Banks, insurance companies, and fintech firms are under growing pressure—from increasing competition, regulatory demands, and ever more demanding customers. Technology is evolving rapidly, clients are online, and they expect services that respond to their needs in real time. Yet, in many areas, pricing in the financial sector remains unchanged—fixed rates, uniform offers, one price for all.

Imagine two customers: one is a small business owner applying for a loan at 11 PM from a mobile phone in New York City, and one is a first-time homebuyer in Los Angeles, visiting a local branch – why should they receive the same offer? Their situations, behaviour, and motivations are different, so the offer should be different too.

This is precisely the essence of dynamic pricing—an approach that is revolutionising how financial products are priced. It’s a data-driven model that uses artificial intelligence to deliver the most appropriate price to each customer, so that it is fair for the individual and profitable for the provider.

The problem with traditional pricing

Most banks today operate with a simple scheme: predefined offers with several rate levels based on basic customer parameters, such as income, age, and creditworthiness. This approach is transparent and easy to manage but often leads to losses or missed opportunities:

• Undervalued offers for high-creditworthy clients – these clients would accept slightly higher rates in exchange for fast service, flexible terms, etc., but the system fails to recognise it.
• Overpriced offers for riskier clients – leading to rejection, even though a better-priced offer would have been acceptable and feasible.
• Inability to respond to real-time customer behaviour (e.g., online activity, changing consumer patterns).
• Limited competitiveness in the digital environment, where customers expect instant, personalised responses.

Additionally, uniform pricing ignores market variability, such as seasonality, competitor promotions, and shifting demand trends.

What Is dynamic pricing and how does it work?

Dynamic pricing is an intelligent approach to pricing. It uses advanced algorithms that consider a wide range of data, from customer behaviour to market conditions, and generates personalised offers in real time.

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How does it work in practice?

• Context analysis

Using machine learning, the system identifies the customer’s decision-making phase—are they just exploring, in urgent need, or hesitant? Dozens of factors are tracked, such as request time, device used, and behavior history in apps or online channels.

• Price sensitivity model (Propensity to buy)

It assesses the likelihood that the customer will accept the offer. The model predicts customer responses to various price points.

• Offer optimisation

Based on the previous steps, a price is proposed that optimises the bank’s business goal, such as maximising NPV (Net Present Value), approval rates, or a combination of both.

The entire process is fully automated and occurs within milliseconds, making it suitable for use in online channels and mobile apps.

The data speaks for itself

Amplifi Capital, a UK-based fintech firm focused on near-prime lending in the highly competitive online broker market, implemented real-time dynamic pricing, and successfully achieved a 30% increase in offer acceptance rates, maintained profitability through controlled optimisation, and deployed a fully automated, cloud-based model.

Another example, consumer lender Equa Bank implemented a model that optimised pricing strategy based on customers’ payment ability and behaviour. This enabled the client to increase revenue and reduce application rejections due to unsuitable offers. The model delivered added business value for almost 70% of the bank’s client base.

Benefits for banks

Dynamic pricing enables banks and financial institutions to capture more value while enhancing customer satisfaction and operational efficiency:

• Higher revenue: More precise pricing enables banks to better leverage each customer’s potential. Creditworthy clients may accept slightly higher prices, while riskier clients can be offered lower ones, while still within acceptable risk levels.
• Improved customer experience: The offer “makes sense” to the client, not just in pricing, but in timing and format. This strengthens trust and loyalty.
• Automation and efficiency: The system operates automatically, requires no manual input, and self-adjusts to market developments through continuous learning.
• Knowledge transfer and flexibility: By maintaining close collaboration with internal client teams during implementation, banks can ensure the system can be easily adapted to evolving needs.
• Easy integration: The solution is designed to plug into existing infrastructure, such as loan systems, mobile apps, or web portals, without disrupting operations.

How to get started

Implementing dynamic pricing isn’t a multi-year endeavour. A typical deployment takes a few weeks to months and can begin with a pilot for a limited customer segment. Banks can expect to boost portfolio profitability by up to tens of percent, enhance customer experience with personalised offers, and realise ROI quickly.

Dominik Matula is Head of AI, Data Science & Machine Learning at Profinit, an Amdocs company