FRAUD & SECURITY INSIGHTS
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- Analytics: A must-have tool for leading the fight on prescription and illicit drug addictionStates and MFCUs now have the analytics tools they need to change the trajectory of the opioid crisis by analyzing data and predicting trouble spots – whether in patients, prescribers, distributors or manufacturers. The OIG Toolkit with free SAS® programming code makes that possible.
- Payment fraud evolves fast – can we stay ahead?Payment fraud happens when a criminal steals a person’s private payment information, then uses it for an illegal transaction. As payment trends evolve, so do the fraudsters. Banks and PSPs can fight back with advanced analytics techniques that adapt quickly to spot anomalies in behavior.
- Online payment fraud stops hereBillions of dollars each year are lost to online payment fraud through channels that provide convenient – yet vulnerable – ways to shop and bank. See how to fight back and win with advanced analytics.
- Continuous monitoring: Stop procurement fraud, waste and abuse nowProcurement fraud, waste and abuse silently robs businesses an average of 5% of spend annually. And even when organizations invest in detection methods, they’re often let down by their techniques. Learn what continuous monitoring is and why this proven analytical method is key to fighting back.
- Unemployment fraud meets analytics: Battle lines are clearly drawnMany fraudsters seized opportunities presented by the COVID-19 pandemic. During the crisis, unemployment fraud became a battleground between international criminal networks and government agencies. Learn how analytics can save billions – and deliver benefits to those truly in need.
- Managing fraud risk: 10 trends you need to watchSynthetic identities, credit washing and income misrepresentation – these are just some of the trends to watch if you’re trying to understand how to manage fraud risk. Find out what’s on the top 10 list of trends according to experts like Frank McKenna and Mary Ann Miller.
- Next generation anti-money laundering: robotics, semantic analysis and AIAnti-money laundering taken to its next level is sometimes referred to as AML 2.0 or AML 3.0. What does this next wave of AML technology look like? What can it do that you can’t do with traditional AML? See the results innovative financial institutions around the globe are already getting.
- How AI and advanced analytics are impacting the financial services industryTop SAS experts weigh in on the topics that are keeping institutions up at night and fraudsters in a job.
- The best gift you can give to thieves this holiday season? Your identity.While the use of EMV in cards has helped to mitigate fraud perpetrated at retail stores, undeterred fraudsters have focused their efforts online. Find out how advanced analytics and machine learning help combat this threat.
- Applying technology to ensure voter integrity in electionsVoter integrity is becoming a serious concern for many elections. Recent disclosures of foreign influence campaigns using social media highlight the potential impact on the integrity of the democratic process. In monitoring your systems, technology can identify both legitimate and fraudulent activity; the balancing act is to minimize the impact on legitimate activity while preventing acts of cyber-criminals and fraudsters.
- Shut the front door on insurance application fraud!Fraudsters love the ease of plying their trade over digital channels. Smart insurance companies are using data from those channels (device fingerprint, IP address, geolocation, etc.) coupled with analytics and machine learning to detect insurance application fraud perpetrated by agents, customers and fraud rings.
- Detect and prevent banking application fraudCredit fraud often starts with a falsified application. That’s why it’s important to use analytics starting at the entrance point. Learn how analytics and machine learning can detect fraud at the point of application by recognizing the biggest challenge – synthetic identities.
- Medicaid and benefit fraud in 2018 and beyondTo curb the growing amount of Medicaid and benefit fraud and improper payments, agencies and their commercial counterparts need fraud and abuse detection systems with data management and analysis that can keep up and even stay one step ahead.
- Strengthen your payment fraud defenses with stronger authenticationThe rapid growth of digital wallets and payment applications ushered in many new payment fraud threats. Today, it’s more critical than ever to authenticate users. Learn four innovative to ways strengthen your authentication defenses while reducing false positives and protecting customers’ assets.
- What do drones, AI and proactive policing have in common?Law enforcement and public safety agencies must wrangle diverse data sets – such as data from drones – in their proactive policing operations. To be most effective, they need modern tools that support AI techniques like machine learning, computer vision and natural language processing.
- 4 strategies that will change your approach to fraud detectionTechnology advances are giving financial institutions a better arsenal than ever for fraud detection. Take a look at four ways to turbocharge your defenses.
- How to prevent procurement fraudPerpetrated in several ways, procurement fraud is difficult to detect. Arm yourself with hybrid analytics that offer various approaches for cross-pollination of data and analysis.
- Improve child welfare through analyticsWith tremendous potential for child welfare agencies to use data and analytics to prevent child abuse and improve outcomes for children and families, child welfare advocates discuss the benefits of using data and establishing a data-driven culture to advance practice and policy.
- 6 ways big data analytics can improve insurance claims data processingWhy make analytics a part of your insurance claims data processing? Because adding analytics to the claims life cycle can deliver a measurable ROI.
- Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Here’s what you’ll need to get started – from integrating supervised and unsupervised machine learning in operations to maintaining customer service while defending against fraud.
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