Hyper-personalisation in Banking. A New Standard for Customer Relations

At Altkom Software, we implement a hyper-personalisation approach that goes far beyond classical segmentation. In this article, we show how it can be effectively applied in practice. Based on our experience from deploying the Altkom Digital Banking Platform, we present concrete mechanisms that allow banks to transform data into personalised, contextual customer experiences – in real time and at scale.

Why Traditional Personalisation No Longer Meets Customer Needs 

The modern customer – whether individual or corporate – expects a bank to not only know them but to understand their context and anticipate their future needs. 

Meanwhile, most banks are still applying a simplified approach: using the customer’s first name in an email, sending a payment reminder or delivering generic offers to thousands of customers. From the user’s perspective, this is nothing more than informational noise that fails to consider individual customer needs and quickly ends up in the SPAM folder. 

For a bank customer, the digital experience is what really matters – and it is primarily this experience that determines their loyalty, not the interest rate on a loan. Today’s customer expects: 

• personalised recommendations in real time, 

• proactive suggestions based on their behaviour, 

• communication that makes sense instead of mass campaigns. 

The lack of true personalisation is not just a missed opportunity – it poses a tangible risk of losing the customer to smaller, more agile fintech companies that offer a modern, contextual approach. 

Hyper-personalisation – What Does It Really Mean from a Business Perspective? 

Hyper-personalisation, powered by new technologies, is the foundation of a modern customer-centric strategy – directly impacting revenue growth, loyalty and operational efficiency. 

In practice, this means: 

• real-time action – the system analyses customer behaviour (e.g. transactions, logins, clicks) and instantly reacts with an offer or recommendation, 

• contextual offers – e.g. when a customer receives their salary, the system suggests a tailored deposit or savings plan, based on their profile and behavioural history, 

• dynamic user journeys – the banking app looks and behaves differently for a young adult than it does for a business owner or a retiree – because needs vary and the experience should support each customer’s goals, 

• proactive communication over reactive service – the system identifies needs and initiates contact: “We noticed frequent transfers to the UK – would you like to use a cheaper currency transfer option?”.

What’s Blocking Hyper-personalisation in Banking and How to Fix It? 

Implementing hyper-personalisation isn’t a single project – it’s a shift in thinking about data, technology and customer service. Banks that want to move from segmentation to real-time individualised approaches must overcome several technological and organisational hurdles. 

Challenge: Dispersed Data and an Incomplete View of the Customer

Many banks are still operating in environments based on information silos. Customer data is dispersed across transactional systems, CRM platforms, call centres or marketing tools. The lack of a unified view limits the effectiveness of recommendations, hinders personalisation and slows decision-making processes. 

Solution: Data Integration and Full Customer Context 

Breaking down data silos requires an approach that enables real-time synchronisation of information and creates a unified customer view – known as Customer 360. A key component here can be an integration platform based on APIs and event-driven architecture. 

One such solution is the Altkom Digital Banking Platform, which connects dispersed data sources, ensuring their consistency and availability where they are needed – both in digital channels and in advisors’ workflows. Thanks to integration (e.g. using Camunda and API Gateway), it becomes possible to: 

• build a unified customer profile, 

• accelerate operational and marketing responses, 

• launch contextual recommendations and real-time personalisation. 

Implementing such an architecture is not just about improving data flow – it forms the foundation for hyper-personalisation, process automation and a better customer experience.

Challenge: Low Quality of Transactional Data 

Transactional data is one of the most valuable sources of knowledge about the customer, but in practice, it is often incomplete, inconsistent or incorrect. The lack of standards, dispersed sources and manual processes make such data unsuitable for advanced analytics, personalisation or effective fraud detection.

Solution: Cleaned Data as the Foundation for Effective Decisions 

To make transactional data truly fuel hyper-personalisation mechanisms and scoring models, it must be complete, trustworthy and up to date. An audit of data quality combined with the implementation of processes for cleansing, standardisation and validation could be the solution – preferably using AI-based tools. 

In this area, the Altkom Digital Banking Platform provides support through its AI Centre module, powered by artificial intelligence, which:

• automatically detects and classifies irregularities (e.g. incorrect MCC codes, missing descriptions, unreadable fields), 

• suggests corrections or implements them in real time, 

• learns from business rules and data context. 

Such processed data becomes a solid foundation for predictive models, accurate offers and automated fraud prevention mechanisms. The result? Better decisions, fewer errors and greater trust in data across the organisation.

Challenge: Lack of Offer Personalisation and Low Campaign Effectiveness 

Many financial institutions still base their marketing communication on a mass approach: one offer sent to the entire customer base, regardless of their needs, behaviours or context. This model not only reduces campaign effectiveness but also negatively affects the customer experience, as customers will increasingly ignore irrelevant messages.

Solution: Relevant Communication Based on Data and Context 

Effective campaigns require a shift from universal messages to personalised recommendations created in real time. The key is to use transactional, behavioural and contextual data to precisely match offers – both via digital channels and through direct contact with an advisor. 

The Altkom Digital Banking Platform supports this process with built-in analytical and recommendation modules. Integrated artificial intelligence assists advisors in real time by suggesting the best offer based on the customer’s history, activities and current context. 

This combination of advanced analytics and human insight increases the accuracy of communication, improves conversion rates and helps build lasting relationships with customers. 

Challenge: Limited Segmentation and Lack of Behaviour Analysis 

Many banks still segment customers based on simple demographic data: age, place of residence or income level. Such an approach does not reflect customers’ actual needs or behaviours. As a result, communication and offers are often irrelevant, and opportunities to build loyalty remain unused.

Solution: Segmentation Based on Actual Behaviour 

Modern hyper-personalisation requires looking beyond standard criteria. The key is to analyse how customers actually use banking services – how often they log in, which app features they use, what transactions they perform and in what context. 

The Altkom Digital Banking Platform supports this model with a built-in real-time segmentation engine, which enables the creation of microsegments based on current user behaviours. The AI Centre module identifies patterns and suggests the best time and channel for contact, increasing the effectiveness of marketing and operational activities. 

Thanks to a behavioural approach, the bank gains the ability to conduct relevant, dynamic communication that translates into higher engagement and better sales performance. 

Challenge: Difficulties in Customer Retention 

Increasing competition and the ease of switching financial service providers have made customer retention one of the greatest challenges in the banking industry. Many institutions still operate reactively – finding out that a customer has left only after they have already resigned, thus missing the opportunity to intervene.

Solution: Risk Prediction and Proactive Measures 

Effective retention requires a shift from a reactive to a predictive approach. It is crucial to detect early signals of potential churn – such as a drop in activity, decreasing account balances, or a lack of interaction with digital channels. 

The Altkom Digital Banking Platform, powered by artificial intelligence, enables real-time analysis of behavioural and transactional data. The system identifies risk patterns and suggests specific actions to advisors, such as: direct contact, a loyalty offer, a change in communication path, or activation of an additional service. 

As a result, the bank gains a real opportunity to respond in advance – before the customer decides to leave. This approach leads to higher retention rates, greater customer lifetime value, and reduced costs of acquiring new users. 

Challenge: Lack of Emotional Engagement from Customers 

Today’s customer expects more than efficient service. They seek a relationship, understanding and the feeling of being treated individually. Standardised, automatic messages and impersonal interactions do not build loyalty. When there is no emotional engagement, the decision to switch banks becomes much easier.

Solution: Personalised Experience and Communication that Builds Relationships

Strong customer relationships are not built through product offers, but through the experiences and the way a financial institution engages in dialogue with its clients. 

The Altkom Digital Banking Platform enables the design of individual user journeys within the banking application. Personalised content – such as financial advice, notifications or contextual reminders – is adapted to the customer’s life situation and emotional context. Advanced artificial intelligence supports advisors in conducting empathetic, value-driven conversations, helping to better tailor tone and messaging to user expectations. 

The result is not only a better experience and a higher level of customer satisfaction but also a genuine competitive advantage in the area of customer experience.

Hyper-personalisation According to Altkom Software 

Effective hyper-personalisation begins with data – but ends with the customer experience. As demonstrated above, the Altkom Digital Banking Platform enables banks to move from product-based communication to building dynamic, contextual relationships with customers in real time. 

A key role is played by the Business Activity Monitoring tool, which allows tracking and analysing user activity across digital channels. This process is supported by other platform components, such as AI Lead Generator, AI LeadBoost, Banking Assist, Senti AI and digital banking portals. 

This makes it possible to: 

• recommend products exactly when the customer needs them, 

• personalise the purchase path and user interface, 

• predict the customer’s next steps and actively respond to their behaviour. 

But hyper-personalisation won’t work without solid data. We support banks in building a foundation for personalisation initiatives – from data audits, through cleansing and standardisation, to the implementation of Data Catalog class tools. 

As a result, the bank gains: 

• full control over data quality and consistency, 

• compliance with regulations (e.g. GDPR, DORA), 

• tools for documenting, classifying and analysing data across the entire organisation. 

We offer a comprehensive approach: we design, implement and maintain analytical and integration solutions that enable data processing for reporting, prediction and advanced personalisation.

Hyper-personalisation in Practice – Experiences from the Middle East 

The Modern Finance Institute analysed the hyper-personalisation approaches of two leading banks in the GCC region: Emirates NBD and Mashreq Bank. The study compared personalisation tactics, technologies used and business outcomes. 

Both banks are consistently implementing advanced solutions based on artificial intelligence, recommendation engines and predictive analytics, combining them with comprehensive customer segmentation strategies. The results? Greater engagement across digital channels, higher satisfaction and retention levels and, above all, a noticeable increase in sales and revenue.

Personalisation of the customer journey is one of the main priorities for banks in the Middle East. Financial institutions in this region are now focused not only on selling products, but above all on creating coherent, omnichannel experiences that respond to the real needs of customers. 

According to data presented in the Global Banking Benchmark Study 2024, Digital Transformation: What’s Next for Middle Eastern Banks?, 40% of banks in the GCC region identify customer journey personalisation – including product recommendations, savings advice and contextual communication – as their most important goal in the area of customer experience. 

This clearly shows that investment in personalisation is not a trend, but a well-considered strategy supporting growth and loyalty. 

Hyper-personalisation in Banking – What It Means for Executives 

Hyper-personalisation is no longer a differentiator – it is becoming the market standard. Banks that want to maintain customer loyalty and increase their revenues must act faster, more accurately and more contextually. 

This requires not only technology, but a shift in approach to data, segmentation and the customer experience. The Altkom Digital Banking Platform supports financial institutions in this transformation, providing specific tools for real-time analysis, recommendation and personalisation. 

The decision is no longer about whether to implement hyper-personalisation, but about how to effectively connect data, processes and the capabilities of new technologies into a coherent development strategy.