By Chirag Shah
Artificial intelligence (AI) is being increasingly used in the fight against fraud throughout the banking and finance sector to detect everything from credit card fraud to money laundering. But nowhere is it proving more effective right now than in the credit lending space.
AI has already transformed the lending decision-making process, enabling users to get a decision on their loan application almost instantly. But another area where it’s fast showing its worth is in detecting fraud.
Previously, lenders have had to process thousands of loan and mortgage applications manually. Given the sheer volume of workload they had to contend with, not to mention the risk of human error, they were increasingly left exposed to fraudulent activity.
The scale of the problem was evidenced by the £4.9 billion that the UK government lost to loan fraud during the COVID-19 pandemic. And the issue has only worsened as the criminals have become bolder and more sophisticated in their methods.
There are four main types of lending fraud. They are application fraud, impersonating another business, providing incorrect information, and hiding data.
Application fraud is usually defined as when an individual or business uses their own details to apply for a financial product but uses false information or counterfeit documents in the application. For example, they may supply fake account statements to indicate that they turn over more than they actually do.
Impersonating another business is when a person masquerades as a company that they are not. Fraudsters have perfected the technique so that, to the human eye, it’s difficult to tell the difference between the real and fake one.
Providing the wrong information takes the form of misstated management information and fudged bank statements. Unless it is checked thoroughly against the correct records, this fraud can easily slip through the net.
Hiding data involves the scammer deliberately withholding information from the lender. This is one of the hardest frauds to detect if the lender doesn’t know what data that individual holds in the first place.
These types of fraud can be carried out by individuals and businesses under their own names or by someone who has stolen their identity. This type of activity can be harder to spot, as the victim may have no idea that a new account has been opened. It may only come to light when they try to apply for credit and are rejected because of the fraudulent activity that has been associated with them as a result.
Using AI to detect fraud
That’s where AI comes in. By using algorithms to analyse the vast amounts of data contained within the application, it can quickly and accurately identify patterns that may indicate fraudulent activity. For example, AI algorithms can detect suspicious behaviour, such as multiple applications for the same loan from the same person or IP address.
AI-driven solutions can also help lenders identify potential borrowers likely to default on their loans by analysing their credit history and other financial information. Additionally, AI-based systems can be used to monitor existing loans for signs of fraud or delinquency in real time.
Once these red flags have been picked up by AI, the technology can then be used to analyse it further or it can be passed on to humans to scrutinise it in greater detail and make a decision on the appropriate course of action to take. If further investigation is required, then it can be checked by another party.
Challenges with AI
There are challenges, however, with implementing AI in the detection of fraud in lending. The most important of these is trying to avoid AI bias or discrimination caused by humans in the programming of machine learning.
Another problem is that the scammers themselves are increasingly using AI to try and avoid detection by making their attacks faster and more convincing through impersonation, automated phishing attacks, and data manipulation. But if the technology used by the lender is up to date and working effectively, it can prevent fraud from happening.
With more than 50 per cent of financial institutions planning to roll out AI solutions to detect unknown fraud cases, the technology’s application in this field is only going to increase in the future. As fintech integrates more closely with traditional banking and finance, this is the next step in the sector’s digital evolution.
By leveraging AI, lenders can more effectively detect and prevent fraudulent loan activity at the earliest-possible stage, while still providing access to credit for those who need it most. AI is transforming the credit lending industry for the better and its use in the fight against fraud is just the latest step in that journey.
About the Author
Chirag Shah, founder and CEO of Nucleus Commercial Finance and Pulse.io, has over 20 years of experience in the financial services industry and a deep understanding of the needs of UK SMEs.
In 2011, he founded Nucleus, a leading alternative finance provider, to offer flexible and tailored solutions for SMEs across various sectors and stages of growth. With an understanding of the challenges that UK SMEs face in the current economic climate, Chirag launched Pulse in October 2022, a free-to-use service that helps businesses and accountants gain insights into financial performance with AI-powered data visualisation and personalised dashboards. Chirag is not only committed to driving growth and innovation in the UK business ecosystem, but he’s also helping SMEs better understand their data to boost their profitability and guide them towards success.
