
Nigerian Couple Jailed for £650k Tax Fraud & ‘Worst Ever’ TfL Data Breach
A Nigerian husband and wife who used confidential Transport for London staff records to carry out a large-scale tax fraud have each been jailed for three…
A Nigerian husband and wife who used confidential Transport for London staff records to carry out a large-scale tax fraud have each been jailed for three years and nine months after a judge described the offending as TfL’s “worst ever” data breach.
Woolwich Crown Court heard that Luciana Akanbi, 38, who worked for TfL in a role giving her access to employee records, unlawfully accessed the personal details of 107 members of staff, including passport numbers, National Insurance numbers and bank details.
Prosecutors said information relating to 40 employees was then used by Luciana Akanbi and her husband, Femi Akanbi, 51, to submit 139 false tax rebate claims to HM Revenue and Customs between September 2021 and January 2022.
The fraudulent claims totalled just under £649,000, with the actual loss to the public purse amounting to more than £433,000.
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Sentencing the couple, both of Dartford, His Honour Judge David Miller said: “TfL suffered their worst ever data breach. It meant they had to change their systems.
“It affected their morale, I am told, and staff performance.”
He added: “There were 139 claims for tax rebates totalling just under £649,000.
“The money lost to HMRC amounted to just over £433,000. That money was almost instantly dissipated in a complex money laundering scheme.”
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Andrew Evans, prosecuting, told the court the fraud was “sophisticated in nature”, required significant planning and involved a large number of victims.
The court heard Luciana Akanbi abused her position of trust within TfL to obtain colleagues’ private information, which was then used to create false self-assessment accounts and submit fraudulent repayment claims.
Judge Miller said the couple were “at the epicentre” of the offending, adding that the fraud was only possible because of Luciana Akanbi’s access to sensitive employee data.
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