Banking Trends 2022: Transforming Customer Experience

Customer habits have been changing over the last decade, with customers expecting frictionless and instant access to their banking needs. Banking customers have prioritized digital self-service via mobile and web over branch or phone banking, with the COVID-19 pandemic only accelerating this trend. Although digital channels are rising in importance, the ability to converge channels is equally, if not more important. This lets customers engage through their preferred channel without the bank or agent losing context of the interaction. Furthermore, Deloitte’s Digital Banking Consumer Survey highlighted this, finding that consumers prefer digital channels for transactional banking activities, yet still prefer branches for product applications and advice.

Beyond frictionless service, customers now expect their banks to understand their specific situation and provide them with personalized offers at their point of need. This was demonstrated in the Bain study that highlighted how 50% of customers would buy a product from their bank through a personalized offer. Moreover, BCG estimated that banks that have focused on hyper-personalized approaches could derive $300m in revenue growth for every $100b in assets.

Banks are adopting three approaches to deliver superior customer service. Each one builds on the next to establish a reinforcement loop that continually enriches the insights that the bank collects and in turn enriches the customer experience.

Approaches adopted by banks to improve customer experience

Digital Customer Engagement and Service
Banks have redesigned their experiences around the customer journey. The focus is on self-service, providing intuitive workflows for their customers to initiate and change payments, dispute transactions, and adjust account information inside of the application. Banks have started incorporating smart chatbot technology to better identify why the customer is initially reaching out, as well as to resolve the enquiry without requiring an agent, thereby reducing the cost.

For customers requiring live support, meeting them within their medium and channel of choice is crucial. Customers are leaning more toward mobile chat and video conferencing over traditional phone or branch interactions. This was highlighted in the Citizens’ Banking Experience Survey, where 90% of consumers and 86% of businesses were found to use digital banking channels. But regardless of the channel, banks must retain the context of the customer’s needs, alongside the authentication done in any channel. This will reduce friction in the interaction, including using real-time data assets to proactively provide the likely reasons for the call, as well as the relevant steps to quickly and empathetically solve the customer’s problem.

Throughout these interactions, banks are starting to collect real-time data, from transcribing call logs to assessing interaction sentiments. This is enabling banks to develop a more complete picture of the customer, and better train their service-center staff.

To address these digital needs, our banking customers leverage a combination of our services, including Amazon Connect and Amazon Lex. These were initially built to support Amazon.com customers globally, but now they’re being used by numerous AWS customers. This is coupled with purpose-built data capabilities that let a bank track real-time customer sentiment, as well as capture and store customer events to help inform the agent of the reason for the call and the customer’s disposition.

Contextualized or Embedded Finance
Beyond interacting with the banks, customers expect finance options to be built into their buying process, rather than occupying an additional, independent step. For example, customers generally don’t dream of obtaining a car loan, rather they dream of buying a car, with financing being the means to achieve it. But more importantly, Embedded Finance isn’t just a consumer trend. Accenture estimates that embedded banking for SMEs could capture approximately one quarter of the SME banking market by 2025, representing a value of nearly $124 billion.

To deliver on these capabilities, banks are building embedded finance solutions. These allow them to partner with other organizations to build finance and banking services into traditional customer journeys. To meet the real-time expectations of customers in this journey, banks are exposing their banking services through Application Program Interfaces (APIs), as well as developing real-time middle office processing capabilities such as credit adjudication.

Given the highly variable nature of these services, many AWS customers are building these services using cloud capabilities. This lets them dynamically scale their services to meet peak demand, rather than purchase hardware for the peak, or worse, have degraded services during high-demand periods.

Hyper-Personalized Banking

Banks are using the data assets that they collect across the customer relationship to better understand the customer’s current state, including what they might need next and how best to serve them. They leverage these insights to suggest recommendations to the customer that will help optimize their earnings, avoid fees, or improve cash flows. Moreover, they use these insights to customize and pre-approve products that help the individual customer, instead of pushing static products through ‘next best offer’ mechanisms.

Furthermore, banks are leveraging these insights to trigger targeted offers to customers in their digital customer experience, either through ad placement or notifications. Often, the more effective model is to leverage front-office staff in relationship roles to better target the messaging to their customer at the right time and in the right context. Arming relationship managers with actionable insights can improve customer intimacy and drive referrals.

AWS banking customers build on the aforementioned data capabilities, thereby creating artificial intelligence and machine learning (AI/ML) models using services such as Amazon Personalize and Amazon SageMaker to integrate the vast amounts of structured and unstructured data. Then, they can generate recommendations to customers via their preferred channel using solutions such as Amazon Pinpoint, as well as generate insights for their agents through Amazon Connect and dashboards.

How are banks doing this on AWS?

Our banking customers are using native AWS services to improve the customer experience. In some cases, they use AWS partners that offer innovative customer experience solutions. In the following section, we’ll provide some applications of customer experience transformation that leverages AWS services.

Figure 1: Omnichannel customer experience platform in banking

Customer support and engagement

AWS banking customers are using services such as Amazon Connect, Amazon Lex, and Amazon Polly to create interactive chatbots and provide live agents to talk to their customers. Services such as Amazon Pinpoint and Amazon Simple Notification Services (Amazon SNS) can be used to reach out to customers on their channels of choice.

Real-time customer support

Contact Lens for Amazon Connect helps an agent follow the sentiment and trends of customer conversation in real-time to identify crucial product feedback. Apart from tracking agent compliance during their customer conversations, supervisors can also conduct quick full-text searches on all of the transcripts to quickly troubleshoot customer issues. Using real-time, ML-powered analytics, supervisors can also get alerts for issues during live customer calls, and deliver coaching to agents while calls are in progress.

AI/ML services

AWS pre-trained AI services provide ready-made intelligence for your applications and workflows. Our customers use Amazon Textract to do OCR processing, Amazon Rekognition to extract information and insights from images and videos, and Amazon Personalize to build applications that can deliver various personalization experiences, including product recommendations and customized direct marketing.

For customers who want to build, train, and deploy their own ML models for any use case, they can use Amazon Sagemaker, our fully-managed infrastructure, tools, and platform for ML.

Data analytics for your contact center

Data is essential to driving the success of a contact center. Having a streamlined data lake solution built on Amazon Simple Storage Service (Amazon S3) helps our customers deliver a personalized experience to their customers. Customers looking for best practices when building a contact center data lake can reference this whitepaper.

For any questions, reach out to your AWS Account manager who can drive this conversation forward for you, or connect with our AWS sales support team here.