Personalized Banking Experience with AI
Obviously, banks have continually the newest technology innovations to redefine how customers interact with them over several decades. Furthermore, there are fewer opportunities for face-to-face communications with customers in the age of digital banking. Instead, there are a number of opportunities for AI to recognize customers, provide personalized experiences, and build loyalty by offering suggestions based on customer behavior and history. As a result, banks require to use these advancements at scale to remain relevant in the competitive world. This article will provide you with some main ideas and best-practices for personalized banking experience by using Artificial Intelligence technologies.
Introduction and Overview
In fact, banks have continually the newest technology innovations to redefine how customers interact with them over several decades. Besides, Artificial Intelligence(AI) technologies are increasingly integral to our world, and banks require to use these advancements at scale to remain relevant in the competitive world as well.
Artificial intelligence is the capability of a machine to imitate intelligent human behavior.
Some surveys have showed that using AI technologies could be helpful for boosting revenues through increased personalization of services to customers. Also, it plays an important role to lower costs through productivities generated by higher automation, and diminish errors rates. In other words, AI technologies could potentially unlock $1 trillion of additional value annually.
As you know, personalization in banking is about delivering a valuable service or product to a customer based on personal experiences and historical customer data. As a result, personalization can help banks deliver solutions to their customers before they even realize they have a problem.
Another important point to mention is that digital banking has increased from 49% to 67% due to the pandemic. Banks are well-aware that customers might contact them through various channels, but it is not just only about the channels from a customer’s viewpoint. It is about the relationship with the bank. The customer would love an agent just to know the context of inquiry as soon as possible with minimum questions. More than 50% of bank customers believe personalized services are one of the significant factors for them to have trust in their banks; whereas, just only 35% of traditional banks provide personalization that meets customers’ needs at the proper time and place. Therefore, these investigations prove that banks will require to invest drastically on personalization of services provided for customers in order to keep their trust and loyalty. To achieve this goal, banks have to use data driven AI capabilities in different areas, like conducting micro segmentation of existing customers and prospects by reconsidering AI/ML technologies and approaches.
Personalized Banking with AI
Personalization means being present at the right time with the right offer.
Obviously, there are fewer opportunities for face-to-face communications with customers in the age of digital banking. In contrast, there are a number of opportunities for AI to recognize customers, provide personalized experiences, and build loyalty by offering suggestions based on customer behavior and history. In general, some examples could be mentioned for using AI in banking as follows:
- Smile-to-pay facial scanning to initiate transaction
- Micro-expression analysis with virtual loan officers
- Biometrics to authenticate and authorize.
- Machine learning to detect fraud patterns and cybersecurity attacks
- Conversational bots for basic servicing requests
- Humanoid robots in branches to serve customers
- Machine vision and natural language processing to scan and process documents
- Real-time transaction analysis for risk monitoring
Digital banking is part of the broader context for the move to online banking, where banking services are delivered over the internet. The shift from traditional to digital banking has been gradual and remains ongoing, and is constituted by differing degrees of banking service digitization. Digital banking involves high levels of process automation and web-based services and may include APIs enabling cross-institutional service composition to deliver banking products and provide transactions.
Basically, AI can use transactional models and other data sources to help banks understand customer behavior to improve their experience. This means AI can make it easier for banks to analyze and understand customer preferences. For instance, if a customer uses their bank card for booking a flight, AI will suggest relevant information, personalized and contextual offers, to that customer that have been linked to the card, like suggesting Uber to get to the airport or Airbnb to be at destination. In other words, a large variety of information about user behavior allows banks to notice what customers really want at any given moment and what they are willing to pay for. For example, banks can offer personalized loans after analyzing all possible risks based on the client behavior and history. ML-based systems can look at different patterns and behaviors to consider whether a customer with limited credit history makes a good credit customer. Hence, optimizing the customer footprint helps banks identify subtle interests in customer behavior and create much more customized experience for each client in reality.
Machine Learning(ML) is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
Essentially, machine learning-based budgeting tools integrated into mobile banking apps can help customers make better financial decisions. A machine learning algorithm can identify user spending patterns in order to offer some useful tips based on the history of transactions. For instance, TransUnion bank has partnered with ML-powered budgeting app Mint to provide its customers with tips on improving their credit scores. Another example is Tally as a fintech company helps customers pay off their credit card debts more quickly by offering which to pay first, how much to pay, and when.
Furthermore, AI can increase productivity by allowing contact center employees to react in a more advisory capacity. This means banks can use chatbots, which are supported by AI for managing and answering common questions automatically. Thus, this can free up customer service agents to spend more time for working on more complicated issues.
Lastly, even though billions of dollars spent on changing the bank technology initiatives annually, few banks have succeeded in utilizing and scaling AI technologies throughout the organization effectively. To perform this task successfully, some obstacles and challenges should be addressed. The most important one is lack of a clear strategy for AI.
Reconsidering AI/ML Technologies and Approaches in Banks
As a matter of fact, banks have to reconsider their AI/ML approach and invest on some crucial steps for implementing these technologies for successful results as follows:
Step 1) Developing an AI strategy: Shifting from just using AI to becoming an AI insights–driven organization, and tackling how to execute these advanced technologies.
In addition, executives must use the strategy phase not only to specify what requirements to be done, but also to address how it must be performed in practice. In short, they should focus on an appropriate plan that defines, for instance, how AI culture can be ingrained across the organization or AI applications can be embedded into existing processes.
Step 2) Specifying a use-case driven process: Moving from “do not miss the hype” to developing value-driven use cases. This means they must focus on value- driven implementation, as well as divers AI capabilities. So, determining relevant use cases and prioritizing them into a road map can be useful for banks stay focused during implementation, and be helpful to achieve the previous goals defined over the strategy phase. Moreover, AI offers a lot of different technologies and capabilities, which can provide a broad range of use cases to extend typical features of applications, like call center automation. Therefore, banks need much time to gather the requisite experience and information about the advantages and obstacles of each capability for finding the most proper AI solutions to certain use cases.
Step 3) Having experiences with prototypes: This aims to build a foundation and prepare for scalable deployment and strategic alignment.
Strategic alignment is a process that ensures an organization’s structure, use of resources support its strategy. “In its simplest form, organizational strategic alignment is lining up a business’ strategy with its culture.
In general, the main reason of a prototype is to show if it is worth continuing investing more time and resources in a technology solution or not. Thus, banks usually focus on a short-term use case scenario before they want to start the project. Nevertheless, AI use cases need prototypes to be scaled up to the enterprise with various areas, such as up-front planning, timelines, and, business goals. In short, unlike considering a prototype as testing an isolated functionality, banks should consider and design a prototype in the context of a whole ecosystem. All in all, strategic alignment with wider AI strategy can boost the opportunities that prototypes will be efficient and successful in reality.
Step 4) Building with confidence: From reactive to proactive focus on risks and ethics, and from proving a concept to laying a foundation. This means instead of having risk, compliance, legal, and ethical reviews in the last phase of an implementation life cycle, when banks want to implement AI, these reviews should occur early in this process due to the importance of trust in this area.
Organizations ready to embrace AI must start by putting trust at the center.
Another significant point in this section is that even though some foundational details are still significant in AI implementations, innovations should not be restricted for diminishing vendor dependency maintenance costs.
Step 5) Scaling for enterprise deployment: Shifting from “nice-to-have” to “must-have” AI talent and from rigid to adaptive technology and operating models. This means to deal with challenges in AI deployment, the leadership team needs to build a centralized talent pool including different key roles, such as data scientist, user experience designer, data engineering manager, analytics visualization developer, and analytics manager. Furthermore, some banks have no required flexibility to deploy AI at scale. So, using AI models and integrating them with current processes by following optimizing rules could be a useful best-practice for scaling AI technologies in banks.
Step 6) Driving sustainable results: Moving from end of implementation to beginning of discovery and from using to improving capabilities. Initially, the ultimate goals of a common software deployment phase are to focus on maintenance of the system and make slight improvements to the system. In contrast, the main goals for AI (after deployment) should focus on continuously learning how models react to different inputs and finding effective methods to enhance outputs. As a result, these approaches can be applied to AI systems development across the entire organization. So, banks can play a role to identify and use methods to boost existing AI applications and technologies in order to add value in this area.
Consequently, in addition to these steps, in general, to address customers’ rising expectations and overcome obstacles in the AI-powered digital era, the AI-first bank will offer main ideas and experiences that are:
Intelligent: recommending actions, anticipating, and automating key decisions.
Personalized: relevant and timely based on a detailed understanding of customers’ past behavior and context.
Omnichannel: seamlessly spanning the physical and online contexts over multiple devices and delivering a consistent experience.
Therefore, banking capabilities with relevant products and services beyond banking can be mixed in this way. For example, the following picture shows how a bank could engage a medium-size customer during the day:
In Conclusion
Basically, Artificial Intelligence(AI) technologies are increasingly integral to our world, and banks require to apply these advancements at scale to remain relevant in the competitive world as well. In fact, there are many opportunities for AI to recognize customers, provide personalized experiences, and build loyalty by offering suggestions based on customer behavior and history. This essay discussed some main ideas and best-practices for personalized banking experience by applying AI technologies based on some documents and resources.
