Fraud Detection: The In-Depth Guide for 2022

Fraud is stealthily stealing billions annually across the globe. As digital finance grows, fraud is growing right alongside it. Advances in artificial intelligence are critical for fighting back against constantly adapting fraudsters.

In this comprehensive guide, we’ll provide an in-depth look at fraud detection in 2022. You’ll learn how cutting-edge analytics and AI are used to sniff out criminals, along with real-world examples across different industries. Let’s dive in!

The Rising Threat of Fraud in the Digital Age

Fraud is expanding at an alarming rate:

• Global losses to ecommerce fraud hit $20 billion in 2021, up 18% year-over-year source
• By 2026, the fraud detection and prevention market is projected to reach $141 billion, up from just $22 billion in 2019 source

Several factors are fueling this fraud surge:

• Digital transactions – More business online expands targets
• Sophisticated fraudsters – Criminals exploit new technologies
• Faster payments – Real-time settlement limits reaction time
• Account proliferation – More accounts spread across apps
• Global commerce – Cross-border fraud harder to combat

"Fraudsters are incredibly adaptable and the pandemic accelerated their migration to digital channels. The only way to keep up is with AI-powered detection that stays ahead of constantly evolving scams."

• John Buzzard, Lead Fraud Data Scientist at Acme Bank

Without advanced fraud defense, these losses will continue multiplying. Let‘s look at how modern systems detect the many flavors of fraud.

Fraud Detection: How It Works

Fraud detection leverages big data pipelines and machine learning algorithms to analyze transactions and user behavior. There are several key steps:

1. Data Collection

The fraud analysis process starts by aggregating data from all possible sources, including:

• Transaction details like timestamps, amounts, merchant info
• Device fingerprints like OS, IP address, browser specs
• Customer identity traits and application history
• External threat intelligence feeds

More data leads to more robust fraud models. Pulling in contextual data from across channels provides a 360-degree customer view.

2. Data Classification

Next, the incoming data flows into machine learning models that classify transactions as fraudulent or legitimate.

Supervised learning techniques train models by "showing" them labeled examples of known frauds and non-frauds. The algorithms learn which patterns are associated with each class.

Unsupervised learning instead looks for anomalies and outliers that deviate from normal activity clusters. This helps detect entirely new fraud tactics with no historic labels.

Specific inputs like device type, location, transaction amount, merchant category, etc. all contribute signals to assess risk likelihood for each event.

3. Decision & Feedback

The model scores each transaction and renders a verdict of approve, reject or review. Approved transactions go through without friction, while rejected ones are blocked.

Reviewed transactions are flagged for human analysts to give a final decision. Their feedback further improves the accuracy of the algorithms in an ongoing tuning loop.

Fraud detection pipelines combine historical data, machine learning models, human review, and continuous improvement. Image source: McKinsey

This collaborative approach combines AI speed and scale with human insight and context. The next section explores different fraud flavors through real-world stories.

Fraud Types in Action

While techniques vary, most fraud boils down to criminals deceiving businesses for illicit gain. Some common scenarios across sectors include:

Account Takeover Fraud

Account takeover (ATO) is when hackers gain access to existing user accounts. Tactics range from phishing links to credential stuffing botnets. Simon McNeal, an avid online shopper, recently experienced ATO firsthand:

“I started getting shipping confirmations from my favorite clothing site for items I never ordered. Turns out hackers broke into my account and changed the delivery address and size preferences. Thankfully the fraud team noticed the unusual activity and locked down the account before more damage occurred."

Effective detection of account takeovers involves recognizing sudden shifts from a user‘s normal behavior patterns. Analyzing changes in areas like new devices, different locations, and unusual purchase categories provides signals to identify ATO attacks.

Transaction Fraud in Banking

Transaction fraud remains prevalent across banking channels. Just last month, fraudsters stole $1500 from Nina Chen‘s checking account via an ATM withdrawal across the country.

“The bank’s fraud monitoring system caught the suspicious withdrawal request in real-time based on the international location and much larger amount than my normal ATM activity. They confirmed with me over the phone that it was fraudulent before approving the transaction."

Issuer banks carry the liability for most unauthorized debit and credit card transactions. Real-time transaction monitoring and risk scoring algorithms help minimize losses.

B2B Invoice Fraud

Invoice fraud happens when bad actors submit fake billing documents for goods/services not provided. This happened recently at Zeta Products, according to AP manager Jenny Cho:

“We received an invoice from a supplier we’ve worked with for years, but something seemed off. The items and quantities didn’t match typical purchase patterns. Our accounts payable software flagged this deviation from historical invoices and recommended rejecting it. We confirmed it was fraud, avoiding a $20,000 loss.”

By analyzing historical invoice patterns, AI can spot anomalies that signal B2B scam attempts like Zeta encountered.

Fraud Detection in Key Industries

While techniques are similar, fraud manifests in industry-specific ways. Some common examples include:

Banking Fraud Patterns

• Fake or stolen payment cards used online and in-person
• Card skimmers installed on ATMs and gas pumps
• Wire transfer deception targeting business accounts
• Synthetic identity fraud to open accounts with fabricated credentials
• Credit card bust out scams maxing out newly opened accounts

Banks deploy multilayered fraud defense from device fingerprinting to behavior analytics to thwart the latest schemes.

Retail & Ecommerce Fraud Trends

• Friendly fraud – falsely claiming items are damaged or not delivered
• Promo abuse – exploiting coupons and trials to improperly stack discounts
• Fake reviews – paying for or generating false reviews to mislead buyers
• Triangulation – buying desired items with stolen cards then reselling them
• Inventory stripping – fraud rings that coordinate to steal high-value products

Retailers integrate fraud tools directly into the shopping journey to catch scams and abuse.

Marketplace Fraud Tactics

• Fake escrow services to guarantee safe transactions, but which disappear after payment
• Selling discounted stolen gift cards purchased with other stolen cards
• Listing counterfeit or nonexistent goods
• Selling goods already sold/shipped to other buyers
• Fake bids and shill bidding to artificially inflate prices

Robust identity verification and activity monitoring helps marketplaces like eBay minimize fraud.

Common fraud scenarios across different industries

This overview demonstrates the diversity of fraud that business face today. Next we‘ll explore how artificial intelligence combats these evolving threats.

AI is Revolutionizing Fraud Prevention

Fraud techniques are constantly changing, so fixed rules and thresholds are ineffective. The AI techniques powering modern fraud platforms include:

Adaptive Analytics

AI fraud solutions analyze millions of transactions to learn normal vs suspicious patterns. Models automatically adjust to detect new anomalies without manual re-tuning.

Deep Learning

Deep neural networks uncover complex relationships across massive feature sets. This provides a nuanced fraud probability assessment for each transaction.

Behavioral Biometrics

Analyzing unique user behavior patterns like typing cadence and swipe speed helps confirm legitimate account owners during high-risk transactions.

Graph Networks

Mapping connection webs between entities reveals suspicious clusters like fraud rings coordinating attacks.

Real-Time Scoring

Instant risk scores on each transaction allow stopping frauds mid-stream vs after the fact. Adding contextual data like merchant profiles improves accuracy.

"With AI, our fraud models continuously learn and adapt to new scenarios as fraudsters shift tactics. The machines even find fraud strategies that humans miss."

• Claire Austin, VP of Fraud at TechCo

Challenges in Fraud Management

While AI delivers huge benefits, fraud prevention still involves tradeoffs and considerations:

• False positives – Declining legitimate transactions harms customer experience
• Data privacy – Collecting and analyzing user data raises ethical questions
• Adaptable fraud – Criminals keep innovating with new technology
• Volume – Billions of transactions require scalable solutions
• Omni-channel – Linking fraud data across channels is difficult
• Global networks – Cross-border fraud is harder to trace

"We‘re combating an intelligent adversary, so fraud systems require ongoing tuning and augmentation. Adding new data sources and detection strategies is key to staying a step ahead."

• Matt Sullivan, Fraud Ops Director at FinServ Corp

Balancing these factors is crucial to maximizing fraud prevention while minimizing side effects.

Fraud Detection Vendors

Many vendors provide advanced AI software for fraud management. Some top options include:

Forter – Their identity-based platform focuses on preventing ecommerce fraud in real time with minimal false positives.

Kount – Kount combines AI with digital identity trust and consensus analytics from the Ekata network.

Featurespace – Featurespace is a leading choice for financial fraud prevention including cards, loans, deposit accounts and more.

DataVisor – With unsupervised machine learning models, DataVisor specializes in detecting completely new types of fraud.

Bolt – Bolt equips companies to build customized in-house fraud solutions using a framework leveraging AI and analytics.

View our full guide to the best AI-powered fraud detection vendors.

Fraud Prevention: A Layered Approach

While fraud detection provides the foundation, truly robust protection requires a layered defense with both preventative and reactive components:

Proactive prevention

• Account security – Strong passwords, multi-factor authentication
• Transaction validation – Mobile approvals for high-risk events
• Customer education – Raising awareness of fraud tactics

Detection and response

• Activity monitoring – Identify anomalies from normal patterns
• Risk scoring – Real-time rating of transaction legitimacy
• Alerting – Notify customers of suspected fraud
• Investigations – Reconstruct fraud details to guide future detection
• Recovery – Recoup losses by identifying criminals

"Technology enables the scale and speed required for fraud prevention today. But we can‘t lose sight of the human side, from further authenticating high-risk transactions to educating customers on fraud prevention best practices."

• Jeanette Wells, Global Head of Fraud at Leading Financial

This combination of human and technical measures provides defense in depth against the enemy of fraud.

The Future of Fraud Prevention

Fraud detection will continue advancing in step with the maturing field of artificial intelligence:

• Connected neural networks – Linking specialized deep learning models creates ensemble algorithms with greater accuracy
• Recurrent neural networks – Memory components detect shifts in sequence/time-based patterns like user behavior
• Generative adversarial networks – Networks face off to better identify anomalies
• Graph analytics – Visualizing entity connections provides insights into fraud rings
• Natural language processing – Analyzing unstructured text data including emails, chat logs, etc. improves fraud context

Meanwhile, criminals will continue evolving their tactics with new technology like deepfakes which are already being used for identity fraud. The fraud war is far from over, but AI offers hope to tilt the battlefield back in favor of businesses.

Key Takeaways on Fraud Detection

Key points from this in-depth fraud detection guide include:

• Fraud is a large and growing threat as transactions move online
• Machine learning techniques enable automated fraud analysis at scale
• Myriad fraud types manifest across different sectors
• AI empowers real-time adaptive protection vs evolving fraud tactics
• A layered approach is needed with both human and technical defenses

Ready to implement next-gen fraud defense? Browse our guide to the top fraud detection vendors. Reach out if we can help you find the right solution tailored for your organization‘s needs.