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Senaste artiklarna inom bedrägeri och säkerhet

Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Discover what you’ll need to get started defending against fraud – from integrating supervised and unsupervised machine learning in operations to maintaining customer service.
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.
How AI and advanced analytics are impacting the financial services industryTop SAS experts weigh in on topics that keep financial leaders up at night – like real-time payments and digital identity. See how advanced analytics and AI can help.
Intelligent policing: Data visualization helps crack down on crimeLearn how data visualization can give police real-time views of locations enriched with other data to help them make intelligent, fact-based decisions.
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.
4 strategies that will change your approach to fraud detectionAs fraudulent activity grows and fighting fraud becomes more costly, financial institutions are turning to anti-fraud technology to build better arsenals for fraud detection. Discover four ways to improve your organization's risk posture.
Know your blind spots in tax fraud preventionTax agencies sometimes miss fraud that's happening right under their noses – despite robust external fraud prevention efforts. Find out where traditional tax fraud prevention and detection efforts fall short, and how analytics can change that.
Are you covering who you think you’re covering? Payers often don't focus enough on healthcare beneficiary fraud in public and private healthcare plans. Before paying a claim, payers need to ensure beneficiaries are eligible. Advanced analytics applied to a broad range of data can help them accurately detect and prevent beneficiary fraud.
Containing health care costs: Analytics paves the way to payment integrityTo ensure payment integrity, health care organizations must uncover a broad range of fraud, waste and abuse in claims processing. Data-driven analytics – along with rapid evolutions in the use of computer vision, document vision and text analytics – are making it possible.
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.
Top prepaid card fraud scamsThe margin for prepaid cards is slim, so it's particularly important to root out the scams. Here are some tips for combating and mitigating prepaid card fraud.
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.
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.
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.
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.
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.
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.
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.

Bedrägeri & säkerhet: Introduktion

Ingen organisation vill drabbas av bedrägerier - vilket Association of Certified Fraud Examiners definierar som “varje planerad eller avsiktlig handling som syftar till att beröva någon annans ägodelar eller pengar genom bedrägeri eller på annat sätt.” Ändå är bedrägerier en av de största riskerna som organisationer utsätts för idag. Bedrägerier och ekonomiska brott kan utföras av anställda, kunder, återförsäljare, leverantörer och andra. Oavsett var och hur det sker är det viktigt att hålla sig uppdaterad om de senaste trenderna och teknikerna, för att effektivt bekämpa bedrägerier. Utforska ämnet bedrägeri med hjälp av ledande experter inom området.

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Vad är bekämpning av bedrägeri?

Förekomsten av bedrägeri, slöseri och missbruk ökar i alla branscher och drivs delvis på av världsekonomins fluktuation. Oavsett bransch - bank, försäkring, hälsa- och sjukvård eller offentlig sektor - kommer din organisation sannolikt känna av effekterna från detta, och förväntas agera.

För att effektivt bekämpa bedrägerier måste du löpande förbättra hur du övervakar olika kundbeteenden, i samtliga system. Få de senaste insikterna om hur du bekämpar bedrägerier och ta del av tekniken som ger din organisation de bästa förutsättningarna att upptäcka bedrägerier, på alla nivåer.

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Vad är bekämpning av penningtvätt (Anti-Money Laundering, AML)?

Kriminella använder penningtvätt - en form av ekonomisk brottslighet - för att dölja var deras “svarta pengar” kommer ifrån, så att de framstår som lagliga. Bedragare följer en avancerad trestegsprocess för att dölja sina spår.

Banker och andra finansiella institut bevakar och rapporter misstänksamma aktiviteter för att bekämpa penningtvätt. De gör så av moraliska skäl och för att följa aktuella lagar och regler.

Listan på kriminella aktiviteter som kopplas till penningtvätt är lång. Smuggling, förskingring, mutor, finansiering av terrorism, prostitution samt handel med människor och droger är bara några exempel.

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