<p><strong>通过采用增强灵活性和响应能力的数字解决方案，贷款机构可以在动荡的环境中获得竞争优势，Finastra 贷款业务部门执行副总裁 Andrew Bateman 在_finews.asia_的评论文章中写道。</strong></p>
<p>技术不再仅仅是支持功能；它正在成为日益复杂和竞争激烈的全球市场中的主要差异化因素。考虑到这一点，未来贷款将定义的三大技术趋势是：数字化转型、人工智能的采用和生态系统的发展。</p>
<p><strong>1. 加速数字化转型</strong></p>
<p>技术为经济不确定性提供了一条重要的出路，但许多金融机构仍受限于遗留基础设施。过时的系统是银行希望采用人工智能、先进数据分析和其他创新以解锁竞争优势的最重大障碍之一。</p>
<p>在贸易融资领域——一个传统上对新技术接受缓慢的行业——从单一解决方案向云原生、模块化平台的转变标志着一个重要的演变。</p>
<p>重要的是，转型并不需要冒险的 “撕掉和替换” 策略。采用分阶段的方法，在现有基础设施的基础上部署下一代解决方案，使机构能够在最小化干扰和保持运营连续性的同时实现可持续现代化。</p>
<p>云优先模型现在是现代贷款技术战略的核心。通过过渡到完全托管的云环境，银行可以受益于端到端的技术服务——使他们能够专注于业务成果，而不是运营开销。这些模型提供无与伦比的可扩展性、灵活性和部署速度，使其成为优先考虑灵活性和创新的机构的理想选择。</p>
<p>数字化转型最明显的好处可能体现在客户体验上。数字自助服务门户和移动界面增强了透明度，减少了传统贷款流程中常见的挫败感。通过将客户置于体验的中心，银行可以建立长期的信任和忠诚度。随着这些数字基础的加强，它们为贷款技术的下一个前沿——人工智能创造了完美的环境。</p>
<p><strong>2. 利用人工智能提升运营效率和改善客户体验</strong></p>
<p>随着数字基础的建立，金融机构越来越多地将人工智能视为贷款创新的下一个前沿。然而，一个根本性挑战依然存在：数据质量和结构。没有良好组织和标准化的数据，即使是最先进的人工智能工具也会显得不足。这突显了协调支撑贷款生态系统的庞大而多样化的数据集的持续需求。</p>
<p>人工智能有潜力通过实现更全面的评估来彻底改变信用评估。与其仅依赖历史财务数据和抵押品等传统指标，人工智能驱动的分析可以整合替代数据源，以构建更丰富、更准确的信用 worthiness 图景。这对缺乏广泛信用历史的小型企业尤其有价值。个性化也正在成为一个关键的差异化因素，银行和金融科技公司根据个别客户独特的现金流模式和运营需求量身定制贷款解决方案。</p>
<p>在贸易融资中，人工智能可以帮助弥补随着经验丰富的专业人士退休和监管复杂性增加而产生的知识差距。这是一个人工智能助手可以提供显著价值的领域，通过为银行员工提供准确、实时和上下文感知的支持。这些工具帮助用户更轻松和准确地处理文档、欺诈检测和合规性。这些工具简化了传统上复杂的流程，减少了对专业经验的依赖。</p>
<p>通过自动化重复性任务，人工智能将提升生产力，同时让员工专注于建立关系和创造收入的活动。先进的机器学习模型将以前所未有的精确度改善信用风险评估和欺诈检测，而实时分析将使银行能够动态响应客户需求。</p>
<p>要充分释放人工智能的潜力，金融机构必须将其嵌入核心运营中——不仅仅是作为附加组件，而是作为其贷款基础设施的基本组成部分。这需要创新文化、对数据卓越的承诺以及战略整合的方法。</p>
<p><strong>3. 建立生态系统和合作伙伴关系以实现未来准备的贷款</strong></p>
<p>虽然内部创新仍然至关重要，但贷款的未来将由超越组织边界的合作来塑造。为了保持竞争力，金融机构必须拥抱生态系统思维，跨行业合作，将数字解决方案和数据流与核心贷款平台整合。这种方法为持续的创新、灵活性和增长奠定了基础。</p>
<p>随着其他金融领域迅速发展，贷款必须加速其技术演变。虽然现金、存款和债券等资产类别已经接受了数字化和代币化，但许多贷款操作仍滞后。如果不进行现代化和采用区块链等技术，贷款机构将面临失去赞助商和借款者的风险，转向更高效、技术驱动的市场。尤其在贸易融资领域，区块链平台有望彻底改变传统流程，创造前所未有的速度、效率和透明度。</p>
<p>在这种协作环境中蓬勃发展不仅需要技术投资——还需要对人力资本的投资。随着人工智能嵌入贷款操作，机构必须发展管理、监督和与智能系统协作的技能。建立未来准备的团队与建立未来准备的技术同样重要。</p>
<p>通过促进技术合作伙伴关系和培养人才，前瞻性的贷款机构可以创建具有韧性和适应性的生态系统——不仅为 2026 年做好准备，还为贷款的长期未来做好准备。</p>
<hr/>
<p><em><strong>安德鲁·巴特曼</strong> 负责 Finastra 贷款业务部门的领导和发展。在金融服务领域拥有近 30 年的经验，他指导全球团队通过技术灵活性现代化技术，支持客户的增长议程。此前，他曾在 FIS 和 SunGard（后被 FIS 收购）担任全球职位。他的金融科技职业生涯始于劳斯莱斯，在那里实施新财务系统后，他进入了 GIS/KPMG 信息解决方案（后被 SunGard 收购）。他的职业生涯使他在亚洲、南美、美国和英国生活和工作。他拥有诺丁汉大学的物理学学士学位，并且还获得了企业财务管理协会的资格。</em></p>

荷兰国际

科凯国际

<p>安德鲁·贝特曼强调了塑造未来借贷的三个关键技术趋势：数字化转型、人工智能的采用和生态系统的发展。他强调了拥抱数字解决方案以获得竞争优势、克服遗留基础设施障碍以及利用人工智能提高运营效率和个性化客户体验的重要性。贝特曼提倡建立生态系统和合作伙伴关系，以促进借贷行业的创新和增长</p>

<p><strong>By embracing digital solutions that enhance agility and responsiveness, lenders can secure a competitive advantage in a volatile environment, writes Andrew Bateman, executive vice president of the lending business unit at Finastra, in his opinion piece for <em>finews.asia</em>.</strong></p>
<div class="lb-trans"><p><strong>通过采用增强灵活性和响应能力的数字解决方案，贷款机构可以在动荡的环境中获得竞争优势，Finastra 贷款业务部门执行副总裁 Andrew Bateman 在_finews.asia_的评论文章中写道。</strong></p>
</div><p>Technology is no longer just a support function; it's emerging as a primary differentiator in an increasingly complex and competitive global market. With this in mind, three technology trends set to define the future of lending are: digital transformation, AI adoption and ecosystem development.</p>
<div class="lb-trans"><p>技术不再仅仅是支持功能；它正在成为日益复杂和竞争激烈的全球市场中的主要差异化因素。考虑到这一点，未来贷款将定义的三大技术趋势是：数字化转型、人工智能的采用和生态系统的发展。</p>
</div><p><strong>1. Accelerating Digital Transformation</strong></p>
<div class="lb-trans"><p><strong>1. 加速数字化转型</strong></p>
</div><p>Technology offers a vital path through economic uncertainty, yet many financial institutions remain constrained by legacy infrastructure. Outdated systems are among the most significant barriers for banks looking to adopt AI, advanced data analytics, and other innovations that could unlock competitive advantage.</p>
<div class="lb-trans"><p>技术为经济不确定性提供了一条重要的出路，但许多金融机构仍受限于遗留基础设施。过时的系统是银行希望采用人工智能、先进数据分析和其他创新以解锁竞争优势的最重大障碍之一。</p>
</div><p>In trade finance – a sector traditionally slow to embrace new technology – the shift from monolithic solutions to cloud-native, modular platforms marks a significant evolution.</p>
<div class="lb-trans"><p>在贸易融资领域——一个传统上对新技术接受缓慢的行业——从单一解决方案向云原生、模块化平台的转变标志着一个重要的演变。</p>
</div><p>Importantly, transformation doesn’t require risky «rip and replace» strategies. A phased approach, where next-generation solutions are deployed alongside existing infrastructure, enables institutions to modernize sustainability while minimizing disruption and maintaining operational continuity.</p>
<div class="lb-trans"><p>重要的是，转型并不需要冒险的 “撕掉和替换” 策略。采用分阶段的方法，在现有基础设施的基础上部署下一代解决方案，使机构能够在最小化干扰和保持运营连续性的同时实现可持续现代化。</p>
</div><p>Cloud-first models are now central to modern lending technology strategies. By transitioning to fully managed cloud environments, banks can benefit from end-to-end technology services – allowing them to focus on business outcomes rather than operational overhead. These models offer unparalleled scalability, flexibility, and deployment speed, making them ideal for institutions prioritizing agility and innovation.</p>
<div class="lb-trans"><p>云优先模型现在是现代贷款技术战略的核心。通过过渡到完全托管的云环境，银行可以受益于端到端的技术服务——使他们能够专注于业务成果，而不是运营开销。这些模型提供无与伦比的可扩展性、灵活性和部署速度，使其成为优先考虑灵活性和创新的机构的理想选择。</p>
</div><p>Perhaps the most visible benefit of digital transformation is on customer experience. Digital self-service portals and mobile interfaces enhance transparency and reduce the frustrations often associated with traditional lending processes. By placing the customer at the center of the experience, banks can build long-term trust and loyalty. And, as these digital foundations strengthen, they create the perfect environment for the next frontier in lending technology: artificial intelligence.</p>
<div class="lb-trans"><p>数字化转型最明显的好处可能体现在客户体验上。数字自助服务门户和移动界面增强了透明度，减少了传统贷款流程中常见的挫败感。通过将客户置于体验的中心，银行可以建立长期的信任和忠诚度。随着这些数字基础的加强，它们为贷款技术的下一个前沿——人工智能创造了完美的环境。</p>
</div><p><strong>2. Harnessing AI For Operational Efficiency and Better Customer Experiences</strong></p>
<div class="lb-trans"><p><strong>2. 利用人工智能提升运营效率和改善客户体验</strong></p>
</div><p>With digital foundations established, financial institutions are increasingly turning to artificial intelligence as the next frontier in lending innovation. However, a fundamental challenge remains: data quality and structure. Without well-organised, standardised data, even the most advanced AI tools will fall short. This underscores the ongoing need to harmonise the vast and varied datasets that underpin the lending ecosystem.</p>
<div class="lb-trans"><p>随着数字基础的建立，金融机构越来越多地将人工智能视为贷款创新的下一个前沿。然而，一个根本性挑战依然存在：数据质量和结构。没有良好组织和标准化的数据，即使是最先进的人工智能工具也会显得不足。这突显了协调支撑贷款生态系统的庞大而多样化的数据集的持续需求。</p>
</div><p>AI offers the potential to revolutionize credit assessment by enabling more holistic evaluations. Rather than relying solely on traditional metrics like historical financial data and collateral, AI-powered analytics can incorporate alternative data sources to build a richer, more accurate picture of creditworthiness. This is particularly valuable for smaller businesses that may lack extensive credit histories. Personalization is also emerging as a key differentiator, with banks and fintechs tailoring lending solutions to match the unique cash flow patterns and operational needs of individual clients.</p>
<div class="lb-trans"><p>人工智能有潜力通过实现更全面的评估来彻底改变信用评估。与其仅依赖历史财务数据和抵押品等传统指标，人工智能驱动的分析可以整合替代数据源，以构建更丰富、更准确的信用 worthiness 图景。这对缺乏广泛信用历史的小型企业尤其有价值。个性化也正在成为一个关键的差异化因素，银行和金融科技公司根据个别客户独特的现金流模式和运营需求量身定制贷款解决方案。</p>
</div><p>In trade finance, AI can help bridge a growing knowledge gap as experienced professionals retire and regulatory complexity increases. This is an area where AI-powered assistants can deliver significant value by providing banking staff with accurate, real-time, and context-aware assistance. These tools help users to navigate documentation, fraud detection, and compliance with greater ease and accuracy. These tools simplify traditionally complex processes, reducing reliance on specialized experience.</p>
<div class="lb-trans"><p>在贸易融资中，人工智能可以帮助弥补随着经验丰富的专业人士退休和监管复杂性增加而产生的知识差距。这是一个人工智能助手可以提供显著价值的领域，通过为银行员工提供准确、实时和上下文感知的支持。这些工具帮助用户更轻松和准确地处理文档、欺诈检测和合规性。这些工具简化了传统上复杂的流程，减少了对专业经验的依赖。</p>
</div><p>By automating repetitive tasks, AI is set to enhance productivity while freeing staff to focus on relationship-building and revenue-generating activities. Advanced machine learning models will improve credit risk assessment and fraud detection with unprecedented precision, while real-time analytics will empower banks to respond dynamically to customer needs.</p>
<div class="lb-trans"><p>通过自动化重复性任务，人工智能将提升生产力，同时让员工专注于建立关系和创造收入的活动。先进的机器学习模型将以前所未有的精确度改善信用风险评估和欺诈检测，而实时分析将使银行能够动态响应客户需求。</p>
</div><p>To fully unlock AI’s potential, financial institutions must embed it into core operations – not as a bolt-on but as a fundamental component in their lending infrastructure. This requires a culture of innovation, a commitment to data excellence, and a strategic approach to integration.</p>
<div class="lb-trans"><p>要充分释放人工智能的潜力，金融机构必须将其嵌入核心运营中——不仅仅是作为附加组件，而是作为其贷款基础设施的基本组成部分。这需要创新文化、对数据卓越的承诺以及战略整合的方法。</p>
</div><p><strong>3. Building Ecosystems and Partnerships For Future-Ready Lending</strong></p>
<div class="lb-trans"><p><strong>3. 建立生态系统和合作伙伴关系以实现未来准备的贷款</strong></p>
</div><p>While internal innovation remains essential, the future of lending will be shaped by collaboration beyond organizational boundaries. To remain competitive, financial institutions must embrace an ecosystem mindset, partnering across the industry to integrate digital solutions and data flows with core lending platforms. This approach lays the foundation for sustained innovation, agility, and growth.</p>
<div class="lb-trans"><p>虽然内部创新仍然至关重要，但贷款的未来将由超越组织边界的合作来塑造。为了保持竞争力，金融机构必须拥抱生态系统思维，跨行业合作，将数字解决方案和数据流与核心贷款平台整合。这种方法为持续的创新、灵活性和增长奠定了基础。</p>
</div><p>As other financial sectors rapidly advance, lending must accelerate its technological evolution. While asset classes like cash, deposits and bonds have already embraced digitization and tokenization, many lending operations lag. Without modernizing and the adoption of technologies like blockchain, lenders risk losing sponsors and borrowers to more efficient, tech-enabled markets. In trade finance, especially, blockchain platforms are poised to revolutionize traditional processes, creating unprecedented speed, efficiency, and transparency.</p>
<div class="lb-trans"><p>随着其他金融领域迅速发展，贷款必须加速其技术演变。虽然现金、存款和债券等资产类别已经接受了数字化和代币化，但许多贷款操作仍滞后。如果不进行现代化和采用区块链等技术，贷款机构将面临失去赞助商和借款者的风险，转向更高效、技术驱动的市场。尤其在贸易融资领域，区块链平台有望彻底改变传统流程，创造前所未有的速度、效率和透明度。</p>
</div><p>Thriving in this collaborative environment requires more than just technological investment – it demands investment in human capital. As AI becomes embedded in lending operations, institutions must develop the skills to manage, supervise, and collaborate with intelligent systems. Building future-ready teams is just as critical as building future-ready tech.</p>
<div class="lb-trans"><p>在这种协作环境中蓬勃发展不仅需要技术投资——还需要对人力资本的投资。随着人工智能嵌入贷款操作，机构必须发展管理、监督和与智能系统协作的技能。建立未来准备的团队与建立未来准备的技术同样重要。</p>
</div><p>By fostering both technological partnerships and cultivating talent, forward-thinking lenders can create resilient, adaptive ecosystems – ready not just for 2026, but for the long-term future of lending.</p>
<div class="lb-trans"><p>通过促进技术合作伙伴关系和培养人才，前瞻性的贷款机构可以创建具有韧性和适应性的生态系统——不仅为 2026 年做好准备，还为贷款的长期未来做好准备。</p>
</div><hr/>
<div class="lb-trans"><hr/>
</div><p><em><strong>Andrew Bateman</strong> is responsible for the leadership and growth of Finastra’s Lending Business Unit. With almost 30 years in financial services, he guides global teams in modernizing technologies through technical agility, supporting customers’ growth agendas. Previously, he has held positions across the globe with FIS and SunGard (later acquired by FIS). His career in financial technology began at Rolls-Royce, where the implementation of a new treasury system led to a role at GIS/KPMG Information Solutions (later acquired by SunGard). His path has seen him live and work in Asia, South America, the US, and the UK. He holds a Bachelor’s degree in Physics from the University of Nottingham and also has qualifications from the Association of Corporate Treasurers.</em></p>
<div class="lb-trans"><p><em><strong>安德鲁·巴特曼</strong> 负责 Finastra 贷款业务部门的领导和发展。在金融服务领域拥有近 30 年的经验，他指导全球团队通过技术灵活性现代化技术，支持客户的增长议程。此前，他曾在 FIS 和 SunGard（后被 FIS 收购）担任全球职位。他的金融科技职业生涯始于劳斯莱斯，在那里实施新财务系统后，他进入了 GIS/KPMG 信息解决方案（后被 SunGard 收购）。他的职业生涯使他在亚洲、南美、美国和英国生活和工作。他拥有诺丁汉大学的物理学学士学位，并且还获得了企业财务管理协会的资格。</em></p>
</div>

影响 2026 年贷款未来的关键趋势