Preventing identity fraud with data analytics

Identity theft is not a new-age problem; it has existed for decades, long before the advent of the internet. 

In the past, malicious individuals would sift through piles of discarded mail or look for lost credit cards to steal the identity of someone. They even assumed the identities of the departed to carry out their criminal activities.

Today, the internet has become a hotbed for identity thieves, and with the increasing integration of the World Wide Web into our lives, these pretence-personalities are unlikely to slow down.

According to the Australian Institute of Criminology, the economy loses $2 billion each year due to identity theft. More worryingly, one in four Australians report being a victim of identity theft at some point in their lives.

Identity theft can be devastating for a victim; they may not only lose money but also get in trouble with the law because of the actions of their digital doppelganger.

The good news is that data analytics can help prevent these thefts from happening. 

In this post, let’s explore how data analytics can be leveraged to curb identity theft.

Machine learning is a key weapon in combatting identity theft

Today, businesses process vast amounts of personal financial, and healthcare information of their customers, which are much sought after by identity thieves.

Using this data, criminals can easily bypass the security of traditional data analytics methods, as using these methods to detect and prevent identity theft is difficult at the best of times.

That said, with machine learning, this process becomes much faster and easier. These algorithms get smarter over time as they interpret data, enabling them to detect odd patterns in consumer behaviour.

For example, if a person who usually purchases inexpensive items suddenly starts spending money on lavish goods, that could indicate that they may be a victim of identity theft.

When these strange spending patterns are detected, ML algorithms can flag them, stopping identity thieves before they inflict more damage.

Risk analytics can power predictive policing

Identity thieves may lurk among regular customers, and risk analytics solutions can help identify potential identity thieves and create a profile for them by analysing the behaviour of thousands of individuals.

These profiles can then be used alongside other customer data to detect any potential identity theft attempts.

Factors affecting the use of data analytics for identity theft prevention

Data analytics can prove extremely useful in detecting fraud and preventing identity theft, but several factors may affect how effectively your company can detect identity theft.

Changing patterns

Data from identity thefts never reveal similar patterns; identity criminals change their modus operandi all the time, making it difficult to establish relationships between the data and identify unusual patterns.

Analytical data models used by businesses may become outdated, and the insights you get from big data analytics and machine learning may not be perfectly accurate.

To work around this, make sure your analytics algorithms are trained to detect the latest MOs of identity thieves.

Lack of initial success

Sometimes, the initial attempts at preventing identity theft through data analytics may not bring positive results. When companies do not achieve the identity theft prevention rate they were hoping for, they may forgo the use of data analytics. 

Be prepared to put in the effort to effectively police identity theft, even if it does not achieve the desired results in the short run. 

Lack of professionals

Not every company will have data analytics professionals to make sense of the data they collect, and with the increasing demand for data scientists these days, finding an experienced professional is difficult, especially for small businesses.

The data scientist will also need to know about cybersecurity and the data patterns associated with identity theft.

Educate your existing teams by leveraging data analytics training programmes to increase their analytics skills and prevent identity theft. 

Keep your company and your customers safe from identity crimes

Identity crimes not only affect individuals, but they can also cause irrecoverable damage to even the largest of companies if not handled properly. 

While cybersecurity solutions are available for protection against these crimes, there is no denying that data analytics play a huge role in curbing identity theft.