Did you know that AI is contributing to fraud prevention?

The Annual Fraud Report released by UK Finance brought alarming data about the impact of financial frauds in the year 2022.

The document indicates that over £1.2 billion (approximately $1.5 billion USD at the current exchange rate) was stolen through different types of fraud, which equates to more than £2,300 (approximately $2,900 USD) stolen every minute.

Among the reported cases, 78% of the frauds originated in the online environment, while 18% began through telecommunications.

Another alarming statistic comes from PwC’s Global Economic Crime and Fraud Survey 2022, which shows that 51% of surveyed companies reported experiencing fraud in the past two years, the highest level in PwC’s 20 years of research.

These indicators send a clear warning: now more than ever, it is essential to invest in technologies to combat financial frauds.

Currently, there are many mechanisms that organizations use to enhance security and prevent threats. However, knowing that fraudsters’ approaches are evolving, it is necessary to resort to strategies that make it practically impossible for scammers to act.

And this is where the role of AI comes in, capable of identifying suspicious patterns, conducting behavior analyses, and detecting risky situations in real-time.

In today’s article, we discuss the importance of investing in Artificial Intelligence (AI) for fraud prevention and how it contributes to your company avoiding financial losses and reputation damage.

Stay tuned!

The Importance of AI for Fraud Prevention

Before explaining the importance of Artificial Intelligence in preventing fraud, it is necessary to understand how it works.

Artificial Intelligence simulates human intelligence using machines and computational systems. Machine Learning, a subset of AI, allows machines to be trained by algorithms to perform tasks and analyze large volumes of data.

When companies use AI-powered platforms, Analytics, and Machine Learning, they gain access to a plethora of resources to detect patterns and anomalies in data, thus identifying suspicious user behavior and fraudulent activities.

In the past, the incidence of fraud was lower because the mechanisms used by fraudsters were limited. There weren’t as many channels or ways to perpetrate scams.

With technological advancements and globalization, it has become much easier for fraudsters to operate in online environments, for example, and use telecommunication services to deceive and trick employees.

Artificial Intelligence has been applied by many business leaders to combat the behavior of these fraudsters, minimizing the risks associated with scams.

A survey conducted by the Digital Banking Report indicates that 75% of financial institutions consider using AI and APIs in their internal processes to improve services and reinforce security.

The idea is to use technology to combat frauds such as:

  • Identity theft;
  • CPF fraud;
  • SQL Injection;
  • Credit card fraud;
  • Among others.

How Artificial Intelligence Helps in Fraud Prevention

As Artificial Intelligence uses algorithms to train machines to perform tasks and analyze large volumes of data, it can help companies identify suspicious patterns, detect threats, conduct behavior analyses, and even reduce false positives.

How? We explain below:

Identification of Suspicious Patterns

Artificial Intelligence is programmed to examine information history and identify what the normal and regular patterns in the data are. These patterns serve as a basis for detecting deviations or any significant changes in the current data, whether generated or collected by the company.

In supervised machine learning, AI is taught from labeled data. In unsupervised machine learning, AI can detect possible suspicious patterns on its own based on data variation.

Real-Time Threat Detection

Machine Learning models are trained with large volumes of data, which means that Artificial Intelligence evolves, adapts, and learns continuously as new information is generated, inputted, and collected.

The algorithms can distinguish between patterns considered normal and abnormal by comparing the most recent data with established patterns. When unusual or suspicious activity is identified, AI can issue real-time alerts to enable immediate action.

User Behavior Analysis

Just like in threat detection, ML models can be trained to recognize and analyze user behavioral patterns based on available data. AI creates profiles using this information and establishes criteria to define normal activities.

After creating the profiles, Artificial Intelligence can analyze behaviors and identify any differences or deviations from users’ normal patterns.

Reduction of False Positives

Algorithms analyze large volumes of data to understand normal behavior patterns. Thus, they can more accurately differentiate between a real threat and false alarms.

In a study, the Information Security team of a large technology company revealed that combining human expertise with Artificial Intelligence resources in the assessment of incident alerts can reduce false positives by up to 70%.

Ensure Fraud Prevention with the Right Technology

The DHuO Data platform was developed by Engineering to maximize performance and technology integration in companies.

With the solution, teams can consolidate customer data from multiple sources, create pipelines for effective analysis, validate and prepare data for use, monitor Machine Learning models, and much more.

Through these and other features, it is possible to identify patterns of suspicious behavior that jeopardize the company’s financial security and reputation.

Strengthen your business’s security with technology that helps prevent frauds.