Personalisation in Financial Services: Getting it Right

February 08, 2023

7 min read

Dasha Fomin

personalisation in financial services


Personalisation is ubiquitous: it’s on Netflix’s homepage, Spotify’s weekly recommendations, or YouTube’s algorithmically-devised watchlist. Today’s consumers get personalised experiences everywhere they go. While the lack of such creates frustration.

And as brand loyalty becomes more elusive, getting personalisation right is crucial. According to SmarterHQ, 72% of consumers state that they prefer personalised messaging. Research says, millennial brand loyalty increases by 28% if they receive personalised messaging. Here, we explore how personalisation transforms the fintech industry in general as well as credits and collections in particular.

Personalisation in fintech: how it works

According to McKinsey, personalisation in finance can “reduce acquisition costs by as much as 50%, lift revenues by 5 to 15%, and increase the efficiency of marketing spend by 10 to 30%.” So, what is personalisation? It’s the consumer-centred approach to digital finance that relies on behavioural data science and Artificial Intelligence (AI) to ensure consumers get offered only relatable products and services. Leveraging real-time data, personalisation generates insights into consumer needs offering users the comprehensive digital experience they want.


It’s not just technology that makes financial services more personal. But, APIs (application programming interfaces) are a critical part of getting customers to trust financial organisations with their personal financial data. A bank or other financial services institution can use one of three overarching types of personalisation:

Predictive personalisation

Using historical data, it anticipates the customer’s needs and desires. This method allows companies to better manage users in accordance with their business’s goals.

Real-time personalisation

To offer such experiences businesses leverage current and historical data in real-time. This type of personalisation is perfect for recommending relevant products to website visitors. This approach usually results in higher engagement and conversion.

ML-Powered Personalisation

Backed by machine learning algorithms, AI-driven automation enables businesses to make viable decisions on customer interaction.


Benefits of personalisation

A recent report says that about 75% of bank and credit union customers want to continue using digital banking services as they were before the epidemic. Additionally, 44.8% of end users utilise mobile and online banking “most” or “all” of the time. So, adding a personal touch to digital services can enable a number of advantages in addition to revenue increases, such as:

Cost savings

Hybrid digital solutions can improve cost reductions and open up new revenue streams while delivering a richer, more satisfying customer experience. Additionally, consumers and stakeholders can benefit from any top or bottom-line organisational improvements.

Higher loyalty

It is believed that customers perceive personalisation, speed, and simplicity, as businesses’ caring for them. The majority of users who desire to make a purchase digitally will abandon it altogether rather than visit a brick-and-mortar office. Through real-time data analysis and partner networks, businesses can create personalised solutions to match user needs.

Personalisation in debt recovery

As seen from the examples above, financial organisations personalise their services to attract and retain customers. Now let’s focus on the debt recovery industry and why many lenders fail at personalisation. There are many ways to personalise different aspects of the debt collection lifecycle: from the basics like using desirable names and messaging styles, to channels to offering different repayment plans based on customers’ preferences.


Debt collection is a sensitive subject. Some customers feel embarrassed and panic, while others try to ignore the problem until the situation blows up. So, businesses need to communicate in accordance with customers’ peculiarities. People are different. Arranging a repayment is a delicate subject – after all, money is always important. Therefore, the chosen approach must be spot on. The most important part of personalisation is remembering there is a human being on the other end. Using customers’ names and expressing empathy; listening to their circumstances and offering potential solutions, and guiding them through the process – all help create truly personalised experiences.


Thus, ICICI bank leveraged advanced business intelligence and analytics to introduce a personalised collections approach, which lead to a 50% increase in debt recovery along with an 80% cut in employee efforts. This proves the potential of personalised, tailored outreach strategies. Relying on Conversational AI automation, businesses can introduce automation scenarios across the entire debt recovery lifecycle driving better recovery results and a unified single view of the customer that combines existing data with communication history analytics to drive personalisation and determine propensity to pay.

Creating and implementing personalisation strategy

Thoroughly analyse customer data

To create truly personalised experiences, businesses need to know their customers through and through: from purchasing behaviour to repayment history to preferences and values. So choose technologies that use a combination of external data and context collected through a conversation to enable personalisation in different scenarios across the debt recovery lifecycle.


This capability is extremely powerful as it allows collection organisations to tailor the way interactions with customers occur at the individual level (always within defined constraints), utilising the single view of the customer to offer anything from payment plans and deferred payment cycles to individual discounts based on circumstance

Segment and group overdue customers

Group overdue customers with similar preferences and characteristics to send out tailored messages en masse rather than coming up with strategies for every single customer.

That’s why you should opt for technologies that allow for integration with existing systems be they customer databases, CRMs or payment terminals. This enables collection scenarios to seamlessly retrieve the data required to proactively reach out to delinquent customers, set up campaigns and drive customers towards meaningful defined actions.

A/B testing

However, even when businesses analyse their customers and segment them into groups, there’s no guarantee that their strategy will immediately work. To choose the best personalisation options, one needs to A/B test them. For example, overdue customers might find certain messages too personal and start worrying about their data.

Enhance and iterate

It’s important that businesses continue adapting and evolving their approach after they gather more customer information. Monitoring changing consumer preferences is crucial to avoid getting left behind. A truly flexible solution typically has a low-code scenario configuration interface. Hence, the business can take responsibility for each scenario within its operations either by itself or together with a solutions provider. In this way, modifications and enhancements can be created more easily where needed and entirely new scenarios can be deployed to solve yet unknown or untapped use cases.


Modern consumers expect a personalised experience and, as such, one-size-fits-all collection strategies are increasingly unsuccessful. Provide your past-due customers with tailored messaging and offers to increase your repayment rates and improve customer loyalty. If you get it right, the benefits will be enormous.

Are you looking to create a truly personalised debt collection solution? Download our recent debt collections report or sign up for our newsletter to receive ongoing industry updates and analysis.

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