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Data Will Help To Track Illegal Employee Activity

With new rules around corporate responsibility likely, data could help track nefarious activity

12Sep

When Enron went Bankrupt in 2001, it was found that the company was defrauding customers at almost every level. Subprime mortgage lenders deliberately manipulated markets in order to sell bad debt, which was one of the leading causes of the financial crash in 2008. We have also seen a number of banks being fined for everything from tricking to customers to manipulating international markets. Most of these ended with minimal impact for the companies and those in charge of the businesses.

It has set a precedent that sees white collar crime being seen as something minor compared to more traditional criminals. A bank robber, for instance, will get 2 years for stealing a few thousand dollars, whilst three bankers in Ireland who committed a €7bn fraud received an average jail sentence of 3 years. Many more white collar criminals are let off with only a fine, which is hardly going to dissuade people from acting nefariously when the rewards involved can be so huge.

However, this may be at an end, with some governments looking at the way they prosecute white collar crime. The UK Attorney General, Jeremy Wright has recently said that the government are currently consulting on 'failure to prevent' offences. David Cameron, the previous UK Prime Minister, announced the work being done in the Guardian, writing 'In addition to prosecuting companies that fail to prevent bribery and tax evasion, we will consult on extending the criminal offence of ‘failure to prevent’ to other economic crimes such as fraud and money laundering so that firms are properly held to account for criminal activity that takes place within them.' This essentially means that if an employee does something illegal, the boss will take a proportion of the responsibility and be prosecuted too.

This is likely to be a recurring theme throughout countries negatively impacted by large company failure, meaning that company leaders need to be informed of what their subordinates are doing.

Luckily, since the financial crash of 2008, we have seen a huge increase in the data available to them. It gives them the opportunity to monitor the actions of anybody in their company. It is unlikely to be popular if they suddenly start reading everybody's emails though and luckily with modern data uses it isn't necessary to take an Orwellian approach in order to monitor behaviour and actions.

Through machine learning algorithms it is possible to flag anomalous transactions that can then be investigated further, which would stop a considerable amount of financial fraud. If somebody is suddenly performing considerably above where they have historically performed, this would also be flagged and the reasons for this could be examined. It could uncover something totally innocent, which could then be shared throughout the department to improve overall performance, or it could show illegal action and action could be taken to stop it before it becomes too damaging.

Data being used by the HR team can even help to identify people who may be at risk of exploiting the system, helping leaders to make informed decisions about new employees and keep tabs on existing ones. For instance, if somebody is consistently underperforming the chances of them turning to illegal means to gain an advantage may become more likely. Through using HR data these situations can be pre-emptively tackled, stopping potential illegal activities happening in the future.

Dishonesty in business is something that Dan Ariely, Senior Fellow at Duke University Kenan Institute for Ethics, studies frequently and he has found that the more 'distance' there is from the eventual reward, the higher the likelihood of cheating and dishonesty. According to the experiments undertaken by Dan's team, it can be around double the rate. As more and more technology is put between two parties in a transaction, there is an increased likelihood that this will happen, but this same technology may well provide the data to eventually prevent it. 

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