Fraud detection systems: Harness the power of data

Protect your business with accurate real-time insights using the power of machine learning

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Fraud detection systems: A safeguard for your business

Technology is evolving at a breakneck pace, and it’s giving us unprecedented business opportunities, but the capabilities of cybercriminals are also very extensive at the same time. The challenge is to implement an effective fraud detection and prevention strategy that will help you stay one step ahead of the criminals. What is even more important, it should be able to adapt to the ever-changing threat. The key to realizing that ability is artificial intelligence.

Technology is evolving at a breakneck pace, and it’s giving us unprecedented business opportunities, but the capabilities of cybercriminals are also very extensive at the same time. The challenge is to implement an effective fraud detection and prevention strategy that will help you stay one step ahead of the criminals. What is even more important, it should be able to adapt to the ever-changing threat. The key to realizing that ability is artificial intelligence.

Systems using machine learning algorithms can detect suspicious transactions in real-time with a significantly greater accuracy than traditional fraud detection methods. They are also able to ‘learn’ new patterns of fraud, giving you greater protection from attacks, which are evolving and becoming more complex.

Machine learning-based fraud detection systems considerably decrease the risk of financial loss, improve customer experience by reducing false alarms, and safeguard your reputation.

What is fraud detection?

The emergence of the digital economy has transformed our lives, but it has also brought along new opportunities for cybercriminals to make profit from weaknesses in our online systems. Gone are the days where unsophisticated attacks were a one-man operation―online fraud is now an organized, multi-channel, and billion-dollar business.

In its simplest form, fraud detection is the identification and separation of fraudulent transactions from those that are genuine. Traditionally, fraud detection has been achieved by manual procedures or complex rule-based systems, but these are becoming more limited in their success rates. Cybercriminals are now evolving at a frightening pace and adapting their tactics to circumvent organizations’ fraud prevention and detection measures.

In response, a combination of technologies is needed to defeat fraudsters, and artificial intelligence is at the heart of those technologies. Complex machine learning algorithms can be used on large volumes of data to create systems that ‘learn’ new patterns of fraud and single them out with unrivalled accuracy.

The importance of fraud detection systems

Fraud is a major issue in banking and finance, as well as the insurance and retail sectors. Every year, fraud costs companies billions of dollars, and as cybercriminals are evolving and becoming more sophisticated, the problem is set to grow.

The true cost of fraud runs far deeper than the financial losses incurred directly. Organizations that suffer from weaknesses in fraud prevention and detection are also at risk of suffering damage to their reputation and loss of public confidence.

In addition, an effective fraud detection system is essential to ensure that customer experience is not impacted. Delays in transactions or legitimate activity being incorrectly identified as fraudulent and declined can cost businesses sales and, ultimately customers, which will negatively impact the bottom line. A good system should detect fraud with a high degree of accuracy, in real-time.

Challenges in fraud detection

Detecting and preventing fraud is not without its challenges. For machine learning to be effective, the availability of large volumes of good quality data is essential. Using artificial intelligence in fraud detection is still a relatively new concept. Many organizations do not have sufficient existing data to enable them to move to advanced data analytics immediately. On top of that, some organizations that do have the necessary information may be unwilling to reveal sensitive data due to privacy issues.  

Even where large quantities of data are readily available, there can be relatively few fraudulent transactions within these data sets, which can result in the problem of imbalanced classes. A system’s predictive power hinges on its ability to form correlations in data, which may be missed if too few cases of fraud are available. Data scientists must work to overcome this challenge and can employ several techniques to do that—from specialized ensemble techniques for outlier detection to data transformation methods such as oversampling or undersampling.

Another important issue is choosing the appropriate single model evaluation parameter. A single value is important from the automation point of view (it is easy to compare different models). Accuracy is not a suitable metric for imbalanced data sets, such as the ones used in fraud detection. When dealing with a binary classification problem, the two most common error measures are taken into account: false-positive and false-negative probabilities. The first one predicts the occurrence of an event if there was none, while the latter predicts no event even though there was one. From a business perspective, the cost of misclassifying fraudulent transactions is greater than the cost of misclassifying legitimate transactions. Therefore the ideal score should take into account both factors and their business value.

Although advances in technology are great for business, ironically, they are also being leveraged by cybercriminals. Their methods are constantly evolving and becoming more and more complex. New ways of avoiding detection and exploiting weaknesses in organizations’ online systems are developed every day. Therefore, the dynamic character of fraudulent activity presents a challenge to businesses, forcing them to react and adapt quickly to changes in attack patterns. 

Project goals

We will help you set the right project objectives and make sure it is executed seamlessly, in order to protect your business with machine learning systems from a variety of perspectives.  Our team of machine learning experts has a strong focus on finding the best solution that will meet your needs.

Flagging fraudulent transactions. The major objective for an effective fraud detection system is the accurate identification of suspicious activity in real time, protecting the business from financial losses.

Reducing false positives. The accuracy of a good system will improve the customer experience by reducing the number of legitimate transactions that are identified as fraud and declined. This can prevent lost sales and, potentially, lost customers as well.

Using adaptive technology. One of the greatest advantages of using machine learning in fraud detection is that besides its capability for identifying and flagging already known patterns of fraud, it is also capable of learning new patterns without human intervention. Thus, it can adapt to new or previously unknown tactics that could be employed by cybercriminals.

Manual intervention should be greatly reduced. Previously, fraud detection efforts relied on manual systems or rule-based approaches. Creating rules and reviewing flagged transactions were heavily manual processes, which was not only vastly time consuming but also prone to human error or abuse. The automation provided by machine learning allows for great reductions in human input.

Main advantages

The advantages of implementing effective fraud prevention and detection strategy are immense. Let’s have a look at some of them.

Financial security
The most obvious benefit is a reduction in the direct financial losses incurred, because fraud is more accurately identified and prevented.
System stability
Customer experience is optimized leading to less frustration and fewer delays, and preventing the potential loss of sales.
Cost reduction
As the fraud detection system moves from rule-based to automated mechanisms, operational costs go down because far less human input is needed.
Brand trust
By eliminating potential risks we increase company credibility and build brand trust. 
  • How we do it at Netguru?

    At Netguru, we have years of experience in developing cutting-edge, data-driven systems. Our strength in building dynamic fraud detection systems means you can be sure that your business is in good hands. Talk with us and find out how we can help you protect your business by harnessing the power of machine learning.
    10+
    Years on market
    1400+
    Projects delivered so far

Who can benefit?

There is no limit to who can benefit from effective fraud detection systems. If you conduct business online, then you may be susceptible to attack by cybercriminals. Fraud is certainly most prevalent in the banking and finance sectors, but insurance providers, online retailers, e-commerce companies, and payment service providers are also highly at risk and stand to benefit greatly from implementing effective systems.

Learn more about fraud detection systems

Machine Learning engineers are happy to share their knowledge of and expertise in fraud detection systems. They can advise you on the important aspects you will need to consider when implementing these solutions into your software. Check out our machine learning knowledge base.

Industries we excel at

Our clients are businesses of different types and sizes. We developed projects for companies in a range of industries: finance, media, healthcare, education, government, real estate/property, social media, travel/transport, e-business, e-commerce, to name a few.

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Trust is a foundation for great products

Our clients often cooperate with us for many years, launching various products. It's because we always strive to become trusted technological partners who care to provide more than just software development expertise. Thanks to that, we work as an extension of our clients’ teams rather than an outsourcing agency.

What I appreciate in working with Netguru: that you take the ownership, you're experienced, and that we can rely on you.

Peter Grosskopf
CTO, solarisBank

The difference between Netguru and other companies with which we have worked so far is that Netguru is good at taking the ownership.

John Furneaux
CEO, Hive

Netguru is a great service provider. I like working with the team, it’s super professional, and they’re nice people to work with.

Asaf Davidi
VP of Product, temi

Build your business security with Netguru

Machine Learning engineers at Netguru will help you to protect your business from the potential security flaws with the machine learning-based fraud detection systems.

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