Hyper-personalised experiences and enhanced risk management – this is how using AI and machine learning for personalisation in finance helps you attract and retain customers

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Are you exploring AI to help you personalise user experiences?

In the rapidly evolving world of finance, where competition is fierce and consumer expectations are high, banks and FinTechs are increasingly looking for solutions to meet and exceed customer expectations.

Data-driven products and personalisation are key tools to accomplishing this – see how to use big data to understand customer needs and how to hyper-personalise based on customer data analytics – especially when powered by machine learning (ML).

Used right, AI can be a powerful, 24/7, no rest, no sleep companion for analysing vast amounts of data and delivering individualised outputs at scale to set new benchmarks in customer engagement.

Here’s how to approach using machine learning for personalisation at scale in finance.

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Personalisation in Finance

Personalisation is so important in finance that it talks to a full 30% of the top banking UI trends.

Yet personalising your service to financial decision-makers is no longer just about addressing a customer by name. Today, it involves understanding the unique financial needs and behaviours of each customer at a granular level, enough to offer them tailor-made services, products, and interactions.

A task that would have been near-impossible (or unaffordable, at least) using human analysts a few years ago is now more than feasible using machine learning.

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See how to use empathy and data to build financial products and way it pays to invest in data-driven product development.

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The Role of Machine Learning in Personalisation

Machine learning is when a computer system (AI) uses algorithms and statistical models to analyse and draw inferences from patterns in data, without you having to give it specific instructions. So it thrives on data – unlike humans, the more data you give the algorithm, the better it performs. 

In the context of finance, these algorithms can sift through vast arrays of transaction data, investment preferences, customer feedback, social media activity, etc. to glean insights that were previously inaccessible. 

This data-driven approach not only helps you to segment customers more accurately but also predict future financial needs and behaviours.

Here’s how you, might use it to retain customers and inform a data-driven product development strategy…

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How Machine Learning Personalisation Can Work in Finance

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If you were to get the algorithm to:

1. Help You Analyse Strategic Data

• Transaction Data: For example, ML algorithms can analyse spending patterns to offer budgeting advice or recommend new financial products that align with the customer’s spending habits.
• Investment Preferences: By understanding past investment actions, ML can propose personalised investment strategies tailored to the risk profile and financial goals of the customer.
• Customer Feedback & Touch Points: Analysing feedback across various channels enables financial institutions to refine their offerings and address customer concerns proactively.
• Social Media Activity: Insights from social media can help in tracking sentiments, board customer needs and even predicting major life events, potentially leading to timely financial advice or product recommendations.

See how to drive engagement with customer data analytics.

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You could use it to:

2. Create Hyper-Personalised Experiences

• Recommend Financial Products: Based on the comprehensive analysis, customers can receive recommendations for products that truly fit their needs, from insurance to investment plans.
• Deliver Proactive Communication: ML enables banks and fintechs to send communications at the most opportune times, perhaps even alerting customers to financial opportunities or risks.
• Personalise the Online Experience: Tailoring the user interface and features to fit individual customer preferences and usage patterns, making every digital interaction a pleasure.

See the ultimate design thinking principles for UX.

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As well as:

3. Enhance Your Risk Management

• Analyse Individual Spending Patterns: This can help in adjusting credit limits and loan offers in real-time, based on the customer’s financial behavior and current needs.
• Customise Creditworthiness Assessments: Beyond traditional credit scoring, ML can incorporate various non-traditional data points to provide a more accurate and fair assessment of a borrower's creditworthiness.

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See the power of using customer feedback analysis to help innovate.

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Benefits for Banks

Banks and traditional institutions that embrace ML-driven personalisation at scale can see increased customer loyalty and satisfaction as they deliver services that customers truly want and need. 

Additionally, the ability to predict financial trends and customer needs allows banks to stay ahead of the curve, potentially increasing their market share.

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A Worthwhile Space for Collaboration

What’s more, in recent years, banks have been steadily losing customers in certain segments to disruptor brands, and collaborating with innovative new FinTechs is one key solution. The only problem with this is that banks don’t really know how to structure, manage and measure that collaboration for success.

One possible avenue is to invest in a FinTech collaboration limited to this type of function: FinTech helps you bring in machine learning to hyper-personalise your customer experiences.

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See how to use blockchain in banking to manage collaborations and how it unlocks data-driven product development.

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Benefits for FinTechs

For FinTechs, machine learning offers a powerful tool to disrupt traditional banking relationships and build market share. By offering innovative, highly personalised financial products, you can attract tech-savvy consumers looking for services that match their lifestyles and preferences.

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See how we’ve helped FinTechs build amazing products.

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Note: Maintain Trust & Transparency

While leveraging data to deliver personalised experiences, it's crucial to maintain a transparent approach to how customer data is used. Building trust through transparency not only complies with stringent data protection laws but also reassures customers about their data privacy – turning them into loyal advocates for your brand.

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Need to hyper-personalise to retain and attract customers?

Speak to our FinTech development specialists to get better insights for more accurate decisions.

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