Infographic
Key Findings from the
UK Finance 2021Annual Fraud Report
UK consumers lost over £1.3 billion to fraud in 2021. The latest Annual Fraud Report from UK Finance described the situation as an “epidemic of fraud,” with authorized push payment (APP) fraud cases notably high at 195,000 reported cases.
Download to learn:
- how fraudsters scammed UK consumers out of £171.7m using investment scams
- how impersonation scams rose by 50% as fraudsters used social engineering to exploit victims’ fears
- how CEO fraud surged by 165% bad actors pretending to be a CEO or boss
- 3 things both banks and consumers can do to prevent fraud
Authorized Push Payment(APP) fraud is surging
195,000
new cases reported in 2021
39%
increase in APP cases
£583.2m
estimated losses
Types ofAPP Fraud
Work with Partners to Future-Proof Your Operations
Work with a trusted provider who understands that fraudsters constantly shift tactics. Stay up to date on the latest trends and future-proof your operations with partners who can protect any transaction type.
57%
increase in losses
from 2021 to 2022
£171.7m
estimated losses in 2021
ImpersonationScams
Fraudsters use social engineering
tactics to play on victims’ fears and
steal their money.
50%
increase in losses
from 2021 to 2022
£215m
estimated losses in 2021
CEOFraud
Work with a trusted provider who understands that fraudsters constantly shift tactics. Stay up to date on the latest trends and future-proof your operations with partners who can protect any transaction type.
165%
increase in losses
from 2021 to 2022
£12.7m
estimated losses in 2021
3 Ways Banks
Can Reduce FraudLosses
- Invest in cross-channel fraud detection, leveraging a full customer view to build profiles of normal activity
- Offer consumers contextual advice based on APP scam types
- Create layered controls to provide added protection
3 StepsConsumers
Can Take to ProtectThemselves fromScams
- Never give out your card or internet banking credentials to anyone
- Follow emerging APP fraud types to stay vigilant
- Be skeptical – if it’s too good to be true, then it probably is
