Ratan Jyoti, CISO, Ujjivan Small Finance BankArtificial intelligence (AI) is revolutionizing the banking industry and bringing significant changes to the way banks interact with customers. With its ability to analyze massive amounts of data, predict trends and automate processes, AI is improving the customer experience in unprecedented ways. Revolutionize customer support by expanding customer-facing activities such as voice banking, chatbots/virtual assistants, loan approval processes and strengthening backend operations such as fraud detection and prevention, legal document examination and robotic process automation, among others. One of the most visible impact of AI in banking is the AI-powered chatbots and virtual assistants that are available 24/7 and provide customers with instant support and guidance. They can understand and respond to customer questions in natural language, provide human-like interaction, provide personalized product recommendations, and help customers find solutions tailored to their needs.
AI has significantly improved the security of banking transactions. Machine learning algorithms analyze transaction patterns to detect unusual activity and potential fraud in real time. This proactive approach helps prevent unauthorized transactions and secure customer accounts. Furthermore, AI improves identity verification processes, ensuring secure access to banking services and reducing the risk of identity theft.
AI is transforming the loan and credit decision-making process by analyzing vast amounts of data, including credit history, income, spending patterns and more. This data-driven approach enables banks to assess creditworthiness more accurately and faster. AI-powered systems can also identify customers who may qualify for new financial products, improving the customer experience by offering timely and relevant options.
AI automates routine banking processes, freeing up staff to focus on more complex tasks. For example, AI can perform tasks such as document verification, data entry, and transaction processing. This automation not only speeds up business operations, but also reduces the chance of human error, ensuring customers a seamless banking experience.
AI tools analyze customer data to gain deeper insights into customer behavior and preferences. Banks can use these insights to develop targeted marketing strategies and improve customer engagement. By understanding customer needs and preferences, banks can create more relevant and personalized offers, increasing customer satisfaction and loyalty.
With AI, banks can deliver a seamless omnichannel experience, giving customers a consistent and integrated experience across different touchpoints, such as mobile apps, websites and physical branches. AI systems can track customer interactions across channels and provide a unified view, enabling a more coherent and convenient customer journey.
AI-powered predictive analytics can help customers make informed financial decisions. By analyzing spending patterns and predicting future financial needs, AI tools can provide personalized financial planning advice. This proactive approach helps customers better manage their finances and plan for future goals, improving their overall financial well-being.
While traditional rules-based security applications may face challenges in keeping up with evolving cyber threats, AI’s machine learning capabilities allow it to stay one step ahead of cyber attackers by identifying new attack vectors and patterns and adapting detection algorithms accordingly. Thanks to its precision in identifying suspicious behavior, AI can significantly reduce the number of false positives, streamlining the authentication process and improving the overall customer experience.
The impenetrable nature of artificial intelligence models complicates the understanding of the processes for generating results, which may conflict not only with current laws and regulations, but also with the internal governance, risk management and control frameworks of financial services providers and banks. While machine learning and generative AI have the potential to improve cybersecurity automation in areas such as intrusion detection and data loss prevention, it also poses risks by enabling the automation of cyber reconnaissance and attacks. Financial institutions using AI algorithms should take stock of their technology systems to identify potential model risk areas. In addition, they must develop frameworks for managing these risks and implement appropriate measures to mitigate them.
The author is Ratan Jyoti, CISO, Ujjivan Small Finance Bank.
Disclaimer: The views expressed are solely those of the author and ETCISO does not necessarily endorse them. ETCISO is not responsible for any damage caused directly or indirectly to a person/organization.
