The enhancement of identity verification owes its momentum to global regulatory requirements aimed at combating money laundering. Nevertheless, these anti-money laundering (AML) regulations necessitate the constant monitoring of new data by banks and regulators to detect any signs of fraudulent activity, even among previously trusted organizations.
Fortunately, AI-powered solutions can assist financial institutions (FIs) in meeting these crucial standards without compromising the customer experience. For instance, if an investigative report uncovers the involvement of a specific organization in money laundering for criminal groups, AI analysis can swiftly process and structure this new information. By applying it to existing AML rules, AI can effectively restrict access to an FI’s network for entities engaged in suspicious or illicit activities.
The financial system faced significant challenges due to the sanctions imposed following the escalation of the Russo-Ukrainian War in 2022. Many fintech companies that lacked sophisticated techniques struggled to fulfill their obligations as the list of individuals exposed to sanctions rapidly evolved. Amidst this backdrop, regulatory authorities have adopted a stricter stance on financial crimes, particularly in terms of enforcing sanctions.
Within this context many companies employs AI to identify individuals and organizations targeted by government actions. As a result, financial institutions can swiftly identify the correct individuals subject to sanctions and update their screening services accordingly.
Financial services companies supporting sanctions enforcement can now update newly named individuals or organizations almost instantly, eliminating the time lag that could be easily exploited in the past.
While it is essential to weed out malicious actors, it is equally crucial for fintech companies to facilitate access to the financial system for consumers through streamlined processes and transparent customer service. Compliance with AML regulations should not come at the expense of the customer experience.
Many Analyst predicts that financial fraud will become increasingly sophisticated over time, rendering rules-based models insufficient in coping with the evolving landscape.
Deep learning, particularly through the utilization of Generative Adversarial Networks (GANs), will play a vital role in addressing these challenges. GANs have the ability to generate realistic synthetic data, augmenting existing datasets. This synthetic data can then be used to train models on potential future risks that banks have not encountered before.
If banks can predict the future to a certain extent and leverage AI to generate synthetic data for training models, they can reduce the long-term costs of fraud.
Another emerging trend in fraud prevention involves collaborative efforts among companies to combat fraud. Federated learning, a secure method of training AI models while preserving the confidentiality of underlying data, enables banks to share data across organizations and geographies without exposing sensitive information. Ultimately, this facilitates resource pooling among FIs and improves the industry’s fraud detection models.
This technique brings significant benefits to the security of the financial system, considering that fraud is often perpetrated not against a single bank or financial institution but through the exploitation of multiple institutions for money laundering and fraud. By leveraging federated learning, cross-industry and cross-geography consortiums are incentivized to collaborate, enabling the financial system to uncover relationships between previously undiscoverable data sets and effectively combat emerging fraud methods.