Mobile Wallet Fraud Mitigation
- Date:March 30, 2016
- Author(s):
- Test
- Kyle Marchini
- Report Details: 38 pages, 9 graphics
- Research Topic(s):
- Fraud Management
- Fraud & Security
- PAID CONTENT
Overview
When early cases of Apple Pay fraud garnered media attention, FIs and issuers were forced to confront the menacing specter of a new financial product they did not completely control: mobile wallets. Not only did they face fresh competition from new entrants to the payment ecosystem, they faced the more immediate threat of damage to consumers’ perceptions about whether FIs and issuers could prevent fraud with this new payment method. In the time since those high-profile Apple Pay fraud cases, technology companies such as Google and Samsung have introduced similar mobile wallets, merchants have launched CurrentC, and Capital One and Chase have released distinct offerings. As the availability and use of mobile wallets proliferates, they are likely to garner increased attention from fraudsters. And while a clear path for avoiding major fraud pitfalls when navigating the mobile wallet landscape has yet to reveal itself, new approaches and technologies hold the promise that the destination is right around the corner — and none too soon.
Key questions discussed in this report:
- What were some of the early challenges of managing mobile wallet fraud risk?
- How does the ability to prevent mobile wallet fraud affect adoption?
- What factors will influence the rate of mobile wallet fraud in the future?
- Which available technology solutions or analytical approaches hold the most promise for preventing mobile wallet fraud?
Companies Mentioned: Apple, Capital One, CA Technologies, Chase, Early Warning, Experian, Gemalto, GIACT, Google, IDology, Jumio, Keypasco, myPINpad, Nexmo, Neustar, RSA, Samsung, VASCO, Verifi, Zumigo
Methodology
Consumer Data
The consumer data in this report are based on information collected from two Javelin surveys that targeted populations representative of the overall U.S. population in proportions of gender, age, and income:
- A random-sample panel of 3,195 consumers with mobile phones or smartphones in June/July 2015. The overall margin of sampling error is +1.73 percentage points at the 95% confidence level
- The 2015 ID Fraud survey was conducted among 5,111 U.S. adults over age 18 on KnowledgePanel; this sample is representative of the U.S. census demographics distribution, recruited from the Knowledge Networks panel. Data collection took place from October 15 to November 2, 2015. Final data was weighted by Knowledge Networks, while Javelin was responsible for data cleaning, processing, and reporting. Data is weighted using 18+ U.S. Population Benchmarks on age, gender, race/ethnicity, education, census region, and metropolitan status from the most current CPS targets.
Learn More About This Report & Javelin
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