Customer expectations of their banking platform are evolving rapidly, especially when it comes to personal finances. As fintech firms continue to innovate, one trend is becoming very apparent: hyper-personalisation.
This tailored approach to banking services leverages advanced technologies like AI and data analytics to deliver customised experiences that meet the unique needs of individual customers. But what exactly is hyper-personalisation, and why is it the next big thing in banking?
Understanding hyper-personalisation
Traditional personalisation strategies often relied on broad customer segmentation, offering highly generalised services to specific demographic groups. However, today's consumers expect more nuanced interactions, with every financial journey being unique and somewhat different. According to research by McKinsey & Company, 71% of customers anticipate personalised interactions from companies, and 76% express frustration when such experiences are lacking.
Hyper-personalisation addresses these expectations by utilising AI to analyse high amounts of data - from transaction histories to real-time digital behaviors - enabling banks to anticipate customer needs and offer timely, relevant solutions before many situations even take place. Things like predictive analytics can identify when a customer is likely to require a particular financial product, allowing banks to proactively present tailored offerings and also protect clients from potential fraud or harmful digital attacks.
For more information about contextual banking, read our article.
Several factors contribute to the growing emphasis on highly personalised digital banking:
Technological Advancements
The main ingredient of hyper-personalisation is artificial intelligence (AI), machine learning (ML), and real-time data analytics. These technologies allow financial institutions to process high amounts of structured and unstructured data, generating precise, customer-centric insights. Of course for this, accurate transactional data are at most priority, being handled by providers like Tapix. Key takeaways:
- AI-Driven Insights: Advanced predictive analytics models enable banks to anticipate customer needs before they arise. For example, AI can detect a pattern in a customer’s transactions that suggests they are planning a major purchase (like a home or car), allowing the bank to proactively offer relevant financing options.
- Real-Time Data Processing: Unlike traditional batch processing, real-time data pipelines continuously track customer interactions across multiple digital touchpoints, enabling banks to deliver immediate, contextually relevant offers.
- Natural Language Processing (NLP): AI-powered chatbots and virtual assistants can personalise financial advice even further based on customer queries, transaction history, and goals.
- Edge Computing & Cloud Adoption: With more banks migrating to the cloud, processing speeds for hyper-personalised recommendations have improved significantly, reducing latency and enhancing user experiences.
Consumer Expectations
As digital-native generations, Millennials and Gen Z most notably, become the dominant banking demographic, their expectations are shaped by hyper-personalised experiences from companies like Netflix, Amazon, and Spotify. It's a new age for digital banking as well, not just consumerism.
- On-Demand & Frictionless Banking: Customers expect a seamless, omnichannel experience across banking apps, websites, and chatbots, with zero friction between touchpoints. Big topic in this regard is open banking.
- Proactive Engagement Over Reactive Services: Unlike legacy banking models where customers seek out services, modern customers expect banks to anticipate their needs and make proactive suggestions - without feeling invasive.
- Micro-Moments & Real-Time Personalisation: Banks must capture real-time intent signals and act on them immediately. For instance, if a user browses mortgage rates on a bank’s website, AI should trigger a personalised mortgage offer within minutes.
Competitive Environment
The rise of fintech disruptors and digital only banks is pushing traditional banks to adopt hyper-personalisation or risk losing market share.
- Fintechs & Challenger Banks Leading the Way: Neobanks like Monzo, Revolut, and bunq have built their entire value proposition on data-driven personalisation, offering customers real-time spending insights, automated savings, and hyper-personalised lending options.
- Embedded Finance & Open Banking: With open banking regulations (like PSD2 in Europe), third-party fintech providers can now access customer financial data (with consent), allowing them to offer more personalised services than traditional banks.
- Big Tech Enters the Arena: Companies like Apple (Apple Pay & Apple Card), Google (Google Pay), and Amazon (Amazon Lending) are setting new CX standards in financial services. Traditional banks must match this level of user experience to remain competitive.
The impact of hyper-personalisation on key performance indicators (KPIs) cannot be overstated. According to recent studies, banks that embrace hyper-personalisation experience:
The risks of ignoring the personal touch
Banking is competitive, and so are user’s demands for personal approach. Everyone wants to be heard and financial sectors have always been notoriously “cold”. If users don’t feel they can trust their bank, it can have longterm consequences.
- Customer attrition: Studies suggest that banks could lose up to 25% of their customer base if they fail to deliver personalized services. Everyone is different and banks need to realize their financials are a reflection of that.
- Missed opportunities: Without insights into customer preferences, banks may struggle to identify and capitalize on revenue-generating opportunities. You cannot change what you don’t know.
- Diminished competitiveness: As competitors embrace hyper-personalisation, banks that lag behind risk losing market share and relevance. Especially in the highly mobile and adaptable market of neo-banks.
Success stories: Who's getting it right?
Of course, there are always those who understand the need for constant innovation and adaptable strategy, following the needs of their clients and their life journeys through the financial sector.
- JP Morgan Chase: Utilises AI-powered chatbots to deliver personalised financial advice and assistance to customers, helping them navigate the nuances of healthy financial habits.
- DBS Bank: Leverages predictive analytics to anticipate customer needs and offer tailored product recommendations in real-time, basically creating a personal advisor in your pocket.
- Ally Bank: Employs machine learning algorithms to customise the user experience, from personalised dashboards to targeted promotional offers.
Keys to effective hyper-personalisation
There is much that can be done “on the go”, but it is important to understand that all innovations need to stand on key pillars of hyper-personalisation. What you can do?
- Invest in data infrastructure: Build robust data infrastructure to collect, process, and analyse vast amounts of customer data. One of the most important aspects is keeping track of data quality and a dependable provider for data enrichment to use the power of data to its full potential.
- Harness AI and machine learning: Utilise AI and machine learning algorithms to derive actionable insights and deliver personalised experiences at scale. There is much that can be automated, which will free your time and resources to focus on next steps.
- Focus on transparency and trust: Prioritise transparency in data usage and ensure that customers have control over their personal information. Safety and trust are the cornerstones for any successful banking platform, and advances in digital security make sure all the data can be safe and sound.
- Iterate and innovate: Continuously refine and optimise personalisation in banking strategies based on evolving customer preferences and market dynamics.
Conclusion
Banking is becoming not only more digital, but also customer-centric, and hyper-personalisation stands out as the key element of this transition. The banks that understand and implement enriched data and their smart use within their banking platforms will not only stay ahead in the competition but will also win the lasting trust and loyalty of their customers. Personalisation in banking is no longer an option; it's the cornerstone of a successful and thriving banking future.
