Every organization that conducts transactions online battles fraud. However, not every organization faces the same types of fraud nor do they approach fraud prevention in the same way. Some businesses present a higher value target for fraudsters, so they deal with more risks and get hit by a wider range of fraud attacks and methods of abuse. Also, companies have different risk appetites, product offerings, operational structures, locations, and sales strategies—and these things may shift over time. To effectively prevent fraud and abuse, businesses require a tailored solution that supports their organization as it operates today, and as it evolves, no matter the nature or pace of change.
Many organizations use supervised machine learning (SML) to battle fraud, where the models are based on historical business data and known instances of fraud. Because SML relies on historical data, model drift is a significant challenge when using this type of machine learning.
The problem with a legacy approach.
In machine learning, data can change, whether its business data or fraud data. If you’re feeding models historical business and fraud data, and you’re not continually fine-tuning each model, and those models will likely drift in the future.
Patterns of behavior that were highly predictive in the past may become less predictive during times of change. For example, in March 2020, consumer behavior changed abruptly as the COVID-19 pandemic led to the shutdown of many brick and mortar businesses. Consumers suddenly had to buy most of the things they needed online. Consumer behaviors changed dramatically from where and how often they shopped, to taking advantage of merchant omnichannel offerings like buying online pick up in-store (BOPIS) or curbside. And fraudsters likewise changed their tactics. Many began targeting merchants offering BOPIS and launching repeated attacks. In 2020, BOPIS fraud attacks increased 55%.
Legacy fraud systems can’t predict this kind of abrupt, unprecedented change—not at scale, nor without retraining the models on new data, and fast. Many merchants saw a massive spike in false declines at the start of COVID, their fraud models basing decisions on historical consumer behavior patterns. False declines hurt your business—40% of customers who are declined falsely will purchase from competitors instead.
Until your models are updated and retrained on new business and fraud data, the results will be influenced by outdated information. Without ongoing customization, fraud analytics, and fine-tuning, your models will drift, becoming less and less accurate over time.
You need tailored fraud models.
Businesses evolve—your organization may eventually expand to new regions and markets, sell new products, expand existing product categories, or change revenue models. Fraudsters continually evolve and grow more sophisticated, finding new and clever ways to attack vulnerabilities in your business.
You need a fraud prevention partner who can help you tailor your models on a continual basis. Your organization requires that every model is built with the unique data generated from your business and continuously adjusted to accommodate changing consumer behaviors, business expectations, and the dynamic nature of fraud. A fraud prevention partner that pulls from a robust global network of data can effectively glean insights from across the e-commerce market to proactively protect your business from evolving fraud and abuse trends.
By meeting with your fraud prevention partner regularly and using feedback mechanisms, your business can ensure your fraud models are always current with changing business conditions and real-world fraud trends.
The benefits of tailored fraud models include:
- Dynamic, real-time risk and trust decisions.
- Protection against policy abuse.
- Ability to offer unique customer experiences and increased personalization.
- Accurately identify the right balance between your acceptance rate and risk exposure.
Continuously tailored models give your business the power to grow, without the fear of risk, fraud, or abuse standing in your way.
Interested in learning more about tailored fraud models? Download our white paper “Cyber Crime is Costing the World $6 Trillion Annually. Is Your Fraud Prevention Ready?”