To prevent different high-profile data breaches, not only organizations but also government and cybersecurity experts are calling for some protective approaches towards data and protection. By using both artificial intelligence and machine learning, the cybersecurity experts recognize identity theft more efficiently and faster than previously.
Machine learning has invaded our everyday lives, from online communications to online recommendations on your favorite streaming site, which have all impressed the people. However, when it comes to identity theft detection, machine learning has improved potentially and have come up with changes in the theft detection process.
Before we get into this debate, let’s first briefly discuss the statistical perspective of cybersecurity in today’s era and how the innovations in AI-ML is proving beneficial in the cybersecurity industry. Let’s read on!
The number of fake transactions, massive data breaches, and cases of identity theft continue to rise as attackers and fraudsters are becoming more and more sophisticated. Back in 2017, the Equifax hack kicks off the new era in data security. The breach affected more than 147.7 million Americans and they also losses trust in data security. Although most Americans have become entirely uninterested in losing their privacy over sensitive information, what remains the problem was identity theft.
Without any doubt, identity theft is becoming a growing problem. According to the Javelin identity fraud study of 2018, it was found that a record of 16.7 million US adults experienced identity theft in 2017, which marks an increase of 8% from the previous year.
Similarly, one in every five victims of identity theft has experienced it more than one time. The Americans have been very prone to identity fraud as 33% of the US adults have been a victim to this fraud, which is more than twice the global average.
In this alarming situation, Artificial intelligence has changed the process of identity theft detection. AI, along with the ultimate support of machine learning, makes it possible to process, prove, and authenticate identities at scale.
Now, there are different ID scanning software having various strengths; some might scan the ID’s barcode, while others might perform biometric and forensic tests to make sure that the ID is not a fake one.
Artificial intelligence and machine learning have appeared as a problem solver to a wide range of issues in various applications and industries, and cybersecurity is one of them. Machine learning can advantage your cybersecurity practices, which must be every organization’s top priority.
Hackers are using different social engineering techniques and result in data breaches, while thefts and other attacks are also becoming common and are causing massive financial pressure and loss to the business. Based on the need and size of the organization, ML security software can make great use of your cybersecurity budget. The software can help in data protection, threat detection and response, and application security. Even if the security team feels some trouble while maintaining the internal data governance so, all these types of solutions can be a great option to use. Apart from this, you can also adopt the best identity theft protection services to prevent identity fraud.
Furthermore, machine learning uses the algorithm to trace and analyze the vast collection of data. All these programs run by identifying and encoding various patterns found within the data and improves their function as time passes. An AI-driven cybersecurity algorithm might be the only thing that is capable of sifting through the petabytes of information present on the dark web, considered as the internet marketplace for pinched data.
Cybersecurity experts are busy in the constant race against identity hackers and thieves. This race drives innovation on both sides. To stay safe and protected from threats requires the development of counter-measures, which need up-to-date and reliable data to create.
The cybersecurity experts are increasingly turning towards AI to boost up the speed and efficiency of the detection process. The following are some of how machine learning is now being able to detect, reduce, and prevent identity fraud cases.
With the help of machine learning, various identity documents such as drivers’ licenses, passports, and PAN cards are scanned and cross-verified with an unknown database in real-time. An extra set of authentication tests can detect and seize the theft to a great extent. This authentication test includes facial recognition and the use of biometrics while other examples of analysis also include OCR-barcode-magnetic stripe cross verification, microprint tests, and paper-and-ink validation.
Machine learning training can automate and operationalize the process of data analytics, particularly the tasks that are prone to human errors. Besides speeding the process of identity fraud detection, machine learning also enables real-time decision making to prevent theft in its tracks or sound a warning in case of a possible threat. It is a blessing for both small scale and large scale businesses who can’t afford to waste valuable human resources on everyday tasks. By detecting identity theft at fast speeds, ML enables the analysts to make some spot decisions before any damage is being caused.
An additional benefit of using machine learning to transform the identity theft detection process is pattern recognition. Since the machine learning algorithm is supported to a database with lots of data, these algorithms scan all the information available over the years to predict future threats and recognize the source and patterns so that preventive measures can be taken in advance. This creates a link among the individual theft cases, allowing the experts to assess the identification patterns better.
More data collected means the better machine learning algorithms are being trained for a variety of situations. Unlike many other cases where tons of data means more complexity, a broader database allows machine learning algorithms to get scaled and adapted as needed. Moreover, it also allows them to expand more accurately with every possible addition, making comparisons and identifying genuine and fraud transactions instantly. However, analysts must monitor the process because if the machine goes over an undetected fraud without flagging it, the more chances are that it will learn to ignore that type of fraud in the future, opening up a massive loophole in the system.
Machine learning is like an evolution that prevents billions of money being lost in data recovery, fraud, and theft. Organizations are increasingly allocating massive amounts of their budgets towards ML-based security systems. It is, in reality, a shred of evidence to show how technology has evolved in the identity theft detection process.