Fraud Prevention
What it is & why it matters
Fraud prevention technology has made enormous strides from advances in computing speeds (high-performance analytics), machine learning and other forms of artificial intelligence (AI). Fraud touches every area of our lives; it raises the price we pay for goods and services, squanders tax money, pulls resources from innovation and even costs human lives.
History
Fraud can encompass waste and abuse, improper payments, money laundering, terrorist financing, public security and cybersecurity. In the past, organizations had to take a fragmented approach to fraud prevention, using business rules and rudimentary analytics to look for anomalies to create alerts from separate data sets.
Data couldn’t be cross-referenced through automation, and investigators couldn’t manually monitor transactions and crimes in real time; they had to do so after the fact. In health care, fraud prevention was more like “pay and chase”, because the criminal was long gone by the time fraud was detected.
To combat fraud, newer technology has been developed to predict conventional tactics, uncover new schemes and decipher increasingly sophisticated organized fraud rings. This involves more than standard analytics; it applies predictive and adaptive analytics techniques – including a form of AI known as machine learning. By combining big data sources with real-time monitoring and risk profile analysis to score on fraud risk, fraud prevention has evolved to start turning the tides of losses.
Fighting Identity Fraud With Analytics
Identity fraud is a growing concern that affects both businesses and customers. Fraudsters now have easier access to more tools and data than ever before, causing identity theft to reach a record high. This chart compares account take over, card not present and other forms of identity fraud losses and their growth, which continues to rise.
Fraud Detection in Today's World
The growing complexities of state-sponsored terrorism, professional criminals and basement bad guys are becoming harder to understand, follow, expose and prevent. Fraud detection in today’s world involves a comprehensive approach to match data points with activities to find what is abnormal. Fraudsters have developed sophisticated tactics, so it’s essential to stay on top of these changing approaches of gaming the system.
Many times, cybersecurity breaches enable fraudulent activities. Take for example, retail or financial services: Once a luxury, real-time transaction monitoring is now a baseline requirement, not only for financial transactions, but for digital event data surrounding authentication, session, location and device.
To identify and stop an array of fraud attacks and crime quickly and accurately – while improving customer and citizen experiences – organizations should follow four critical steps:
- Capture and unify all available data types from across departments or channels and incorporate them into the analytical process.
- Continually monitor transactions, social networks, high-risk anomalies, etc., and apply behavioral analytics to enable real-time decision making.
- Instill an enterprisewide analytics culture through data visualization at all levels, including investigative workflow optimization.
- Employ layered security techniques.
The fraud detection and prevention technology that you choose should be able to learn from complex data patterns. It should use sophisticated decision models to better manage false positives and detect network relationships to see a holistic view of the activity of fraudsters and criminals. Combining machine learning methods – such as deep learning neural networks, extreme gradient boosting and vector machines – as well as proven methods such as logistic regression, self-organizing maps, random forests and ensembles – has proven to be far more accurate and effective than approaches based on rules.
Fighting Back Against Fraud
Just like the techniques fraudsters use, approaches to fraud prevention must constantly evolve. Learn more about how you can use big data and advanced analytics techniques to fight back.
Next-gen anti-money laundering
Robotics, semantic analysis and artificial intelligence – all can help financial institutions automate and improve effectiveness of AML processes. But how do you get started? Read about 10 keys to success with AML powered by machine learning.
Using analytics to combat digital fraud
Digitalization creates both opportunities and threats. Learn about the risk and fraud scenarios financial institutions should avoid, how big data and analytics help reduce digital fraud, and how innovative organizations are detecting fraud today.
Attacking fraud, waste and abuse
Governments spend billions to combat fraud, waste and abuse. And traditional methods of detection are no longer enough. Read three case studies to see how SAS can help – and discover the benefits of taking an enterprise approach to fraud detection.
Shut the door on insurance application fraud
Agent and customer gaming are growing problems for insurance providers. As fraudsters grow more sophisticated in their digital trickery, learn how insurers are keeping pace and beating them at their own game using analytics and AI.
Serving customers while protecting them from fraud
At Deutsche Kreditbank AG (DKB), the second largest bank in Germany, customers expect service in real time and maximum security for their online banking. But fraudsters constantly adapt and get faster. Recognizing the need for speed in detecting fraud and protecting customers, DKB turned to fraud detection and anti-money laundering solutions from SAS. Now the bank not only secures customers’ money – it also wins their trust.
Who's using fraud prevention?
Businesses and governments alike have embraced technologies like data visualization and artificial intelligence to greatly reduce and even prevent the economical and reputational repercussions of fraud. Analysts and investigators work together to break down siloes, score and prioritize alerts based on severity, then route high-priority alerts for more in-depth analysis.
Banking
Fraud is often perpetrated through synthetic identities, customer account takeover, nefarious applications, digital payments and authentication, procurement and other financial crimes. Financial institutions detect fraudulent transactions in real time with fewer false positives and detect money laundering or terrorist financing through complex algorithms looking at a multitude of factors.
Insurance
Claims fraud runs rampant, and application fraud is on the rise. Instead of the pay-and-chase approach – after money has been spent – data analysts are preventing fraud by applying algorithms to detect anomalies and patterns. Analyzing multiple factors to determine how claims fraud is perpetrated, not only can fraud be detected when it happens, but more importantly, fraud can be prevented before it’s too late.
Public Sector
Governments are now combining siloed data to catch tax fraud, predict intrusions, identify abnormal behavior, and to shut down real-time and future threats. All of this work enhances border security, gathers intelligence for law enforcement, monitors opioid abuse and keeps children safe.
Health Care
Health care claims fraud costs millions, even billions, worldwide. Health care organizations are successfully preventing fraud by taking an enterprise approach to payment integrity and health care cost containment by using advanced analytics.
How Fraud Prevention Works
Fraud detection and prevention is not a static process. There’s no starting and ending point. Rather, it’s an ongoing cycle involving monitoring, detection, decisions, case management and learning to feed improvements in detection back into the system. Organizations should strive to continually learn from incidents of fraud and incorporate the results into future monitoring and detection processes. This requires an enterprisewide analytics life cycle approach.
Your goals may involve fraud detection, compliance or security. As technologies like artificial intelligence and machine learning have become more prevalent, the next generation of technologies is automating manual processes associated with combining large data sets and employing behavioral analytics
Supervised Learning
Supervised machine learning algorithms learn from historical data, identifying patterns of interest that an investigator might want to flag.
Unsupervised Learning
Unsupervised machine learning assesses and examines data that does not contain identified fraud. It is used to uncover new anomalies and patterns of interest.
Network
Analysis
Network analysis to identify paths, connections and hubs that reveal patterns and social networks of interest that are essential to an investigator’s toolkit.
Text
Analytics
Text analytics to accurately identify expressions of names, times, companies, monetary values and more through search, content categorization, and entity extraction.
The rise of the digital economy has been matched by the rapid spread of fraud and cybersecurity risks. We want to meet customers where they are in their analytics journeys, particularly as they adopt technologies like AI, IoT and cloud. With SAS to help them, they will be better equipped to break down data silos, adjust to shifting regulations and safeguard against present and future risks. Stu Bradley Vice President, Fraud and Security Intelligence Practice SAS
Next Steps
Explore SAS Solutions for Fraud, AML and Security Intelligence
Trusted fraud protection
As today’s fraudsters work smarter and faster, you need a trusted partner to protect your business. As an industry leader in advanced analytics, SAS delivers proactive fraud protection through artificial intelligence and machine learning.
Featured Solution for Fraud Prevention
SAS® Visual Investigator
SAS Visual Investigator is a fraud detection, investigation and incident management solution that combines large, disparate, structured and unstructured data sources. Through a visual user interface, investigators can define, create, triage and manage alerts and perform detailed investigations to uncover hidden behaviors and activities.
