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Financial institutions must leverage data science and artificial intelligence (AI) technologies to strengthen defenses against emerging cyber security threats, often amplified by AI itself.
Rizal Commercial Banking Corp. (RCBC) director and expert on Data Science and Artificial Intelligence Erika Legara emphasized the transformative impact of AI on banking operations.
“The trend is clear—I expect that the influence of AI, especially in our sector, is set to grow,” Legara said in a forum focused on the realm of human-like AI, its advantages and potential risks.
The data science expert also underscored the urgent need to modernize verification systems and enhance fraud detection capabilities.
“Our verification systems need to be upgraded. Through AI, we can enhance fraud detection accuracy by as much as 85 percent. The shift is inevitable. It’s coming, and we have to be ready,” she added.
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To complement its technological advancements, financial institutions, like RCBC must invest in employee training and awareness to solidify their cyber-security framework.
RCBC employs 51 rigorously validated AI models supported by an elite team of 40 data scientists, composed of statisticians, physicists, mathematicians, and engineers. This diverse group collaborates closely on each project, ensuring that every business decision is not only data-driven but also aligned with ethical standards, focusing on extracting actionable insights.
For instance, RCBC responsibly employs AI models to analyze customer data and transaction histories, identifying tailored product offerings that foster stronger customer relationships and drive revenue growth. Additionally, these models assess various financial metrics, including credit history and income levels, to accurately gauge applicant creditworthiness.
Meanwhile, AI models that also help prevent cyber fraud utilize a broad range of technologies, including anomaly detection systems, machine learning algorithms, natural language processing (NLP), behavioral biometrics, predictive analytics, network traffic analysis, and fraud scoring systems.
In contrast, some examples of cyber fraud using AI include sophisticated phishing attacks that utilize AI-generated emails to mimic legitimate correspondence, thereby deceiving users into disclosing sensitive information or clicking on malicious links.
Additionally, AI-powered deep fake technology enables cybercriminals to create convincing audio and video impersonations, facilitating social engineering attacks and manipulation of digital identities.
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