How the era of frictionless finance can stay ahead of fraud

Written in collaboration with Gabriel Vieira (CI&T’s VP of Engineering) and Henrique Souza (CI&T’s VP of Data)

There is a central tension at the heart of modern finance: The more digitised and friction-free payments become, the greater the risk of fraud and compromised customer data rises. 

While the need for stringent security is obvious, it can’t come at the price of a consumer-centric customer experience. So, how does the financial service industry protect its customers without undermining the very convenience that digital finance promises? 

The key lies in creating an effective and unobtrusive security framework that operates seamlessly in the background, powered by data and strengthened by AI. And as it turns out, the financial sector has a lot to learn from other industries on this matter. The telecommunications industry is facing many of the same challenges and therefore offers some possibilities for solutions and innovation. 

Let’s take a closer look at the problem and opportunities it presents.

Data as a problem and solution

It’s worth first dwelling on the nature of the problem at hand. Data is the lifeblood of modern finance. It enables personalised customer services and powers sophisticated fraud detection. The sheer volume of data that the sector deals with can be both an asset and a liability. 

The stakes for getting things wrong are rising, too. Regulators are increasingly levying fines and placing more regulations on financial institutions, underlining the need to take this risk seriously. For example, the European Union’s DORA regulations require financial institutions to boost their resilience by investing in key operational areas such as incident reporting, risk management, and information sharing. Firms that fail to comply will risk fines and reputational damage. 

There are a slew of best practices used across the sector which utilise data in the fight against fraud. These include capabilities like pattern recognition and anomaly protection, risk scoring and predictive modelling, network analysis, and real-time monitoring. While these practices certainly still have their place, there is simply too much data to rely on business as usual. 

AI needs to be incorporated into each of these standard practices to make them more efficient and to help face growing threats like social engineering and identity theft. AI and machine learning can analyse vast datasets to identify suspicious patterns and predict fraudulent activity. Using AI in this way means that the vast quantity of data becomes something firms can leverage to boost security, rather than being a liability. 

Reaching across industries 

One industry that financial services can look to is the telecommunications industry. Both sectors are facing a similar challenge: figuring out how to ensure robust security measures while also providing a frictionless customer journey. 

The similarities don’t end there, though. Both sectors also deal with vast quantities of consumer data, all while having to maintain a high degree of consumer trust with their customers. And in a digitised world, customers increasingly interact with both banks and their telecoms provider in the process of carrying out a transaction, whether it be paying via contactless payment on their phone, or using SMS or a phone call to verify a transaction and prevent bank fraud. 

So it should come as no surprise that the financial services sector not only has a lot to learn from telecoms, but also has a lot to gain from collaborating with them on innovative products. An example from one of our clients, a leading telecommunications company in the UK, showcased a tap-to-pay product that allows small businesses to accept in-person contactless payments using both iPhone and Android devices. This allows merchants to accept card and contactless payments without having to employ additional hardware. The collaboration opens the door for many SMEs to accept payments and customer data in a secure and safe way.

Another example comes from Revolut, which rolled out an AI-powered scam detection feature designed to prevent users from falling victim to Authorised Push Payment (APP) scams. According to the company, the feature “uses sophisticated machine learning to detect if a customer is being scammed, and therefore break the ‘spell’ of the scammer before they send their money to the criminal.” The company has observed a 30% reduction in fraud losses since the roll-out. 

You’ll notice that these examples keep the customer experience front and centre. They add security without sacrificing customer centricity. Furthermore, they deepen the relationship between the customer and either the bank or telecoms provider. Imagine how reassured a consumer might feel if their bank noticed that the phone used to access their online banking app was using a different SIM card. This kind of attunement to the customer builds trust beyond a simple transaction and forms a deeper relationship.

That’s why the success of any fraud prevention strategy hinges on a customer-centric approach. By embracing innovation, leveraging data responsibly, and prioritising the customer experience, financial institutions can navigate the tightrope between security and convenience and build a more secure and trusted financial ecosystem.