Financial institutions and other organisations have quickly adopted Know Your Customer (KYC) solutions to comply with Anti-Money Laundering (AML) regulations and secure transactions. However, not all solutions are created equal.
Acuant’s Trusted Identify Platform completes more than 1.5 billion transactions in over 200 countries and territories. As a company that powers trust for leading global financial institutions, FinTechs, digital banks and more, here Acuant explains why it is necessary to implement AI and machine learning in fraud prevention solutions.
As financial institutions rapidly work to comply in the midst of digital transformation, many experience challenges to scale KYC and serve their customers. Even worse, some solutions lead to more fraud and regulatory findings during compliance audits. In fact, fines against financial institutions almost reached $10.5 billion in 2020 due to compliance breaches.
Therefore, it is necessary financial organisations leverage compliance solutions that are easy to deploy, adaptable thanks to modern technology in order to keep up with evolving compliance and regulatory changes, as well as new fraud challenges.
Fraudsters are exploiting the gaps in user authentication, stolen identity data, and account hacks. This can be mitigated if businesses deploy both KYC and fraud prevention technology to enable strong, efficient KYC processes and fraud mitigation strategies. Companies should look for layered solutions that employ the right level of artificial intelligence (AI) and machine learning (ML) to be the most effective- balancing that with human oversight.
Customers should be able to be verified in seconds with seamless, automated identity proofing that provides a trust anchor from an ID, data point, biometric or other identifiers such as social media or alternative data- and one that can be built upon (layered) for initial and continuous identity proofing and risk management.
The best KYC solutions go beyond the initial KYC event to recognise a user’s risk profile changes over time with real-time transaction monitoring. These tools can capture additional and updated attributes, conduct identity analysis, and ultimately update customer risk profiles and risk of transactions accordingly based on associated data.
To cut out even more time-consuming leg work, transaction monitoring should be packaged with analysis reporting. Without AI, manual reports do not provide enough information to accurately assess risk and create time for escalations and decision making. Reporting can be designed to provide companies with these findings in real-time and even efficient AML compliance results to avoid any accidental compliance breaches.
In addition to monitoring transactions, managing the risks identified during monitoring can be made more efficient with ML. Through ML, teams can avoid managing too many alerts by rating these alerts and requiring potentially suspicious transaction users to experience more hurdles. Organisations of all sizes can also leverage growing data on fraud patterns and untrusted identities to make decisions based on trust scores tailored to their industry and updated risk levels.
As we continue to experience the rapid digital transformation of 2021 and increased cyber vulnerabilities and fruad, AI and ML technologies will be necessary for every financial institution workflow. As fraud rises with digital transactions, financial institutions must act fast to protect themselves. They require robust KYC solutions with identity proofing, transaction monitoring, and risk management that can be tailored to new needs and threats continuously.
Learn more about KYC in Acuant’s Buyers Guide, here.