UK Court Jails Nigerian Couple Over Large-Scale TfL Data Breach and £650,000 Tax Refund Fraud

Story: written by springnewsng April 16,2026
A UK-based Nigerian couple, Luciana Akanbi and Femi Akanbi, have been sentenced to prison after being found guilty of illegally accessing employee data and orchestrating a major tax refund scam involving Transport for London staff records.
The pair were sentenced at Woolwich Crown Court after investigators uncovered a coordinated cyber-enabled fraud in which personal details of more than 100 employees were compromised and used to submit dozens of fake tax rebate applications.
Court findings revealed that the scheme was carried out between September 2021 and January 2022. During this period, the duo allegedly exploited sensitive information such as passport numbers, National Insurance details, and banking records belonging to TfL employees to file fraudulent claims with the UK tax authority, HM Revenue & Customs.
Mrs Akanbi, a human resources worker at TfL, was said to have accessed confidential staff data, while her husband—an IT specialist—allegedly helped design and execute the digital aspects of the fraud. Authorities say over 130 fake claims were submitted using multiple computer systems operated from their residence and other locations.
The court heard that although the total attempted fraud amounted to nearly £650,000, approximately £433,000 was successfully stolen from public funds before being detected and traced through financial investigations.
Sentencing the pair, Judge David Miller described the case as one of the most serious data breaches in TfL’s history, noting the significant operational disruption, reputational damage, and financial impact caused by the offence.
He further stated that the defendants accessed data belonging to 107 employees while directly using details of around 40 individuals to carry out the fraudulent claims. The stolen funds were rapidly moved through a complex laundering network.
Authorities say the case highlights the growing threat of insider-driven cyber fraud within major institutions and the increasing risks posed to sensitive public-sector data.