4 Strategies that Will Change Your Approach to Fraud Detection

Fraudsters and money launderers are getting more sophisticated. We’re getting more sophisticated at fighting back.

When it comes to fraud, even the most cynical and careful among us are at risk. According to Javelin Strategy & Research:

  • In 2017, there were 16.7 million victims of identity fraud, a record high that followed a previous record the year before.
  • Account takeover losses reached $5.1 billion – a 120 percent increase from 2016. Consumer victims pay an average of $290 in out-of-pocket costs and spend 16 hours on average for resolution.
  • Card-not-present fraud is now 81 percent more likely than point-of-sale fraud; EMV chip cards are driving fraudsters to online channels.
  • One and a half million victims of existing account fraud had an intermediary account opened in their name first. This is 200 percent greater than the previous high.

Fortunately, advances in fraud detection technologies are giving financial institutions a robust arsenal for attacking fraud and financial crimes. Let’s take a look at four ways to turbocharge your defenses.

Download the SAS white paper to learn more about how AI can fight crime.

Bring new accuracy and efficiency to fraud detection with artificial intelligence.

Machine learning – a form of artificial intelligence (AI) – is a powerful force for improving both the accuracy and efficiency of fraud detection.

Find more fraud – earlier – with machine learning.

Unlike rules, which are easy for fraudsters to test and circumvent, the application of machine learning through analytics has been the standard for the SAS® fraud and financial crimes solutions for many decades.

  • Supervised machine learning algorithms can self-learn from targets within the data, flag anything that doesn’t fit the observed norm and then apply this knowledge to new and unseen data.
  • Unsupervised machine learning uncovers potentially suspicious risks you might not think to look for, since it works without being given a target. Instead, it looks for anomalies in the data.

It’s easy to see how these techniques, combined in an ensemble model, provide a valuable breadth of coverage across current risks, as well as new and emerging threats.

Machine learning reduces false positives from existing approaches while identifying previously unknown risks. For example, SAS deployed a digital payment model that demonstrated rapid success for real-time fraud detection. It detected 50 percent of fraud, alerting on only 0.5 percent of the portfolio, with very few false positives.

Use machine learning to improve process efficiency.

Financial institutions want to reduce the costs of managing fraud and financial crimes operations that have risen to an unsustainable level given the speed and dynamic nature of attacks. With machine learning, systems can automatically:

  • Create and update rules for detection and alert handling. Machine learning can examine masses of data to help establish rules and keep them current. Even something as simple as a decision tree can add some benefit (certainly in the segmentation approach) to more accurate rules. A typical branch of seven or eight nodes that points to a fraud-rich area in the data is a pretty easy discovery for a machine, but a very difficult one for a human.
  • Select the most accurate detection models. For many years at SAS, we’ve used a combination of machine learning techniques to deliver the most accurate detection rates. Our approach allows newer techniques – like gradient boosting and support vector machines – to enhance proven methods such as neural networks. We now have interfaces that can intelligently launch 10,000 iterations of strategies so that the process is much more automated with limited human intervention.
  • Automate processes for investigation. On average, 60 to 70 percent of an investigator’s time is spent collecting data about a subject. Machine learning can guide systems to automatically search and retrieve data, run database queries and collect information from third-party data providers without human intervention. We’re seeing clients reduce time to case decision by 20 to 30 percent.

Converge fraud, anti-money laundering and cyber events.

Financial institutions are taking advantage of big data architectures to consolidate data across typically isolated functions. It only makes sense to bring these functions together for a more holistic view of risk.

Much of the data is similar, regulations are pushing risk identification closer to real time and there is abundant opportunity to reduce operational costs and enhance efficiency while developing a cross-functional view. In fact, regulators in some countries expect firms to have a holistic view of risk across functions and to report cyber events as part of their normal AML and fraud SAR reporting obligations.

Several SAS clients score transactions for multiple types of risk during a single engine process.

Whether you’re reviewing a loan applicant or a payment transaction or uncovering terrorist financing, there are opportunities to learn from every interaction. Achieving real-time agility in a faster payments world is essential because retail and financial entities are adapting to meet customer demands for convenience, intensifying the risk environment:

  • The rise of mobile and online activity has reshaped expectations for speedy authentication across all contact channels. You need a combination of customer, device and session behavioral analysis to prevent losses.
  • More forward-thinking intelligence units are finding common elements – domain names, IP addresses, devices, etc. – that reveal transnational criminal organizations previously undiscovered by siloed functions.
  • The demand for real-time fund availability began with the U.K. banking initiative Faster Payments Service (FPS), which reduces payment clearing times from three working days under the legacy BACS system to a few hours. New payments systems in the U.S. and Australia – coupled with the growth of fintechs – are bringing this down to a few seconds.

Once a luxury, real-time transaction monitoring is now a baseline requirement for all payment types, incorporating not only financial transactions, but also authentication, session, location and device event data.

A tier-one global bank reported that when it adopted SAS’ 100-percent real-time decision making, it not only significantly reduced fraud, but also increased card revenue by $50 million in the first year of adoption due to lower false positive rates and improved customer experience.

Adopt more agile know-your-customer (KYC) processes.

High-profile leaks, such as the Panama Papers, dramatize the need for more transparency into the real owners or beneficiaries of corporate and legal entities. At the same time, the financial industry has seen deposit account and credit application fraud rise to 20 percent of all banking fraud. Both of these realities redefine the expectations for KYC processes. SAS is responding by:

  • Fortifying and speeding authentication processes that validate digital devices and in-person applicants.
  • Using robotic process automation (or RPA) to automate searches and queries of third-party data during an enhanced due diligence process.
  • Supporting new data elements such as ownership percentages and controlling interests.
  • Offering investigative interfaces that streamline the ad hoc process of gathering external unstructured information, including numbers, text, images and video.

For document query and retrieval, we’ve been developing some interesting capabilities for image recognition using natural language processing that are showing remarkable results. In one test case, we reduced the time to identify, classify and analyze trade documents from 700 hours to a matter of minutes.

In another pilot, we scanned approximately 9,000 SWIFT messages looking for things such as Palestinian boycott language. A human would need about five to seven minutes to review each message. During this pilot we found we could do image recognition and contextual analysis of those messages in less than one second per message.

Streamline investigations with intelligent case management.

Much of the initial adoption of AI focuses on automating manual processes to reduce the costs of running a fraud and financial crimes front line. Investigators shouldn’t spend their valuable time on rote tasks that machines can do better. An advanced, analytics-driven alert and case management solution can automatically:

  • Prioritize cases, recommend investigative steps and fast-track straightforward cases.
  • Enrich alerts with detail about the associated customers, accounts or beneficiaries.
  • Intelligently find and pull data for a case from internal databases or third-party data providers.
  • Present data in easy-to-understand visualizations, appropriate for the type of activity under review.
  • Allow automated prioritization of client contact strategies where suitable.
  • Autopopulate and prepare SARs for electronic filing if applicable.

SAS has been automating operational processes for years, including alerts prioritization and triage and investigative processes and dashboards. Right now, SAS is prototyping an adaptive learning system that embeds analytics into our alert and case management capabilities to automate processes and continuously learn from outcomes. This enables the system to adapt to new financial crimes risks.

We continue to push the envelope on behavioral analytics, cognitive computing and deep learning so we have the most effective financial crimes risk platform possible.

This article originally appeared on SAS Insights and was republished with permission.

[Top photo: Tookapic/Pexels]

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David Stewart manages strategy development, product management and marketing counsel for SAS fraud and compliance solutions worldwide.

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Ian Holmes provides fraud expertise to drive the enhancement, pre-sales and business implementation of SAS Fraud Management globally with current and potential customers.

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