凯捷：AI转变金融服务客户体验( 英文44 页)

Smart Money

How to drive AI at scale to transform the financial services customer experience

Executive summary 

Financial services firms are accelerating the adoption of AI for customer interactions 

Financial services firms understand the importance of making customer interactions AI-enabled and are taking steps to achieve this goal. The use of AI in customer interactions has grown significantly in the past three years in the industry. A number of leading financial services firms are responding to this demand with innovative solutions. 

However, half of banking and insurance customers 

(49%) feel that the value they received from their AI interactions was non-existent or less than expected. This perhaps reflects the fact that the novelty factor of traditional industry innovations – such as basic chatbots – may have worn off. Today, people expect chatbots to be as advanced as those available from technology firms such as Google, and today only 31% say chatbots are their preferred AI-driven way to interact with financial services institutions compared to 41% for voice assistants.

Organizations derive significant benefits from AI’s use in customer interactions

While AI has not lived up to customers’ expectations, it offers a range of benefits for banks and insurers. Financial services firms have reduced their cost of operations by 13% and have increased the revenue per customer by 10% after deploying AI in customer-facing functions. One example is the AI-powered recommendation engine “ADA” (algorithmic decision agent), from Aviva which ranks the products that customers are most likely to buy.¹

The industry comes last when it comes to scaling of AI-based customer interactions

While the level of organizational benefits from AI-enabled interactions is one of the highest for this industry compared to other sectors, financial services performs worst in terms of achieving scaled implementation of AI solutions. The top challenges standing in the way of achieving scale are: leadership and organizational resistance; difficulty in identifying the right use cases to scale; the long gestation periods for implementation, coupled with lack of data management; and lack of trust for high-priced interactions.

Multiple factors are critical to accelerating AI use cases to scale

Drawing on this research, best practice examples, and our own experience working with clients on these issues, we have outlined six recommendations for achieving scale with AI:

• Invest in value-driven AI to transform the customer experience

• Create trust-based and ethical AI governance approaches to drive broad-based customer adoption

• Deliver an AI experience that takes into account

“signature moments” that require empathy and emotion

• Set up the technology foundation required for an AI-enabled customer engagement

• Put in place senior leadership roles for AI to accelerate adoption

• Educate customers on what AI can do for them and make AI systems explainable and transparent.

聪明的钱

如何大规模推动AI转变金融服务客户体验

执行摘要

金融服务公司正在加速采用AI进行客户互动

金融服务公司了解使客户交互具有AI功能的重要性，并正在采取步骤实现这一目标。在过去的三年中，在客户交互中使用AI的情况已显着增长。许多领先的金融服务公司正在通过创新的解决方案来满足这一需求。

但是，一半的银行和保险客户

（49％）认为他们从AI互动中获得的价值不存在或低于预期。这也许反映出这样一个事实，即传统行业创新（例如基本的聊天机器人）的新颖性因素可能已经消失。如今，人们期望聊天机器人与Google等技术公司提供的聊天机器人一样先进。如今，只有31％的人说聊天机器人是他们首选的AI驱动的与金融服务机构进行交互的方式，而语音助手是41％。

组织可以通过AI在客户交互中的使用而受益匪浅

尽管AI没有达到客户的期望，但它为银行和保险公司带来了一系列好处。在将AI部署到面向客户的功能后，金融服务公司的运营成本降低了13％，每位客户的收入增长了10％。一个例子是来自Aviva的AI支持的推荐引擎``ADA''（算法决策代理），它对客户最有可能购买的产品进行排名.1

扩展基于AI的客户互动方面，金融行业排名倒数

尽管与其他行业相比，启用AI的交互所带来的组织收益水平是该行业最高的水平之一，但就实现大规模实施AI解决方案而言，金融服务的表现最差。 达到规模目标的最大挑战是：领导和组织的抵制； 难以确定合适的用例以进行扩展； 实施的妊娠期长，加上缺乏数据管理； 缺乏对高价互动的信任。

多种因素对于加速AI用例的规模扩展至关重要

借助这项研究，最佳实践示例以及我们与客户在这些问题上的合作经验，我们概述了六项关于通过AI实现规模化的建议：

•投资价值驱动的人工智能，以改变客户体验

•创建基于信任和道德的AI治理方法，以推动广泛的客户采用

•提供考虑了AI的体验

需要同情和情感的“签名时刻”

•建立支持AI的客户参与所需的技术基础

•设立AI的高级领导职位以加速采用

•向客户介绍AI可以为他们做些什么，并使AI系统可解释和透明。

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