Fraud solution providers need to be prepared to competitively position and message against the new Amazon Fraud Detector risk modeling services. Here’s what risk vendors and sales professionals need to know about Amazon’s new risk management service offering and how to prepare for prospects or current clients considering this new service.
One of the most common reasons organizations fail to realize significant improvements in risk management after implementing custom modeling solutions can be described with one phrase: Junk in. Junk out. This article discusses best practices as it relates to data management and other factors that are shown to improve performance when it comes to custom modeling and machine learning or artificial intelligence.
It’s not just breadth of data, but also quality of data, that is important. One of the biggest misconceptions about machine learning (ML) and artificial intelligence (AI) is that you can just flip a switch and let the technology work its magic.
Fraudulent insurance claims are responsible for more than $80 billion in losses per year in the United States, leading to higher premiums for all policy holders. It is estimated that card issuers will lose $1.3 billion this year from cards issued to synthetic identities. Total fraud losses from synthetic identities receiving loans and credit cards are estimated at $6 billion annually. Lenders, issuers and insurance carriers are increasingly combating fraudulent applications and claims with custom modeling solutions and AI.
Checking pay stubs to verify income is no longer sufficient. Completing an application with their authentic identity, a consumer can easily fabricate paycheck stubs or purchase them online for less than ten dollars. When fraudsters create synthetic identities to take out an uncollateralized loan with no intention of paying it back, they can just easily create or buy fake pay stubs to corroborate their synthesized existence.
While there is value in leveraging Artificial Intelligence for modeling and analytics to detect cyber threats and fraud, security professionals are still more likely to indicate that a human touch is more valuable. A recent survey found that half of organizations are making use AI or machine learning but 60 percent put more trust in findings verified by humans. Meanwhile, changing consumer patterns and the rush to work from home in response to the pandemic has likely led to higher rates of false positives.
This isn’t to say AI and ML are not important – they are. In the same survey, 65% said these tools allow them to focus more on preventing cyber attacks than before and 40% reported feeling less stress.
PayPal completes their fifth acquisition in the past 12 months, this time purchasing machine learning fraud prevention provider Simility for $120 million. PayPal COO Bill Ready says each of these five recent acquisitions are part of the company’s effort to strengthen the services they provide to merchants.
Simility was founded in 2014, they are based in Palo Alto and provide advanced risk analytic and modeling solutions for fraud prevention in the Customer Not Present (CNP) channel. Major clients include eBay/StubHub, Dick’s Sporting Goods and OfferUp. The fraud prevention provider had previously raised $25 million, including PayPal as one of their early investors.
How well would your fraud management program perform if your organization depended on it for doing business with customers from countries like Ukraine and Nigeria? Now what if we said your business was facilitating money remittances to these countries? WireCash is doing just that, and we sought to uncover how this could be possible by talking directly with the company that found success at the intersection of one of the highest risk industries and some of the highest risk countries in terms of online fraud.
The use of custom modeling and risk analytics also allows WireCash to be more consumer-friendly than they were in their ATMCash days, and compared to most online MSBs. While WireCash may sometimes require strong authentication or verification techniques, they try to eliminate the need for this and reduce friction for most new customers through use of risk modeling and amassing many neutral or low risk signals. They have also leveraged this experience and insight to enter new markets where others wouldn’t go or would only tread lightly. WireCash today helps consumers send money to Nigeria, Ukraine, Russia, Armenia and other countries that present high risk. Money Service Businesses providing remittances to these countries have tended to offer limited services or maintain several day waiting periods.