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Letters

Use artificial intelligence in war on economic crimes

Con artists target phone users with fraudulent transactions. FILE PHOTO | NMG
Con artists target phone users with fraudulent transactions. FILE PHOTO | NMG 

Whenever we discuss money laundering and other financial crimes, the big picture which comes to mind is the Carribean drug dealers. Even the illustrations and statistics used are all about dollar-denominated cases and the huge financial implications and fines in the European markets.

Rarely, do we really see locally quantified figures to assess the extent of the damage. That is the reason why, the issue has remained a foreign discussion, which we cannot attach emotions and feelings despite the economic mess it has done to our economy.

The Communication Authority of Kenya recently issued an alert on international mobile phone scams.

Wangiri fraud, as it is known, emanating from Japan and the dark web specialises in the theft of calling credit through high premium calls that are rerouted to automated answering machines.

The scammers will just call and disconnect prompting the victims to call back. Within the shortest time, their calling credit is wiped out.

According to Communications Fraud Association estimates, the scammers were able to rip off $4.96 billion (about Sh500 billion in 2011), through compromised voicemail systems. This is about half of Safaricom’s worth. The most profitable company in the region valued around Sh1.2 trillion. Locally, we also have the mobile phone scams.

Almost all Kenyan mobile subscribers have received messages informing them of winning jackpots of non-existent phone-based lotteries, messages marketing loans, alleged call centre staffs informing them of the wrong linkage of their phones and wrong account numbers, among others. The messages are followed by calls from the scammers and gullible ones have ended up losing money.

The fraudsters who have mastery in social engineering, will engage the victims in a friendly conversation, get their personal identification numbers and proceed to defraud them.

The smart thieves will first get some basic details like names of the victims through social engineered processes which include sending small amounts of money, initiating transactions and cancelling them halfway.

Psychologically, any person calling you by names gains some level of trust and that does the magic.

The amounts involved might not be huge since they are capped at the daily transaction limits or the cash the victims are holding in their accounts.

However, like the Wangiri calling credit fraud, the money collectively runs into millions in a single day. Unfortunately, we have not endeavoured to quantify the cash stolen through all the mobile systems locally.

The authorities have established reporting hotlines meant to deactivate the lines involved in fraudulent transactions, however, most of them go unreported. Second, there is no major gathering of the artificial intelligence to establish the extent of the rot.

Last week, it emerged that, the government might have lost billions of shillings in another National Youth Service scandal.

Just like it happened years ago, millions were lost but no analysis was done to determine the exact amounts involved.

Like other local economic scams such as Goldenberg, maize scandal, pyramid schemes, looting of public coffers, tax evasion and phantom real estate scams, the exact amounts involved have not been established.

Artificial intelligence has not been used to analyse the complex tactics employed and all the individuals involved in the scams leaving loopholes for the scandal to happen again.

Currently, all these cases are only analysed for prosecution purposes but nothing more.

In this era of Big Data and advanced technology, if we have to make progress in the fight against economic scams, we should start using local artificial intelligence in analysing and quantifying the cases.

We need to establish the trends, patterns and predict the possibilities of recurrence and curb economic crimes.

Thiong’o Irungu via email

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