The seasonality of fraud: How to anticipate and prevent fraud spikes

Picture this: you’re at your annual Memorial Day barbecue and your phone starts buzzing. Your organization is seeing a 200x spike in applications. For many financial institutions and fintechs, this kind of surge is all too familiar during weekends and holidays. Without the right tools in place, it’s hard to decipher if this surge is for good reasons (like a marketing campaign) or if you’re under a fraud attack. Either way, your BBQ could turn into a crisis response.

Fraud happens year-round, but certain times of year bring predictable spikes in suspicious activity. During high-volume, high-stress periods, fraudsters exploit chaos and consumer urgency to blend in with legitimate behavior.

From natural disasters to tax refund season to the holiday shopping surge, each “fraud season” has its own triggers. Understanding these cycles is critical for financial institutions looking to build proactive, adaptive defenses.

The seasons of fraud

TLDR? Anytime you expect an increase in volume is also a time when you can expect fraudsters to mimic client behavior and try to mask their activity. 

Let’s dive into a few key triggers for surges in fraud volume: 

Holidays and weekends

Holiday spending has shifted significantly over the years. Previously, stores would be closed on Thanksgiving Day. Now “Black Friday” sales start days before, while stores stay open throughout the entire holiday. This trend extends to other holidays, too. As consumerism rises, it seems like every holiday comes with a new flash sale, which in turn drives an influx of transactions. 

Block reported that they processed a record number of daily transactions during the Black Friday/Cyber Monday weekend in 2024. During Black Friday, Cyber Monday, and the holiday season, scams surge and financial institutions face a spike in fraud volumes. Last year, 1 in 3 Americans fell victim to scams during the holiday season, according to McAfee’s 2024 Global Holiday Shopping Scams Study.

Fraudsters take advantage of shoppers looking for deals and the period of high transaction volume with AI-generated fake websites and social engineering scams. At banks, many employees are out of the office. Contact centers are flooded with claims and disputes while teams are already running lean. It’s not uncommon for an analyst to have to step away from a Thanksgiving dinner or holiday gathering to handle a sudden spike in fraudulent activity.

Tax season

Between February and April, identity thieves race to file fraudulent returns before legitimate taxpayers do. The scale is staggering: during the 2025 filing period through the end of February, the IRS flagged more than 1.9 million tax returns for potential identity fraud, representing $16.5 billion in refunds.

Summer travel and PTO

By mid-year, refund money runs out and fraudsters’ attention shifts. Warmer weather, school breaks, and looser work schedules send more people on the move, whether for family vacations, long weekends, or extended PTO. That surge in travel means more card swipes in unfamiliar locations and increased digital payments. 

For fraudsters, this presents an opportunity to exploit distracted consumers and lighter staffing at financial institutions. For fraud teams, legitimate spending patterns may look irregular, while staffing issues could lead to some fraudulent activity slipping through the cracks. 

Major events and crises

Natural disasters, pandemics, or government relief programs open new doors for exploitation, as fraudsters move quickly to impersonate agencies, steal benefits, or reroute funds. 

In one case, one major credit union in Florida faced a surge in account takeover attempts following Hurricane Milton—fraudsters exploiting the chaos in the region. With Alloy, the credit union immediately detected the attack and kept digital channels open for genuine members needing urgent assistance.

Times of economic volatility

When inflation rises, unemployment shifts, or markets fluctuate, fraud risk tends to climb. Economic uncertainty fuels both desperation-driven scams (such as bust-out or loan fraud) and opportunistic attacks by organized groups that exploit weaker controls or distracted consumers. Fraud teams often see higher volumes of first-party fraud, credit abuse, and synthetic identity creation during these turbulent periods.

How financial institutions can prepare for expected surges in fraud

Now you know when to expect fraud spikes. But what can you do to prevent it? Fraud isn’t inevitable, but it is predictable. By preparing for seasonal spikes, teams can protect both customers and operations.

1. Plan for the surge

• Increase on-call coverage during peak fraud windows.
• Cross-train teams to cover critical gaps during holidays or PTO-heavy weeks.
• Outline the line of defense if problems arise.

2. Monitor and adapt in real time

• Watch for sudden increases in declines, claims, and contact center volume.
• Track rule performance, adjust thresholds, and measure false-positive rates.
• Report metrics across leadership teams to increase visibility and alignment.

3. Modernize your fraud defenses

• Traditional, rule-based systems are reactive — they spot known fraud, not new patterns. Leverage machine learning models that learn, link, and spot emerging threats before they’re codified into rules.

Ultimately, the more visibility you have, the better. 

Alloy’s suite of Actionable AI tools makes it easier to respond to fraud spikes

Fraudsters don’t take a day off, but actionable AI ensures your fraud team can. Alloy’s suite of actionable AI tools is designed to empower financial institutions and fintechs with actionable insights to streamline operations, reduce losses, and make faster, more confident decisions. We deliver a suite of integrated AI capabilities that drive operational outcomes (not just informational alerts).

Alloy’s actionable AI suite is made up of four core elements:

At onboarding: Fraud Attack Radar

When a large-scale fraud attack is suspected in your origination funnel, Alloy’s Fraud Attack Radar proactively sends clients an alert and enables clients to take swift action within the platform to triage, investigate, and improve their workflows.

Learn more about Fraud Attack Radar

Post-onboarding: Fraud Signal

Fraud Signal is Alloy’s predictive machine learning model that connects data from onboarding, transactions, and ongoing account activity to deliver full-lifecycle fraud detection. It’s designed to identify risk earlier and help financial institutions and fintechs respond faster. 

Behind the scenes: Generative AI Agents

Alloy’s generative AI and agentic AI tools make human investigators 100x more effective via summarization, enrichment, prep/assistance, and sometimes automated actions. 

Open orchestration: connectivity to all of the best AI models

Various third-party solutions use different techniques and model off unique authoritative datasets. As a result, their scores can often be complementary; for example, one vendor might be good at detecting synthetic identities, another might flag cases of first-party abuse. Alloy enables financial institutions and fintechs to orchestrate intelligence from a variety of third-party solutions (as well as in-house, client-owned proprietary models) in their decisioning logic to find the optimal balance between risk, cost, and customer experience.

Alloy’s actionable AI in action

A fast-growing digital provider boosts operational efficiency, reducing false positives by 82%.

A digital provider’s fraud investigation team was drowning in alerts. 

Every day, analysts sifted through thousands of flagged transactions — and the vast majority turned out to be false positives. 

To fix this, the company implemented Alloy’s Fraud Signal to help distinguish between high- and low-risk customers in real time.

The results were immediate:

• 82% reduction in false positives, achieved by reducing alerts that would have otherwise been dismissed after manual review.
• Previously missed fraud cases were accurately flagged by Fraud Signal, revealing gaps in their existing rule-based systems.
• Freed from the noise, analysts were able to redirect their time toward genuine investigations, improving both speed and morale across the team.

By integrating machine learning into its fraud strategy, the organization transformed its operations — moving from a reactive, overwhelmed model to a proactive, precision-focused one.

Tying it together for year-round fraud protection

Behind every fraud alert are the analysts working late, the customers panicking over unauthorized charges, and the institution protecting trust.

Fraud peaks may be seasonal — but your defense doesn’t have to be. By combining continuous monitoring, actionable AI, and proactive staffing, you can prepare for the inevitable surges and prevent the damage.