Unlike static algorithms , AI adapts to new data and evolving fraud tactics . “ Static algorithms have limitations . They excel at specific tasks but struggle to adapt to changing environments . This is a major drawback in the fight against mobile network fraud , where fraudsters are constantly devising new tactics ,” adds Harsha . “ Here ’ s where LLM-based AI agents shine – their ability to adapt to new data and evolving fraud tactics significantly impacts the mobile sector .”
AI agents in fraud management operate independently , making decisions and taking immediate actions without constant human intervention . This autonomy , combined with real-time actioning , allows CSPs to reduce fraud response significantly .
“ AI agents can autonomously execute actions like blocking suspicious transactions or triggering additional authentication , enhancing fraud prevention capabilities and minimising losses ,” he explains .
“ AI agents continuously learn from new data , including past fraud attempts . This allows them to identify emerging patterns and adapt their detection methods . This ongoing learning cycle keeps AI agents ahead of the curve , leading to a significant improvement in fraud detection rates within the mobile sector .”
78 September 2024