Five Trends for Modern Fraud Strategies
Date: Sunday , November 06, 2016
Headquartered in California, Experian proffers information, analytical tools and marketing services to businesses to help them in managing risk and reward for commercial and financial decisions.
The fraud landscape is always changing at a faster pace than ever before. But with today\'s sophisticated solutions in hand you can grab a hold of market opportunities and maintain your competitive edge. It\'s a tall order, when you add business-as-usual efforts to the mix like growing revenue and protecting customers. But it\'s achievable and it starts by keeping up with the latest trends in fraud mitigation - knowing what\'s out there and how to bring them into your business.
Here are five trends to assess and take action-on to mitigate fraud:
1. Applying right-sized fraud solutions to reduce unnecessary customer disruption:
Have you ever been in the middle of completing an online transaction when you were unexpectedly challenged or prevented from moving forward? You aren\'t the only one. The ratio of disrupted legitimate traffic to actual fraud attempts is now as high as 30:1. The reason this happens is because so many businesses are still using a one-size-fits-all approach to fraud detection and it creates more customer friction than necessary.
Instead, right-sized fraud solutions that reflect the value and level of confidence needed for each transaction will help provide your customers with a hassle-free experience without sacrificing protection reducing that 30:1 ratio. Right-sizing your fraud solutions means aligning true fraud rates with your commercial strategy. You\'ll catch more fraud without disrupting business.
2.Having a universal view of the consumer is the core of modern fraud mitigation and marketing:
Achieving the highest level of protection without adversely affecting the customer experience is a constant balancing act, isn\'t it? A multi-layered approach to authentication is considered the gold standard for identifying legitimate customers, but this can be hard to do without creating more challenges to good customers. Relying on the traditional 360 degree view of a consumer was once a sufficient way to identify a customer, but it\'s not enough anymore.
Today you need a universal profile of consumer behavior. This requires access to a combination of identity data, device intelligence, online behavior, biometrics, historical transactions and more - for consumer interactions not only with you, but across other businesses and industries as well. Companies that translate this knowledge and use it to identify consumers can distinguish a fraudster from a real customer more easily, building trust along the way.
3.Expanding your view through a blended ecosystem:
You can maintain the status quo by continuing to rely on your own first-party data sources. Real progress in the fight against fraud happens when you work outside your business and even your industry. It\'s us against many fraudsters have access to more data than ever before, including data traditionally used to verify identities, and they use that data to create an entire digital profile. Therefore, you can no longer get to the digital interaction data you need by managing the process in a siloed manner. Achieving an expansive view of the universal consumer requires multiple data sources working together, made possible when participating in a blended ecosystem. When a business has a single customer view the customer experience is enhanced, and this supports business growth without sacrificing protection.
I suggest taking the blended ecosystem concept a step further, pushing for greater collaboration across teams and processes that are highly susceptible to fraud, such as account opening, account access and transaction. Traditionally, internal teams have worked independently of one another, often using different fraud solutions and risk mitigation philosophies. The way to get from where we\'ve been to where we need to go is by putting the customer at the heart of your business, where a holistic versus siloed approach means you detect fraud earlier (at account opening), reducing vulnerabilities and financial loss later (at the point of transaction).
4.Achieving agility & scale using service-based models:
To keep up with the speed of fraud, more and more companies are choosing subscription-based systems rather than building in-house or implementing on-premise solutions. Fraud adapts quickly, so when a business is slow to respond it can hurt both the businesses and the customer. Continuous upgrades and the access to new risk logic that come with subscription models provide more agility and faster response to emerging threats, no matter how fast your volume grows or what products, channels or geographies you pursue.
5.Future-proofing fraud solution choices:
Companies need access to a wide variety of traditional and emerging technologies and information sources to fill in knowledge gaps and blind spots where fraudsters try to hide. The ability to modify strategies quickly and catch fraud faster while improving the customer experience is a critical aspect of fraud prevention moving forward.
This last point is more of an emerging trend and one that businesses should really pay attention to - and that is machine learning. It is a powerful predictor of fraud, but it is not the silver bullet.
Machine learning is an invaluable tool as it helps companies move from reactive to predictive by highlighting attributes or relationships that are indicators of fraud. It can analyze more data than a human can, but you should be aware of the limitations. Machine learning can take a long time to react and prevent fraud, it can fail to generate effective patterns or consistent profiles, and it can produce a lot of false positives.
Machine learning can be improved when it is paired with unsupervised machine learning techniques that look for uncharacteristic items, known as anomaly detection. These models can be a strong complement to supervised learning approaches because they go at the same problem from entirely different angles and exploit orthogonal information. The resultant analytics engine can recognize previous patterns of confirmed fraud and raise an alert if a pattern changes.
Fraudsters are relentless and constantly evolving and circumventing fraud detection systems. Staying current helps mitigate fraud loss, enrich customer relationships and drive growth.