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Gang of Ghanaians sentenced for £1 million series of frauds in the UK

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Left to right Carter Allan Spencer From left to right: Carter, Allan and Spencer[/caption] On 13 December 2018, three men of Ghanaian origins were sentenced at Southwark Crown Court for their part in a series of frauds. The fraudsters created make-believe companies to apply for credit from well-known businesses, including American Express and Barclaycard. Luxury items purchased included over £126,000 worth of mobile phones and tablet devices plus Cartier and Rolex watches and items from Burberry. The case was investigated by the City of London Police’s Fraud Squad following an initial report made to them by American Express.

The remaining three, who have been found guilty, will be sentenced in the New Year. The three men were sentenced as follows:
 
  • Marcus Carter, also known as Marcus Boahene-Coabbina, 38, who is currently serving a prison sentence at HMP Wandsworth for an unrelated matter, pleaded guilty to conspiracy to defraud and conspiracy to acquire criminal property. He was sentenced to seven years and two months in prison. He has also been disqualified from being a company director for 12 years. [caption id="attachment_137349" align="aligncenter" width="480"]Marcus Carter Marcus Carter[/caption]
  • Jeffrey Spencer, also known as Victor Templar-Quarshie, 35 of Belgrave Road, South Norwood, London was found guilty of conspiracy to defraud, conspiracy to acquire criminal property and failing to disclose the PIN to his two mobile phones and was sentenced to nine years in prison. He has also been disqualified from being a company director for 12 years. [caption id="attachment_137350" align="aligncenter" width="480"]Jeffrey Spencer Jeffrey Spencer[/caption]
  • Frank Allan, also known as Frank Templar-Quarshie, 33 of Belgrave Road, South Norwood, London was found guilty of conspiracy to defraud, conspiracy to acquire criminal property, possession of a false identity document with improper intention and failing to disclose the PIN to his mobile phone and was sentenced to seven years and nine months in prison. He has also been disqualified from being a company director for 10 years. [caption id="attachment_137351" align="aligncenter" width="480"]Frank Allan Frank Allan[/caption]
  • Yaa Abrefa, 37 of Hythe Road, Thornton Heath, London, was found guilty of conspiracy to acquire criminal property.
  • Abena Amankwaa Frempong, 34 of Transmere Road, Petts Wood, London was found guilty of conspiracy to acquire criminal property.
  • Craig Apaw, 29 of Hazelbank Road, London was found guilty of possession of criminal property.
Between June 2014 and February 2016, the defendants would, using false documents, buy an inactive company name and appoint a fictitious director. Once the company had been formally established, fake back-dated accounts were submitted to Companies House which gave the impression that they had a very successful trading history and represented a low credit risk.
They would then make applications to their chosen credit card companies, who after carrying out the necessary checks, were reassured by the false information and approved a line of credit. This gave the fraudsters and their fraudulent companies access to a high credit limit. The suspects would then rapidly spend up to this limit, without making any repayments. This is known as short firm fraud.
The core offenders involved at every stage were Carter, Spencer and Allan. The investigation found that Abrefa, who is Carter’s ex-partner, aided him by allowing him to use her bank account and registering high-end designer watches, paid for with the proceeds, in her own name. Amankwaa Frempong, who is Carter’s ex-partner, would buy goods in her name with the fraudulently obtained cards, which he then exported back to Ghana. Apaw assisted Carter by collecting a £8,250 Rolex watch when Carter was in prison and passing this to Frempong, which she then pawned for cash.
This case was initially reported to the City of London Police as a case of a fraudulent application for, and subsequent use of, American Express corporate cards. When the investigation into this criminality started, a wider criminal network was uncovered.
The items that were bought included electronics, lease vehicles and high-end watches, to the value of £945,936. The goods would then be delivered to virtual offices opened in the same fictitious details as the companies themselves. Once the goods were delivered, they were immediately removed and the suspects moved on.
The suspects also specifically targeted London-based private hire taxi company Addison Lee Group to make journeys which they failed to pay for. On uncovering this, Addison Lee Group were able to provide journey history and call recordings which showed the group’s whereabouts at given times and was a means of identifying the members.
Fraud Investigator and officer in charge of the case, Andy Cope, said:
“These three men thought they could get away with their devious crimes, however the Fraud Squad’s investigation has uncovered the full extent of their complex fraudulent activity.”
“Today’s sentencing should serve as a warning to those who are thinking about committing similar offences; this is not a victimless crime. We work tirelessly to pursue fraudsters to make sure that justice is served.”
“The POCA orders sought by the prosecution today will stop the offenders from being able to re-offend, and will remove any financial benefit they gained from the offending.”
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Meta’s AI AI machine translation research helps break language barriers

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Today, Meta announced that it has built and open sourced ‘No Language Left Behind’ NLLB-200, a single AI model that is the first to translate across 200 different languages, including 55 African languages with state-of-the-art results. Meta is using the modelling techniques and learnings from the project to improve and extend translations on Facebook, Instagram, and Wikipedia.

 

In an effort to develop high-quality machine translation capabilities for most of the world’s low-resource languages, this single AI model was designed with a focus on African languages. They are challenging from a machine translation perspective. AI models require lots and lots of data to help them learn, and there’s not a lot of human translated training data for these languages. For example, there’s more than 20M people who speak and write in Luganda but examples of this written language are extremely difficult to find on the internet.

 

We worked with professional translators for each of these languages to develop a reliable benchmark which can automatically assess translation quality for many low-resource languages. We also work with professional translators to do human evaluation too, meaning people who speak the languages natively evaluate what the AI produced. The reality is that a handful of languages dominate the web, so only a fraction of the world can access content and contribute to the web in their own language. We want to change this by creating more inclusive machine translations systems – ones that unlock access to the web for the more than 4B people around the world that are currently excluded because they do not speak one of the few languages content is available in.

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“It’s impressive how much AI is improving all of our services. We just open-sourced an AI model we built that can translate across 200 different languages — many of which aren’t supported by current translation systems. We call this project No Language Left Behind, and the AI modelling techniques we used are helping make high quality translations for languages spoken by billions of people around the world. To give a sense of the scale, the 200-language model has over 50 billion parameters, and we trained it using our new Research SuperCluster, which is one of the world’s fastest AI supercomputers. The advances here will enable more than 25 billion translations every day across our apps. Communicating across languages is one superpower that AI provides, but as we keep advancing our AI work it’s improving everything we do — from showing the most interesting content on Facebook and Instagram, to recommending more relevant ads, to keeping our services safe for everyone,” said Meta CEO Mark Zuckerberg in a post on his Facebook profile.

 

Language is our culture, identity, and lifeline to the world. However, as high-quality translation tools don’t exist for hundreds of languages, billions of people today can’t access digital content or participate fully in conversations and communities online in their preferred or native languages. This is especially true for hundreds of millions of people who speak the many languages of Africa.

 

“Africa is a continent with very high linguistic diversity, and language barriers exist day to day. We are pleased to announce that 55 African languages will be included in this machine translation research, making it a major breakthrough for our continent,” Balkissa Ide Siddo, Public Policy Director for Africa said while speaking about the launch of the AI model. “In the future, imagine visiting your favourite Facebook group, coming across a post in Igbo or Luganda, and being able to understand it in your own language with just a click of a button – that’s where we hope research like this leads us. Highly accurate translations in more languages could also help to spot harmful content and misinformation, protect election integrity, and curb instances of online sexual exploitation and human trafficking.”

 

While commenting on accessibility and inclusion in the pursuit of building an equitable metaverse, Ide Siddo added “At Meta, we are working today to ensure that as many people as possible will be able to access the new educational, social and economic opportunities that the next evolution of the internet will bring to future technology and an everyday living experience tomorrow.”

 

To confirm that the translations are high quality, Meta also created a new evaluation dataset, FLORES-200, and measured NLLB-200’s performance in each language. Results revealed that NLLB-200 exceeds the previous state of the art by an average of 44 percent.

 

Meta is also open-sourcing the NLLB-200 model and publishing a slew of research tools to enable other researchers to extend this work to more languages and build more inclusive technologies. Meta AI is also providing up to $200,000 of grants to non-profit organizations for real world applications for NLLB-200.

 

There are versions of Wikipedia in more than 300 languages, but most have far fewer articles than the 6+ million available in English. Following Meta’s partnership with the Wikimedia Foundation, the non-profit organization that hosts Wikipedia and other free knowledge projects, modelling  techniques and learnings from the NLLB research are now also being applied to translation systems used by Wikipedia editors. Using the Wikimedia Foundation’s Content Translation Tool, articles can now be easily translated in more than 20 low-resource languages (those that don’t have extensive datasets to train AI systems), including 10 that previously were not supported by any machine translation tools on the platform.

 

To explore a demo of NLLB-200 showing how the model can translate stories from around the world, visit here. You can also read the research paper here.

 

 

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