Organizations battle fraud armed with SAS® Analytics
International Fraud Awareness Week promotes awareness and education
The proof is in the data: Analytics-based, anti-fraud measures save organizations money. The Association of Certified Fraud Examiners (ACFE) found that organizations lacking anti-fraud controls suffered greater median losses – in fact, twice as much. SAS, the leader in analytics, works with organizations across industries to battle this global menace.
Globally, fraud costs about $3.7 trillion each year. SAS is a strong supporter and sponsor of International Fraud Awareness Week, an ACFE program that aims to minimize the impact of fraud through awareness and education. International Fraud Week takes place Nov. 13-19.
“Increasing volumes of data make advanced analytics crucial to detecting and preventing fraud,” said Greg Henderson, Senior Director of Fraud and Security Intelligence at SAS. “The typical organization loses 5 percent of annual revenues to fraud; to survive, they must manage the threat. By combining advanced analytics with deep domain expertise, SAS gives our customers effective tools to protect themselves against fraudsters.”
Worldwide, SAS works with banks, insurance companies, government entities and health care organizations to combat the growing incidence and sophistication of fraud, waste and abuse and money laundering.
Texas Capital Bank modernizes anti-money laundering
Consistently ranked among the Best Banks in America by Forbes, Texas Capital Bank doesn’t take that status for granted. Growing from $9 billion to $21 billion over the past three years, the bank has had to ensure its financial crimes systems keep pace as well.
“We are a relationship-focused bank and are very good at managing risk; however, our growth rate requires us to continually invest in best-in-class technologies,” said Phil Leary, Senior Vice President and Manager of Compliance Technology at Texas Capital Bank. “SAS is helping us take proactive steps to ensure our anti-money-laundering platform provides us with an appropriate control environment today and is scalable to meet future needs.”
Additionally, Leary’s team is using SAS® Visual Analytics to quickly fulfil requests from senior leadership and business partners for compliance-related data.
Iowa Department of Revenue uses data to keep pace with fraudsters
Iowa reports about 1.5 percent of filed tax returns are fraudulent – a fairly low percentage. But it can add up to millions of dollars in fraudulent refunds. That’s why fraud analytics sit at the core of the Iowa Department of Revenue’s (IDR) operations.
“Our mission is to collect all tax due – and no more,” said Courtney Kay-Decker, IDR’s Director. “Eliminating fraud, waste, and abuse – and using tax revenues responsibly – benefits all citizens of Iowa.”
IDR has built flexible tools and fraud models, such as the SAS Fraud Framework, that morph to meet fraudsters’ constantly shifting strategies. IDR is even incorporating publicly available data to try to identify characteristics that predict indicators of non-compliance or fraud.
Netherlands health insurer CZ proactively detects fraudulent claims
Transaction volumes in the millions each day, coupled with tight claim processing windows, advantage the fraudster. SAS is shifting odds in favor of payers like CZ with predictive analytics that stop losses before they happen.
“Rather than focusing on fraudulent activity after the fact, we now address it early on in the process,” said Fleur Hasaart, Program Manager at CZ, the Netherlands’ third-largest health insurer. “That way, we can take pre-emptive measures and proactively detect false declarations or fraud before we pay out claims.”
SAS supports additional fraud prevention efforts
Stateside, SAS is sponsoring the NHCAA Institute for Health Care Fraud Prevention’s annual training conference in Atlanta, Nov. 15-18. The agenda includes two sessions describing how companies work with SAS to detect and prevent fraud, waste and abuse. In one session, a large, nonprofit insurer will discuss its use of predictive modeling and network analytics to pinpoint connections, patterns and anomalies hidden in its data, including instances where initial data leads evolved into in-depth investigations with substantial findings.