Explore the benefits and risks of AI in fintech – TechTarget
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With AI’s increased visibility following the generative AI boom, enterprises have rushed to explore and adapt it for competitive advantage. Financial institutions like JPMorgan Chase, Bank of America and Goldman Sachs pioneered new AI technology to reduce costs, boost efficiency and increase competitive advantages.
AI’s use cases in financial technology, or fintech, range from customer service chatbots to fraud detection to automation of repetitive tasks. However, like any new technology, AI carries risks for fintech, including data privacy concerns and potential biases in decision-making.
Fintech is a broad term referring to the use of innovative technology to provide financial services. As pioneers in the digital revolution, fintech companies were among the earliest adopters of AI to support financial services and operations.
AI can improve efficiency and accuracy in many aspects of fintech. Technologies like machine learning, natural language processing (NLP) and computer vision are now widely used in fintech, for example.
AI offers numerous benefits for fintech organizations, including fraud detection and scam prevention, automation, compliance, virtual assistants, personalized service, predictive analytics and enhanced security.
AI can combat fraud and scams in fintech via the following measures:
AI helps fintech organizations automate repetitive tasks, improving efficiency and reducing human error. The following are a few examples:
In compliance processes, AI can help conduct regulatory checks and prepare risk assessment reports. For instance, AI tools can monitor financial transactions and related activities in real time to ensure adherence to regulations and flag potential issues as they arise. These tools can also automatically generate compliance reports for human review, reducing manual effort and ensuring timely delivery to regulatory bodies.
AI-powered chatbots and virtual assistants can help fintech companies improve customer service and reduce support costs. For instance, AI chatbots provide the following benefits:
AI can provide more personalized customer service through machine learning, NLP and data analytics. Examples include the following:
AI can enhance decision-making processes in fintech in the following ways:
AI can assist fintech companies in several cybersecurity endeavors. For example, NLP algorithms can scan incoming emails, text messages and social media posts to identify phishing attempts and other social engineering attacks.
Many of today’s antimalware and antivirus tools employ advanced machine learning algorithms to analyze software behavior in real time. This improves their ability to effectively stop emerging threats, especially zero-day exploits and polymorphic malware, compared with traditional security options that depend on detecting malware signatures. AI can also be connected to various threat intelligence feeds, providing up-to-date protection at lower costs.
AI-powered antivirus options also continually learn from their work. For instance, they can update their detection mechanisms based on emerging malware trends, which boosts their ability to detect and stop emerging malware, such as variants specifically targeting financial institutions.
Despite these benefits, integrating AI into fintech also comes with risks. Key considerations include the following:
Nihad A. Hassan is an independent cybersecurity consultant, expert in digital forensics and cyber open source intelligence, blogger, and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.
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