Fraudulent practices in financial activities trace their origin to our ancient civilizations. The earliest example recorded is from Ancient Greece, where a ship captain defrauded people out of insurance money. Needless to say, it’s not a new issue. But with the world becoming increasingly digital, the scale and complexity of fraudulent activities have become more sophisticated and increased to worrying levels. According to the latest Crime Survey for England and Wales, there was a 54% rise in fraud and computer misuse offences, between December 2019 and December 2021. One area contributing to this rise is tenancy fraud.
With the ongoing rental crisis, created by a lack of available properties and skyrocketing rental prices, tenancy fraud is becoming an increasingly profitable form of financial fraud. In fact, there are even numerous companies who specialize in providing fraudulent tenancy documents (right to rent, income statements, landlord references, employment references, etc) and deal in other sophisticated methods to help people illegally obtain access to properties.
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While agents and landlords have long been aware of the risk of fraud, new technologies are now being used to fight against fraudsters and their attempts to commit these financial crimes. AI tools such as open banking are making a real difference in the referencing process used by letting agents, boosting their ability to flag and refute fraudulent tenancy applications.
What is open banking and how does it work?
Open banking describes financial technology that securely opens up banking data, such as transactions, credit cards and loans to authorized third parties. These third parties are then required to go through a detailed due diligence process to get regulatory permission from the Financial Conduct Authority.
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With the permission of the bank account holder, these third parties can help consumers to make better-informed decisions around their finances and manage their money more easily. Returning to financial tenancy fraud, this means the lettings industry has a more accurate and reliable way to verify potential tenants’ information — like someone’s income, for example.
Previously, it was thought that the best way to check someone’s income is to check their payslip, but these can be easily edited or faked. Open banking means that lettings agents can verify your income based on the pay that actually goes into your bank account — it can’t be falsified.
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By presenting a transparent picture of a renter’s income, open banking not only adds a layer of protection against fraud, it also paves the way for a far simpler and quicker referencing processes. And, thanks to this added benefit of ease, letting agents are adopting open banking more than ever before.
Anti-fraud tech is a necessity for letting agents
With the tenant’s permission, open banking provides letting agents with a snapshot of their tenant’s financial behavior based on their income and rental payment history directly from their bank. It’s an instant process that can help speed up the referencing process dramatically and ensures that information cannot be falsified.
This is hugely beneficial in providing agents, landlords and tenants alike with peace of mind when it comes to verifying tenants and validating their income and expenditure. In fact, our industry data has revealed that lettings agents using open banking over the last year were able to ensure that not a single fraudster fooled the system.
Data from the industry has also revealed that anti-fraud tech, like open banking, has the capacity to save landlords huge amounts of money, with individual landlords risking millions of dollars without anti-fraud technology in place to prevent suspicious documents and fraudulent information from being submitted.
While the referencing industry has remained archaic in its approach for some time, the rise of open banking has great potential to reduce fraudulent activity. According to our data, currently, about half of rental applicants are being verified through open banking, but this is increasing every year.
