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Overcoming Data Challenges in FinTech: A Case Study
技术
- 分析与建模 - 机器学习
- 基础设施即服务 (IaaS) - 混合云
适用行业
- 教育
- 零售
用例
- 面部识别
- 租赁金融自动化
服务
- 数据科学服务
- 培训
挑战
在全球金融科技公司中，81% 的公司表示数据是他们最大的技术挑战。这些数据问题分为利用 AI-ML 数据（41% 面临）和连接到客户应用程序和数据系统（40% 面临）。金融科技面临的更多数据问题是安全性（40%）和多云部署（39%）。
由于对客户所需的产品和服务类型以及企业本身缺乏清晰的认识，在利用人工智能数据获得有价值的见解时面临的数据问题的后果是进一步创新面临的困难。无法连接到客户应用程序直接影响用户体验以及向更广泛的客户群提供现有产品的能力。
此外，后果包括无法与现有银行建立合作伙伴关系，以及更严重的缺乏监管合规性。上述两个后果可能会导致生存能力方面的问题，其根源应在其生效之前解决数据问题。
客户
金融科技
关于客户
FinTech 是术语“金融”和“技术”的组合，包括任何使用技术来增强和自动化金融流程、服务和产品的企业。金融科技并不是新事物，但由于在过去十年中的广泛采用和使用而迅速发展和增长，金融科技已经崛起。金融科技的例子包括支付领域的 Venmo、Stripe 和 PayPal 等组织和企业，以及消费银行领域的挑战者银行和 Neo 银行。
为金融科技产品和服务提供动力的技术因项目、部门和应用程序而异，但示例包括机器学习、人工智能、数据科学和区块链，以支持从信用风险评估到自动交易和对冲的一切基金管理。
解决方案
智能数据结构。金融科技公司需要审视他们当前的数据管理策略，以弥合数据孤岛，并借助一种名为 Data Fabric 的新架构方法将它们集成。数据结构访问和转换来自多个数据集的数据，以产生洞察力，使金融科技公司能够更好地了解和服务他们的客户。智能数据结构具有内置的商业智能、分析、自然语言处理和 ML 功能。此外，额外的优势是它不是一种淘汰和替换技术，消除了对预算限制的担忧，并允许遗留数据源与更现代的数据源共存。 44% 的金融科技公司正在考虑实施这项技术，以弥合他们的数据孤岛并利用人工智能数据。
云部署解决方案。投资于有关云的培训和知识，并采取措施构建云优先解决方案，可以缓解金融科技公司在部署到混合云时面临的数据问题。 54% 的金融科技公司正计划实施此类措施以克服他们目前面临的问题。
人工智能的一次性学习模型。一次性学习模型允许计算机从较小的数据集中学习，这些数据集可用于金融科技初创公司经常面临的大量大数据无法访问的情况。 51% 的早期金融科技公司正在考虑使用此类模型来克服大数据的不足。
运营影响
数量效益
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