New Growth Opportunities in Trade
February 22, 2025
AI Innovations Elevate Mobile Banking
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- March 9, 2025
Since December 2024, several banks, including China Construction Bank, Industrial and Commercial Bank of China, Agricultural Bank of China, Bank of Communications, and Beijing Bank, have upgraded their mobile banking applications, with a significant focus on the integration of artificial intelligence (AI). This development marks a considerable leap forward in how services are delivered and experienced by customers, reshaping traditional banking paradigms.
Recent analyses indicate that the application of AI in mobile banking primarily enhances customer experience and fosters business innovationAs the digital landscape evolves, customer expectations have grown, leading banks to leverage AI to meet these demandsBanks are transforming their services to be more user-friendly, efficient, and secure while ensuring privacy and data security in an era marked by increasing concerns over data breaches.
Data security has become a crucial topic, particularly with the growing reliance on digital channels
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As part of the AI enhancements, banks have implemented measures to boost the security of data collection, storage, and analysisThis proactive approach not only protects sensitive user information but also improves overall service quality, showcasing a commitment to safe banking practices.
One of the priorities for AI application within banks is customer marketingA recent survey indicated that 63.9% of AI resources are allocated to client marketing efforts, 55.6% focused on risk control, while 50% are utilized for customer operations managementMoreover, 47.2% is directed towards customer service and rights protectionThis allocation of resources highlights the strategic importance banks place on cultivating customer relationships and optimizing service delivery through intelligent automation.
Sun Yang, a senior researcher at Suzhou Bank, emphasized the pivotal role AI plays in enhancing customer service responsiveness
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AI systems can provide 24/7 automated responses to customer inquiries, significantly boosting customer satisfaction levelsMoreover, these systems enable personalized financial product recommendations that adapt to individual customer preferences, aligning with the ongoing trend towards tailored banking experiences.
Further insights from Wang Peng, an associate researcher at the Beijing Academy of Social Sciences, underscore AI's potential to improve operational efficiency within banksBy automating repetitive and time-consuming processes such as loan approvals and account management, banks can streamline workflows and optimize resource allocationAdditionally, AI-driven scheduling systems provide insights on optimizing service locations, such as branch offices and ATMs, thus enhancing resource utilization and minimizing operational costs.
AI's capabilities extend into the realm of risk management
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By analyzing transaction patterns and behavioral characteristics, AI systems can detect potential fraudulent activities in real-time, safeguarding customer fundsFurthermore, leveraging big data and machine learning algorithms enables banks to assess credit risks more accurately, thereby enhancing decision-making capabilities.
However, with increased automation arises a critical question: how can banks ensure that the application of AI does not compromise the accuracy and security of business processes? Liu Tong, the deputy general manager of CFCA's Product Center, highlighted the multifaceted strategies implemented to maintain precision and safety in AI-driven operations.
To guarantee the accuracy and safety of AI technology in processing transactions, banks establish stringent model evaluation frameworksComprehensive assessments and continuous testing of AI models ensure their reliability and stability over time
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In the context of loan approval models, for instance, historical data regression testing aligns closely with actual business performance metrics to validate model effectiveness and risk detection capabilities.
Maintaining a balanced asset liability matching is essential for insurance companiesThe rigidity of liabilities, as demonstrated in life insurance products, necessitates a stable investment approachFor instance, despite repeated declines in predetermined interest rates, the interest rates on long-term policies hover around 3%. Coupled with operational management costs—which include labor, marketing, and real estate expenses—the pressure on investment returns becomes increasingly pronouncedThis dual challenge compels insurance companies to invest in secure, stable fixed-income products such as government bonds and bank deposits to ensure capital safety while simultaneously seeking higher yields to cover rigid liabilities.
However, the persistent decline in market interest rates has posed significant challenges to fixed-income assets
Products like government bonds and bank deposits, once regarded as steadfast income sources, now face diminishing returns and struggle to meet the investment yield demands of insurance capital, complicating efforts to maintain long-term stable income.
Conversely, opportunities within the capital markets become increasingly prominentAs the market matures and its regulatory framework improves, many quality companies have reached reasonable valuationsIndustries with high growth potential, such as renewable energy and artificial intelligence, present exceptional investment opportunitiesThis shift allows insurance companies to reallocate capital towards equity assets, enabling them to share in corporate growth while optimizing asset structures to enhance overall portfolio yields and better align assets and liabilities.
Moreover, in this rapidly evolving financial technology landscape, optimizing and updating AI models becomes increasingly crucial
Banks now harness reinforcement learning—a machine learning approach built on feedback from environmental interactions—to refine decision-making strategiesBy encouraging the model to experiment with various actions and adjust based on rewards or penalties received from the environment, these systems learn to navigate complexities effectively and make optimal decisions.
In practice, intelligent investment advisory services exemplify this applicationGiven the volatile market dynamics and shifting customer investment behaviors driven by economic conditions and personal finances, banks utilize reinforcement learning technology to ensure their AI models remain responsiveWhen market fluctuations occur, models swiftly assess asset risks and returns while factoring in clients' risk tolerances, financial goals, and investment timelines to adjust strategies
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