Fraud Detection: Harness the power of data


Learn how to protect your business with accurate real-time insights using the power of machine learning
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Fraud Detection Solutions: 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.

bank

Danske Bank successfully fights fraud with Machine Learning

The road to fraud detection success

In 2016, Danske Bank began to struggle with an increase in fraud, alongside an increase in the number of false positives being produced by their rule-based system. After investing in bespoke machine learning models to complement their existing systems, they achieved 20- to 30-percent reductions in false positives, faster response times, and improved customer experience.

Our mission was to not only solve the fraud situation… but to build generic components and capabilities that could be used for many other use cases.

Nadeem Gulzar

Mobile Deputy Engineering Manager, Netguru

Fraud detection goals


It's crucial to 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.
  • Sign

    Flagging fraudulent transactions

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

  • Structure

    Reducing false positives

    Fraud detection will improve the customer experience by reducing the number of legitimate transactions that are identified as fraud and declined.

  • Artificial-intelligence

    Using adaptive technology

    Machine learning is capable of learning new patterns without human intervention. Thus, it can adapt to new or previously unknown tactics that could be employed by cybercriminals.

  • Like

    Reducing manual intervention

    Manual systems or rule-based approaches are vastly time consuming and prone to human error or abuse. The automation provided by machine learning allows for great reductions in human input.

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Main advantages


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

    Financial security

    The most obvious benefit is a reduction in the direct financial losses incurred, because fraud is more accurately identified and prevented.

  • Scale

    System stability

    Customer experience is optimized leading to less frustration and fewer delays, and preventing the potential loss of sales.

  • Saving

    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.

  • Like

    Brand trust

    By eliminating potential risks we increase company credibility and build brand trust.

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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.

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.

Why is fraud detection so important?

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.

What are the 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.

World-class specialists at your disposal

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