Is AI the Future of Tackling Fraud?

Is AI the Future of Tackling Fraud?

Bureau Team
Bureau Team

February 24, 2023

Bureau Team

February 24, 2023

Technology has brought rapid advancements to life and business. However, this growth we're experiencing has also led to the proliferation of cybercrimes and fraud, as new equipment and strategies have made committing criminal activities much more effortless, with a lower risk of getting caught. The Federal Trade Commission received around 1.7 million reports of identity theft in 2021, and almost 84,000 Americans reported new account bank fraud that same year. Without the proper cybersecurity or technology, people and organizations risk losing a lot from these crimes.

One major challenge to the tech industry will be increased attacks on legacy systems in organizations worldwide. These critical infrastructures across the country will be at risk, so it’s no surprise businesses are prioritizing positions that can find abnormal fluctuations and secure data. Unfortunately, filling this demand has been difficult with academia struggling to propagate data science courses. This has caused many universities to adapt their courses to be 100% remote. Online data science degrees are enabling a wider pool of talent to improve company efficiencies, decrease costs, evaluate investments, and more. Given their expertise in programming languages, predictive modeling, and machine learning, it’s no wonder that growth for these specialist jobs can go as high as 27%. Through their insights, businesses can work towards solutions against fraud harnessing modern technology — specifically in artificial intelligence (AI).

Artificial intelligence is a term that loosely describes the ability of computers or robots to think and act like humans. In this article, we'll explore how AI can be utilized to tackle fraud:

Developing digital identities

The proliferation of e-commerce and online payments has made it difficult for retailers and businesses to gauge a customer’s intentions or if a fraudulent purchase has been made. In place of a face-to-face relationship between seller and consumer, digital profiles and relationships are now constructed by technology. The chief revenue officer at Kount, Jared Kernodle, notes how AI is used to harvest consensual consumer data to create these digital customer identities. Anytime a customer interacts with a business digitally, machine learning and behavior analytics technology can determine whether they are good actors by studying their interactions and their behaviors. This can prevent fraud from occurring even before a transaction is made.

Suspicious pattern detection

AI is usually trained through unsupervised learning, where it is fed a large amount of data and explores and analyzes it for meaningful patterns and trends. Any broken rule or abnormal activity the system detects gets flagged. Will Douglas Heaven of MIT Technology Review points out that this process won’t always provide clarity regarding what the AI deems suspicious, but the more the system flags transactions that break the rules, the more it can adapt to new information and patterns and increase its accuracy. As it is near impossible for the human eye to pinpoint subtle, strange activity, AI steps in to point them out. This machine learning system can help bring unexpected issues to a company's attention, making fraud easier to distinguish from regular payments.

Preventing credential stuffing

Credential stuffing is a type of automated threat that attempts to input or "stuff" common login credentials in hopes of gaining access to an account. Though this type of fraud has a low success rate, there is still a chance that a successful hit could come up. It is incredibly difficult to detect credential stuffing since a regular login and a fraudulent one can't be easily distinguished from each other. Websites with high traffic may not be able to identify these cases at all. AI can prevent credential stuffing by verifying the legitimacy of emails as they make an order, checking to see if a human is behind the account or not through the digital signature. It monitors every visit to the site and every activity from customers to employees, making sure there aren’t any logins causing issues.

Going above and beyond with deep learning

Most organizations, such as banks, use machine learning AI systems to tackle fraud, but that also has its limits. While it can detect abnormalities in the data it's given, it's still limited by human insight and logic and cannot dig deeper than that. Deep learning is emerging as a more effective and efficient way of combating fraud as it looks at the context surrounding a suspicious transaction, so it can figure out if it doesn’t fit into a customer’s usual patterns. In the World Economic Forum’s write-up on deep learning, this AI software thinks more closely to the human brain in the way it learns from examples. It looks at past and present data to make predictions for the future, stopping fraud in its tracks with much greater reliability and ease.

AI is becoming more widely used for businesses to find and shut down cases of fraud. With more developments in this technology, it may stay one step ahead of cybercriminals. Putting a stop to these threats and attacks protects essential data and information, increases business performance, boosts sales, and prevents losses, helping you maintain customer trust and satisfaction.

Key Takeaways:

  • Technology has made fraud easier to get away with, but AI can make detecting it more efficient.
  • AI helps develop digital identities to determine good actors.
  • AI prevents attacks such as credential stuffing.
  • AI can point out and flag suspicious patterns in transactions.

  • AI can use deep learning to take past and present data to make predictions.

  • Developing AI can prevent more fraud cases from occurring and bring in more business. 

Written By Prim Warner

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