Running a business in today's market challenges tackling identity fraud, which can be one of the most complex and costly tasks. Failing to meet regulations can result in financial loss and damage the company's reputation, making fraud management a crucial concern in the boardroom.
Although fraud is not a new problem, the fraud and identity theft industry is experiencing a resurgence. The increasing reliance on digital services and products has provided many advantages to businesses but has also created new opportunities for fraudsters. As more people access online services and applications from remote locations across the globe, the potential for digital identities to be accessed has increased. The fact that the fraud sector is now worth trillions proves that identity theft is no longer just a hobby for hackers.
Business continuity is now dependent on the shift from analog to digital offerings, and organizations across industries are undergoing digital transformation to reinvent themselves. As a result, the number of interactions between digital platforms and customers is growing, providing more opportunities for fraudsters to exploit.
Using data to win the race against identity fraudsters
Regarding identity theft, we are seeing a surge in both attempts and the complexity of attacks. Essentially, as our digital world booms, so does that of cybercriminals. So, how can businesses navigate this evolving fraud landscape? As a critical first step, they should look to embrace data, collaboration, and emerging technologies like advances in identity verification.
What’s more, how technology can support identity theft prevention is evolving, and we can now use data as a core part of these defenses. An ever-growing, flexible, multi-dimensional data set can support decision-making, risk assessments, and market understanding. There’s, therefore, no doubt that leveraging the potential of connected data points to support identity theft prevention measures will become ingrained within future business operations.
Preventity identity theft with verification
Identity verification lies at the heart of fraud prevention. However, with each step forward in identity verification technology, there’s also a progression in identity theft methods. Ultimately, the battle between the two drives the need to continue innovating and developing new verification technologies.
Most recently, we’ve seen businesses face the challenge of remote identity verification. Under remote working measures, businesses have suddenly shifted from in-person to digital verification, leaving a gap that the online world doesn’t provide: the certainty that a person behind the screen is who they say they are.
In a time of such heightened concern, there’s more need now than ever before for businesses to be proactive in their fraud prevention solutions – to protect themselves and their customers. Identity verification technologies can be quickly and easily implemented into business services. By embracing connected datasets, organizations can benefit from more intelligent, up-to-date, and relevant insights to verify who is a legitimate customer and who’s a fraudster.
Strengthening defences with data orchestration
Data orchestration allows organizations to coordinate the use of data effectively and efficiently through one layer. As risk officers strive to equip their organizations to be safe and secure, we see a transition from data orchestration being a future goal to becoming intrinsic to secure operations. For example, during onboarding processes, identities will be verified and cross-checked with existing datasets to determine if the person is who they say they are or if they’ve committed fraud in the past. This adds a contextual layer to fraud and identity theft prevention measures, improves accuracy, and ensures compliance.
Manual data processes and siloed systems are replaced with intelligent datasets to generate real-time responses to identity theft and fraud. Behavioral in behavioral (i.e., insight into a customer’s behavior as behavioral data) is increasingly used to detect the abnormal and increase the power of fraud detection. Furthermore, there is a growing emphasis on streamlining processes, so neither customers nor fraudsters are aware of them.
As a further example, device data can indicate potential identity hijacking. Consider the example where an individual’s mobile number has changed sim cards shortly before a significant transaction occurs. Using data to spot suspicious patterns of behaviors such as this can be vital to both the organizations, such as retailers or banks, and the customers involved.
AI and ML: The future of fraud prevention
Combining data and critical behavioral intelligence with technologies such as machine learning (ML) can open up possibilities for tackling fraudsters. Machine learning can uncover previously unknown fraud patterns and use automation to support real-time fraud detection and prevention. Furthermore, ML can reduce mistakes, improve accuracy, and boost efficiency, sharpening operations overall. Organizations across industries are making real progress here and investing more and more.
Ultimately, fraudsters are continually evolving and developing new ways of stealing identities - their methods can change daily. By adopting data and intelligence-driven strategies and leveraging sophisticated AI and ML capabilities, enterprises can detect potential identity theft as it happens, mitigate the impact – and, therefore, stay ahead in the fraud race.
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