The Stack Digital Magazine: Protect Against Financial Fraud – Vol 1, Issue 2

Even in uncertain times, there’s one thing you can expect: Fraudsters will continue to invent new ways to steal from your business and your customers.

Protecting your business from relentless online thieves is crucial. But every minute you spend guarding against financial fraud, disputing chargebacks, or trying to make sense of new solutions is a minute you didn’t spend focusing on your customers.

The relentless battle against financial fraud

The right mitigation strategy can help grow your business and enhance the customer experience. If the past is prologue, an economic downturn will expand the ranks of online fraudsters. Even if the global economy stays relatively stable over the next several months, online criminals will continue to innovate and devise new ways to steal.

Reducing risk for your business is critical in good times and bad. This issue of The Stack focuses on how the right payments platform can help you minimize the threat of financial cybercrime to your business and your customers.

PayPal has the tools to help make secure transactions simple and speedy. Being able to see both sides of so many transactions means we can detect trends and evolving fraud patterns to then create risk models that can help to protect against fraud.

Statshot: Chargeback risk

High chargeback rates can send payment costs soaring, which can be a problem for your business. Every chargeback impacts your overall chargeback ratio, which can determine your reputation and standing with credit networks. When it comes to chargebacks, the key is to proactively prevent them from happening in the first place, in order to help protect your business. Download Issue 2 for a more in-depth look at how you can help mitigate against and reduce chargeback risk.

Fraudsters learn fast – so can you

Fraudsters are always at work and always adapting. They probe relentlessly for ways to steal from businesses and customers. The minute a new digital commerce channel opens, bad actors get busy on new schemes. Fortunately, businesses have tools to fight back and mitigate risk.

The two main methods of combating fraud are rules-based tactics and machine learning. While rules are still prevalent and powerful, businesses are increasingly leveraging machine learning to detect fraud. In one survey, 83% of respondents said machine learning is pivotal to their company’s ecommerce fraud strategy¹.

To help mitigate risk:

• Consider out-of-the-box solutions to help spot suspicious transactions
• Leverage manual review capabilities for in-house fraud teams
• Implement dispute automation to address 100% of disputes and reduce losses
• Use live risk analysis on direct credit card transactions to reduce losses and liability
• Get real time recommendations with AI and machine learning-powered fraud detection

Pros and Cons: Rules vs. Machine Learning for Fraud Detection

With fraud on the rise, should you use rules or machine learning to protect your business? While rules are still prevalent and powerful, businesses are increasingly leveraging machine learning to detect fraud. Take a look at both and see how they can work in concert to detect fraud and help protect against losses.

Rules pros and cons

Pros

• The human element of rules can make it easy for analysts to spot errors and make a fast fix.
• New rules can be made when analysts detect new fraud patterns emerging.
• Fraud detection using machine learning takes time, and a lot of fraud can occur while machine learning is getting up to speed.

Cons

• Rules can’t be changed, which means new rules are needed to thwart fraudster innovation.
• As rules are added, systems can grow complex and hard to maintain.
• The need to manually monitor fraud trends and create new rules can create a corresponding demand for more people and resources.

Machine learning pros and cons

Pros

• Machine learning creates algorithms to process large datasets with multiple variables to find correlations indicating sophisticated fraud attempts.
• Machine learning captures more fraudulent attempts with fewer false positives.
• Machine learning offers fraud mitigation in real time, detecting patterns as they develop, and can be retrained with fresh data for greater precision.

Cons

• Machine learning models can become “black boxes”, producing results that are hard to understand and explain.
• A lack of transparency makes future modeling more challenging.
• Machine learning can take one to three months to absorb enough data to work effectively.

Read the full issue of The Stack on the relentless battle against fraud

The latest issue of The Stack is all about protecting your business from fraud in the most efficient ways possible. Discover the pros and cons of machine learning, learn how one major online brand automated chargeback disputes, and more. Download the full Issue 2 of The Stack for more in-depth stories and stats about enterprise fraud prevention here.

We hope you enjoy this edition of The Stack. You can access more resources to help make your business more resilient with our business roadmap planning.