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Why fraud prevention and member satisfaction go hand in hand

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As financial institutions that pride themselves on prioritizing satisfaction above all else, credit unions are constantly striving to meet increased scrutiny and provide better protection to their members. Even in today’s fraud-ridden environment, credit unions have the opportunity to achieve this level of security and member service by deploying complex technologies that enhance satisfaction while keeping their members safe.

The Ponemon Institute and Accenture have reported that as consumers become more connected and money movement edges closer to real time, financial services' information-security risk will continue growing. One estimate shows cybercrime damages ballooning to $6 trillion worldwide by 2021. This number is twice as high as it was in 2015.

Results of a 2018 survey show 62% of U.S. consumers believe they are at higher risk of fraud now than they were two years ago and, while the number of reported data breaches in the United States actually decreased 23% from 2017-2018, there was a 126% increase in the number of exposed records with personally identifiable information.

These records include a veritable treasure trove for criminals: social security numbers, credit and debit card numbers, financial accounts, Department of Motor Vehicles records, and general usernames and passwords. Fraud rings will use this information to open fake accounts or compromise existing ones.

What’s more, e-commerce has driven the volume of card-not-present transactions, creating additional security concerns with hacking, skimming and phishing. Last year, Javelin reported CNP fraud is 81% more likely to occur than card-present fraud.

Fighting an evolving threat

Extra security measures such as tokenization, biometrics or multifactor authentication are helpful in the fight against fraud, but not enough on their own. It's also possible to have too many barriers in place – because while they may be keeping fraud out, they could also be keeping members out too. Having a payment declined is embarrassing, but multiple unnecessary authentication steps creates friction, both of which can erode a member's satisfaction levels.

The best way for credit unions to deliver a more secure experience to their members is by leveraging artificial intelligence and machine learning to measure account interactions in real time with adaptive behavioral analytics to detect and prevent the evolving fraud threat. Being equipped with more intelligent data around their members’ behavior allows credit unions to automatically identify anomalies and suspicious behavior.

In addition to fraud detection, machine learning with adaptive behavioral analytics ensures fraud analysts are spending time investigating actual cases instead of wasting time on false positives. Ultimately, this delivers a more personalized experience to credit union members, without the downside of added friction.

Further, communicating fraud-prevention efforts to members, such as with text messages asking them to verify potentially suspicious activities, demonstrates your credit union is proactive and prioritizes member safety and satisfaction. Even a reminder to update an account password is valuable, as consumers commonly use a single password across multiple accounts. Member apathy makes a credit union's job of securing accounts much harder. Last year, one fraud survey showed 20% of consumers use the same password for their online bank accounts as they do for other online accounts, such as social media, email and app logins, putting paychecks, emergency funds and life savings at risk.

James Clapper, a retired lieutenant general in the U.S. Air Force and former director of National Intelligence, called cybercriminals "the most pervasive cyber threat to the U.S. financial sector.” These criminals will perpetuate fraud in any way possible and to outsmart the risk it poses, credit unions should collectively address fraud prevention and member satisfaction with the most advanced machine learning capabilities blended with adaptive behavioral analytics.

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