The European Commission wants its own anti-fraud data-mining tool made mandatory for all EU states to crack down on financial crimes.

“It is a data collection, a data-mining tool which allows us to identify risks and more importantly also allows us to detect conflicts of interests,” Joost Korte, a senior European Commission official told MEPs on Thursday (11 June).

Known as Arachne, the software is already being used in around 20 member states and digs into big EU budget items like the structural funds – alone worth over €350bn.

Arachne stores the data of some two million beneficiaries of EU funds. It then cross-checks the data with more than 210 million companies and the some 120 million people behind those companies.

Plans are also in the works on predictive modelling to detect fraud.

“We need the legislature to roll Arachne out for all the member states and make it a compulsory instrument,” said Korte.

Korte is the head of the European Commission department that deals with employment and social affairs. Under his portfolio, he oversees some €85bn of EU-funded projects.

Conflicts of interest

But Korte also admitted that conflicts of interest should not be entirely eliminated because it would undermine the concept of “shared-management”.

Shared management means the Commission largely entrusts the paper work and oversight of how EU money is spent to the member states.

“There is a tension between them because the whole concept of the shared management fund is that we rely on politicians,” he said.

That money is often channeled through government structures, which may then be captured by a state-like mafia similar to examples found in Hungary and the Czech Republic.

In Hungary, the government has used EU agricultural funds to retain party loyalists, according to a New York Times investigation.

Andrej Babis, the billionaire prime minister of the Czech Republic, is also currently fighting court battles with the European Commission over Agrofert.

The firm, which is one of the biggest in the Czech Republic, received millions in EU funds.

Although Babis had placed Agrofert in a trust before taking up office, an EU audit found he actually retained control.

The EU complained that Babis cannot retain control over Agrofert while at the same time making decisions over the EU budget.

“We need to be careful that the conflicts of interest should not derail in such a way that it is impossible to find anybody who wants to be part of our shared management,” said Korte.

“I am not talking about Mr Babis, that is clear,” he added.

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Big data refers to the massive volumes of data collecting on everything from internet searches to app usage to IoT devices such as connected wearables. The development of tools able to find the useful pieces of information in this firehose of data and collate it into patterns is likely to impact numerous sectors.

One of the areas where big data has already started changing the business practises is the legal field. Examining this particular example shed some light on how big data is about to transform all the sectors where data intelligence really makes a difference. Here are a few ways massive data collection and computing is already transforming the legal field.

Identification of Potential Clients

Big data is allowing many law firms to identify potential clients much more strategically and at a lower cost. The classic case of sending a postcard to everyone who has recently received a DUI ticket is being replicated by law firms using data analysis tools to find clients like those who are running a business out of their home and may want business legal advice.

Another variation of this is the analysis of law enforcement actions, such as finding that one cop issues five times as many tickets as others or one specific jurisdiction is responsible for the vast majority of civil forfeitures. These abnormalities are not necessarily bad since Pareto’s Law says that you’ll have a few outliers in any situation. However, identifying the oddities gives you the situations that require investigation, as well as hard data that can be used to prove the case that cops in a particular town are looking for cash to seize or issuing citations for profit.

Offensive and Defensive Database Analysis

Data mining can find potential cases of fraud in billing, contract awards and sales. Data mining provides hard evidence of potentially biased hiring, promotion and firing practices. Conversely, data mining by police and data-driven policing can snare people who did nothing wrong but fit the “bad” profile or did something out of the ordinary.

Another form of data mining is the mining of social media, whether looking for the source of a data leak, determining the timeline of activities by a subject or getting a bigger picture of the impact on a business’ reputation after false allegations. In this regard, social intelligence tools used by marketing companies can be used to prove the harm caused by slander and data mining tools like RIOT and PRISM by the feds can likewise prove that someone was not where the state says they were. This is where the USD Law School and other institutions have started teaching data mining practices as part of their curriculum. It’s an interesting topic for law students and one that is likely to crop up more frequently in the future.

The massive amount of legal records to be mined offers opportunities, too. The ability to search for legal records about nearly anyone allows attorneys to identify those who have a pattern of filing frivolous lawsuits or suing for personal financial gain.

Data-Driven Decision Making

Data analysis and artificial intelligence can mine databases and connect different data sets, providing insights that otherwise take months or years of research. This can save massive amounts of time for law firms that know how to use these tools.

Big data is about to prompt one of the biggest shifts we have ever seen in the world of law. But it is also going to have some major impacts in the business processes of many other industries. And IoT, should play a key role in this transformation by fueling the market-specific’s big data algorithms with new types of information and insights coming from the billions of connected devices out there in the field.

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