For several years, banks have been trying to deliver what customers want – a truly personalized experience that covers engagement with the bank, pricing, offers and deals, and even rewards. But with younger, more tech savvy Gen Z entering the formal banking economy, delivering on the personalization promise has not been easy. As competition heats up, and the global economy continues to face disruption, banks must get their personalization strategies right to win and retain customers and deliver superlative service to ensure customer delight.
The Customer Satisfaction Index
A recent report by Salesforce revealed that only 26 percent of customers were happy with their bank’s customer service speed, and only 27 percent were happy with the effectiveness of the customer service they receive.1 Almost half (47 percent) of respondents expressed dissatisfaction with the lack of continuity in their customer service experience as they had to explain their requirements repeatedly to different personnel.
And despite their efforts to deliver personalized services, only 21 percent of banking customers were fully happy with their financial institutions’ personalization effort. 84 percent of them expected their banks to go beyond mere transactions to be their trusted financial advisors. The younger customers especially, expected their banks to go beyond just their name or current relationship with bank to craft offers and pricing. They expected them to use their data to really understand their needs, offer proactive, value-driven advice, services, and offers. On the bank’s part, relationship managers rarely had the data, insights or inputs they needed to deliver on this customer expectation.
AI-Powered Strategies to Drive Banking Personalization
Banks must put some serious thought to the problem of personalization. They must ramp up their technology strategies to meet the demands of the new banking customer. The significant advancement in Artificial Intelligence (AI) and Machine Learning (ML) can help them accelerate their personalization strategies. In fact, used effectively, AI can help the banking sector grow profits by 9 percent or USD 170 billion by 2028.2 Little wonder then that 60 percent of bank leaders are willing to incur some risk in order to leverage AI and automation.3 44 percent of banks are already using AI to understand their customer needs and customize their experiences.4 The Royal Bank of Canada, for example, has a suite of personalized automation tools called NOMI that gives customers information on their accounts, reminds them about relevant financial activities, and even automatically transfers funds into pre-approved savings accounts.5 The app helped the bank open 250k new savings accounts and NOMI users saved USD225 a month on average. DBS Bank came up with a mobile app that leveraged their in-house AI predictive analytics engine to deliver more than 100 personalized and automated insights to their customers.6
The Future of AI in Banking Personalization
AI technology is evolving at an incredible pace to usher in new era of autonomous performance. Agentic AI is the next frontier in AI evolution and has the potential to completely transform banking as we know it. Agentic AI does not require prompts like generative AI. It uses advanced machine learning, large language models (LLM), NLP, and automation technologies to understand the objectives and context of the problem at hand, and provide complex, detailed, and even proactive responses.
The technology’s ability to support real-time decision making and address complex tasks autonomously holds tremendous potential for improving personalization strategies. It can analyze a customer’s financial habits and patterns in real time, correlate that to their financial goals and risk profile, and provide customized recommendations like investment options or savings plans, or even loan offers. AI agents can detect unusual spends or identify better interest rates and send alerts to the customer in real time. It can track income and expenses and provide personalized budgets. AI agents can analyze customer behavior to go a step beyond the traditional credit score and improve risk assessment strategies. They can then recommend the best loan products, offer custom loan terms, and optimized interest rates.
Agentic AI can help banks improve their customer service operations as well. Banks can implement multilingual voice support services, breaking any language barriers. Customers can ask for help and work through their problems in their own language and take as much time as they need till they get what they need. Agentic AI systems can even offer proactive advice or recommendations to customers. For example, if someone engages with the Agentic AI system to check their balance, the system can take it a step further to analyze their account information and habits to recommend savings plans, or investment options. It can even recommend rewards and discounts, based on pre-determined criteria.
Agentic AI systems can become a valuable component of wealth and investment advisory practices too. AI agents can autonomously monitory market conditions as well as the customer’s risk profile to suggest adjustments in investment portfolios in real time. It can analyze documents to extract tax data and recommend tax saving opportunities and even predict major financial decisions to offer personalized help and guidance when customers need it most. For example, AI agents can track when a customer approaches retirement age and offer proactive retirement planning advisories at the right time.
Preparing for the Future – Managing Risk and Building a Robust Technology Foundation
Agentic AI, like all AI models, relies heavily on customer data. Customers are now more open to the idea of banks accessing their data to improve services, as they are realizing the benefits of AI-powered engagement models. 31 percent of customers believe that their financial services institutions already use AI to manage their relationship, and 65 percent feel that AI will help speed up transactions.7 It is now up to the banks to ensure data privacy and security with some robust cybersecurity strategies. AI can play a crucial role in strengthening their security posture as well.
Of course, before they implement advanced AI strategies like agentic AI, banks need to strengthen their technology foundation. Legacy banking core systems lack the agility and scalability required to deploy advanced technologies like agentic AI. But banks do not need to overhaul or change their legacy cores completely as that is an expensive, time intensive, and highly risky proposition. All they need to do is deploy a powerful cloud-native microservices-based middleware over their legacy cores. This agile, scalable, and robust middleware platform can drive cutting edge AI strategies and help financial institutions transform their personalization game.
Effective personalization is inextricably tied with customer satisfaction and retention, and banks must turbo charge their strategies now to retain their competitive edge. While agentic AI is still a work in progress, banks need to prepare their technology ecosystem to be ready to deploy AI agents. They must also focus on generative and voice AI models to continuously improve personalization efforts and connect with a diverse customer base.
