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Feeling overmatched by fraud? There’s a smart way to use ML and AI to save time, cost

Fraud undermines revenues for companies in all industries. The Federal Trade Commission estimates that U.S. consumers lost more than $10 billion in 2023 due to investment scams, fraudulent charges, stolen identities, and other schemes. And that doesn’t account for the billions lost to brand reputation, consumer faith, and customer loyalty.
Fraud represents the No. 1 threat to our confidence in the digital economy and yet many don’t realize we now have tools to combat what some call an existential threat.

Are you overmatched by fraudsters?

Most organizations concede they are ill-equipped to fight the growing threat of fraud. To determine whether you can benefit from fraud automation via machine learning and artificial intelligence, look within and ask if you’re experiencing these pain points:

  • Inefficiency: Investigating every suspicious transaction takes excessive amounts of staff time and manual effort.
  • Poor customer experience: Freezing legitimate transactions while waiting for manual reviews frustrates customers and reduces revenue.
  • Data overload: Identifying deception signals in massive volumes of data is nearly impossible to do consistently through manual scrutiny alone.
  • Fast-changing patterns of fraud: By the time teams catch up to new methods, criminals have moved onto something else. Manual processes cannot adapt fast enough.
  • Slow dispute resolution processes: Card network rules around chargeback response deadlines pile on pressure. Manual processes make timely responses extremely difficult.
  • Inability to learn from prior fraud events: Because reviews are one-off, investigators cannot leverage past cases to better predict new incidents. No consistency across analysts either.
  • Legitimate customers blocked: A lack of indicators to qualify the risk levels of reviewed events means high volumes of manual reviews for transactions that are legitimate.

Watch out for common scams across industries

Some of the most prevalent fraud attacks face the retail, hospitality, travel, and gaming industries. There are hundreds of variants depending on the type of business you are, but for now, we’ll concentrate on those that perform some type of commerce online. 
Chargebacks: A chargeback is when a shopper disputes a credit card charge and the money is returned to them. These can happen fraudulently if someone denies making or authorizing a purchase. This causes loss of revenue and significant fees for retailers.
Fake accounts: Scammers create accounts using stolen identities or false information to make fraudulent purchases. This allows them to buy items they don’t pay for or to facilitate money laundering.
Website vulnerabilities: Hacked websites, breaches, or vulnerabilities like SQL injection can give fraudsters access to customer data or allow them to make admin changes for fraudulent transactions.
Refund or return fraud: Criminals make purchases with stolen cards then issue fraudulent refunds back to accounts they control. Retailers lose the sold inventory and payouts.
Identity theft: Personal information obtained on the dark web or via data breaches allows fraudsters to open new accounts or take over existing accounts to make unauthorized purchases.
Promotions abuse:  Special offers, discounts or gift cards are obtained fraudulently and used to purchase products at a lower cost for resale or conversion to cash.
Affiliate or referral fraud: Fraudsters set up fake affiliate accounts or referral links to earn commissions on purchases made with stolen payment credentials or that funnel transactions for laundering purposes.

How to fight fraud with AI and ML

Advanced analytics and predictive machine learning (ML) models are at the center of modern fraud management.
How does it work? To start, machine learning algorithms comb through massive sets of data about transactions, user behavior, and relationships to detect patterns indicative of fraud. These algorithms derive signals correlated to fraud incidents without explicit programming, thus improving fraud detection for your teams.
As a best practice, models must be adapted and retrained to learn of emerging fraud patterns.  Machine learning models continually refine to identify risky attributes and statistical score risk levels associated with users, devices, transactions, locations, and more in real time.

Implementing risk-based fraud assessments

Transaction risk-scoring lies at the heart of most anti-fraud systems. Parameters like email addresses, shipping addresses, order value, order contents, time since last order, login device type, IP geolocation, and hundreds of others are assessed by ML models to generate risk scores. Companies set thresholds for review or rejection based on model output.
User behavior analytics also assess risk levels associated with account activities over longer periods by examining patterns in login attempts, transaction amounts, transaction failure rates, content browsing paths, and more. Outlier activities generate alerts for human review. The highest risk traffic can be automatically blocked until verified.

Ongoing model optimization

The key advantage of machine learning is the ability for continuous, automated self-improvement over time. Consistently integrating new data — including emerging positive fraud cases — into training data improves model performance and detection rates. Data teams run backtesting, simulate fraudulent transactions, and compare performance of model iterations to maximize predictive accuracy.
AI and machine learning enable highly accurate fraud prevention at scale, stopping criminals in their tracks while minimizing disruption for legitimate customers.

Measuring the ROI of your fraud prevention investments

Calculating the potential return on investment (ROI) is crucial to justify spending on fraud prevention and to understand its financial impact. Some ways to estimate the ROI of fraud management investments include:

  • Direct savings: Reduced chargebacks and reversals and less revenue lost to refund fraud and abuse
  • Risk mitigation: Lower fines and fees from payment processors and card networks; avoid costs related to security breaches
  • Operational efficiency: Automation of manual reviews; consolidating tools to reduce licensing costs
  • Increased revenue: Higher authorization rates from improved fraud models; improved customer conversion through frictionless authentication 

Top performing merchants can achieve ROIs as high as 1,000% to 1,500%. Studies of major retailers have found savings between 70% and 80% on fraud costs after investing in AI and machine learning-powered software.
In addition, the lifetime value of legitimate customers who can be identified through smarter fraud screening should also be considered. Estimating the tangible benefit in dollars alongside risk mitigation and operational metrics helps solidify your business case for fraud prevention payback.
Attract good customers, repel bad actors
Modern companies face a complex and ever-evolving fraud landscape that demands sophisticated technological capabilities to identify threats and mitigate risks across various fronts, from payment fraud and identity theft to account takeovers, competitive attacks, and more.
Implementing automated machine learning improves the effectiveness and efficiency for fraud analysts by 10x. We recommend future-proofing and scaling your fraud operations so that your company is known for exceptional trust and safety — and not in the headlines for being a haven for bad actors and scam artists.