Ensuring innovation remains one step ahead of fraudsters

Ensuring innovation remains one step ahead of fraudsters

Fraud in the UK in the last five years has become commonplace. Scams, from fake and phishing emails to crypto and romance fraud, pose a risk to businesses and their customers. Eduardo Castro, Head of Identity and Fraud, Experian UK&I, spoke to Intelligent CXO about the problem of fraud within the financial services and some of the major innovations tackling the problem.

What is the extent of the problem of fraud within the financial services?

Fraud remains a widespread issue across the financial services sector and is impacting both businesses and consumers alike. Latest figures from Experian reveal that current accounts are the main target of all financial products, with one in 100 current account applications being fraudulent. In the last quarter alone, we have seen an 13% increase in the fraud rate for current accounts, as well as an 18% increase in credit card fraud.

A key problem the industry faces is the ever-changing nature of fraud, with new trends arising every year. For example, this year we’ve seen a rise in money mule cases. This is a type of fraud where criminals recruit people to use their accounts and transfer illegally obtained funds between different accounts. Our research shows that two in five instances of account fraud can be classed as mule activity, making this a major obstacle for the industry.

There are types of fraud which we see on an annual basis. For example, cases of identity fraud tend to peak during the Christmas period. In the past five years, identity fraud rates have risen by up to 15% year-on-year during November and December. This is because fraudsters attempt to take advantage of the volume of online transactions during this period, which gives businesses less opportunity to investigate potential fraud.

How has fraud prevention changed within the last five years?

Fraud in the UK in the last five years has become commonplace. Myriad scams, from fake and phishing emails, false information, to crypto and romance scams – which all potentially fall under the category of Authorised Push Payment (APP) fraud – pose a risk to businesses and their customers. Latest Experian analysis found that credit card fraud is at a 10-year-high, indicating the scale of identity fraud occurring as personal information is sold online.

New UK legislation that makes institutions liable for losses associated with APP fraud means fresh approaches will be required that support real-time transaction analysis and enhanced user authentication – something which consumers increasingly have high expectations of.

From a business perspective, financial services have been focused on developing and deploying new systems that can identify suspicious transactions and activity, rising to meet the challenge. In a survey of leading organisations, Experian found that they are increasing fraud prevention investments in the face of this growing threat. Almost three-quarters of businesses are expecting increased budgets for fraud management for the next year. Of these, 79% are expecting increases of more than 8%.

What are some of the major innovations in fraud prevention?

Innovative technologies involving Machine Learning (ML) and Artificial Intelligence (AI) are becoming a growing – and increasingly commonplace – tool in fraud prevention. Machine Learning is now a ‘non-negotiable’ as it means large numbers of transactions and datasets can be analysed automatically, extending fraud prevention measures across an entire customer portfolio, ensuring that new and existing fraud risks can be identified quickly and at scale.

In fact, more than a third of businesses are now looking to build ML capabilities into their fraud identification and prevention strategies. However, nearly half (49%) cited cost as the most significant barrier to ML adoption.

An example of Machine Learning is Experian’s recently developed Mule Score solution. This service helps banks identify mule accounts using Machine Learning powered solutions to analyse account opening history, turnover activity, Experian bureau data and the modelled characteristics of more than 200,000 confirmed mule cases. Banks can use this to assess their entire portfolio and identify questionable account activity.

What are the current trends and best practices within fraud prevention in the financial services?
We’ve seen credit card fraud hit a 10-year-high, while the cost of living crisis has also triggered a rise in first party fraud, with some households misrepresenting their financial situation to meet additional costs, or even cover everyday expenditure.

It also sets the conditions for vulnerable people to be targeted as mules, which now accounts for 42% of first party fraud data.

We’re also expecting far more investment from financial services providers into technology that can prevent APP fraud, following new legislation from the Payment System’s Regulator. This legislation means consumer victims are now eligible to be refunded for APP, which could potentially double the cost to banks and lenders to over £400 million. This comes as APP fraud cost consumers £249.1 million last year.

What types of fraud are being predicted for the year ahead?

Cutting-edge technologies like ML and AI will play a fundamental role in fraud prevention but, at the same time, fuel new threats. Previously, fraudsters were able to create ‘synthetic’ or ‘Frankenstein’ IDs by using a combination of genuine and fake information to create a new identity and commit fraud.

This process could often be time consuming. Now, with generative AI widely accessible, that process can be reduced dramatically, with fraudsters exploiting the technology for illegal means.

By automatically scraping the Internet, social media and the Dark Web to obtain information such as names, addresses, phone numbers and email addresses, the AI system combines them to create convincing synthetic IDs, complete with contact points like email, phone numbers and physical addresses – all of which can be used to pass verification checks and commit fraud.

AI systems have a great capacity to quickly learn from past failures. With each failed fraudulent attempt, AI can refine its approach, understanding each target’s fraud controls, learning the strengths and weaknesses of the fraud detection systems and refining the characteristics of the synthetic IDs that it generates. This makes them progressively more difficult to detect, with the threat constantly evolving over time.

Conversely, by deploying ML and AI prevention systems, businesses can quickly detect questionable activity from a ‘customer’ and investigate further.

Does a one-size-fits-all approach work in tackling fraud?

There are of course recurrent themes across various types of fraud, but each fraud scenario has its unique challenges, and therefore requires tailored solutions. For example, in the case of identity fraud, authentication solutions are key for prevention. Meanwhile, account fraud prevention relies more heavily on ML systems that can cross-reference volumes of data and records to spot anomalies.

The landscape for fraud is constantly changing, with fraudsters looking for new ways to exploit businesses, consumers and financial services. This means the industry cannot afford to rely on a one-size-fits-all approach when it comes to tackling fraud; it must remain vigilant to new types of threats and ensure prevention techniques and innovation remain one step ahead of fraudsters.

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