicious behaviours that have been flying under the radar for a long period. Not all bad actors behave outside the standard deviation of the group norm, sometimes they are too close to the mean for way too long.
Lastly, it's important to approach the implementation of AI, ML, and NLP technologies with a critical eye and a focus on transparency, fairness, and accountability. By doing so, it is possible to harness the power of these technologies while minimising potential risks and drawbacks while optimising the expected return on investment.
This editorial was initially published in the Financial Crime and Fraud Report 2023 which dives into the captivating world of fraud management, digital onboarding, and financial crime in the financial services industry. You can download your free copy here.
With 20+ years of compliance and anti-financial crime experience, Alan specialises in strategic planning, technology integration, risk mitigation, and change management. His expertise spans US, Canadian, UK, and APA financial regulations, and he has held leadership positions at JP Morgan/Bear Stearns, Adsideo LLC, Oliver Wyman, and Sapient.
With 20+ years of experience, Fanny drives business transformation, customer experience, and automation maturity for institutions across multiple industries. Her expertise spans consumer products, higher education, energy, financial services, and healthcare. Fanny previously held leadership positions at UiPath, McKinsey & Company, PwC, and Deloitte.
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