It’s no secret that cyber criminals are upping their game with the help of Artificial Intelligence (AI). AI is predicted to encourage defrauding subscribers with robocalls. However, the same AI is also predicted to rescue with a solution called branded calling. AI is also predicted to be the cause for upped Anti-money Laundering (AML) system spend because of its ability to improve risk assessment and reduce costs.
How to fight fraudulent robocalls with AI
As per a study, AI-based voice cloning is the biggest challenge to subscribers as they lose to fraudulent robocalling, which will exceed $76 B globally next year, rising from $64 B in 2023. AI-based voice cloning uses AI to generate realistic human-like speech in real-time, often imitating a known individual to the subscriber.
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Robocall mitigation vendors can tackle the rise in voice cloning by ensuring that frameworks are able to identify fraudulent robocalls before they are connected to the subscriber. Phone number whitelists, such as the RMD (Robocall Mitigation Database) in the US, provide a substantial tool to authenticate enterprises before the call is made. However, branded calling solutions can minimize losses to robocalling scams.
Branded calling solutions display a visual key that quickly informs users of the legitimacy of the call. Frameworks must contain robust verification processes for enterprises using these solutions to build trust amongst subscribers. In turn, branded calls will limit the pick-up rate for fraudulent robocalls using AI and reduce the impact of this emerging threat before subscribers answer calls.
AML system spend to go up owing to AI’s ability to improve risk assessment & reduce costs
As per a study by Juniper Research, by 2028, total spend on third-party AML (Anti-money Laundering) systems will have grown by 80%; up from $28.7 billion in 2024 driven by the use of AI to assist AML analysts and reduce false positives.
AML systems are increasingly using AI in an assistive role. These AI co-pilot systems can reduce the number of false positives and improve risk assessment. AI could still remain popular as a co-pilot due to ongoing concerns from regulators around the explainability of fully automated decisions using AI.
“AML systems vendors must foster partnerships with an extensive range of data providers to allow for real-time updates from respected news sources and risk types. These collaborations will enable AML systems to more readily analyse behavioural attributes and assess risk.” — Research author Daniel Bedford
AML system vendors are also increasingly expanding the scope of industries they cover beyond financial markets. For example, the total spend on third-party AML systems by professional and other businesses, such as the legal, real estate and non-profit sectors, will reach $6.3 billion globally by 2028, growing 170% from 2024.
This substantial growth will be driven by a tightening of regulatory requirements, necessitating areas such as legal and real estate sectors to adopt comprehensive AML systems to monitor and reduce potential fraud.
There will be an increased need for data monitoring solutions that utilize adverse media screening, which scans media for criminal activity and negative news, to combine multiple information sources beyond traditional outlets. Therefore, Anti-money Laundering systems should expand the capabilities of their adverse media screening systems to include blogs, social media, and search engine data. This will allow such systems to gain a more holistic view of customer risk levels and improve threat-detection accuracy.
Read more: AI the two-way sword in cybersecurity: It endangers & rescues in an unforeseen threat landscape
To better manage financial crime risk, AML solutions must be able to respond to dynamic changes in watchlists or adverse media, and analyze customers’ behavior against established indicators of suspicious and non-suspicious activity.
Research author Daniel Bedford said, “AML systems vendors must foster partnerships with an extensive range of data providers to allow for real-time updates from respected news sources and risk types. These collaborations will enable AML systems to more readily analyze behavioral attributes and assess risk.”
