[Note: All statistics cited throughout this piece are based on data from Manage the Investment: Maximizing Budgets for Fraud Prevention, a report conducted by Javelin Strategy & Research, sponsored by Galileo Financial Technologies.]

Identity fraud losses surged 19% in 2024, reaching $27.2 billion, according to a new report from Javelin Strategy & Research. Yet nearly half of financial institutions allocated less than $50,000 to fraud prevention solutions, the study found. This budget-to-threat mismatch demands a strategic rethink. Organizations can no longer afford to piece together disconnected tools or delay critical investments. Success requires understanding which capabilities deliver the greatest impact, how purchasing decisions actually get made, and what comprehensive fraud detection looks like in practice.

Key Takeaways

• Fraud losses increased 19% from 2023 to 2024, with account takeover jumping from $12.7 billion to nearly $16 billion

• Budget constraints are severe: 45% of organizations allocated under $50,000 for fraud, identity verification, and authentication in 2023

• Critical tools remain underutilized: Only 32% own authorized push payment fraud solutions, 30% have real-time payment fraud tools, and just 25% use decision engines

• Purchase drivers center on trust: 84% need full product demonstrations, 83% weigh vendor reputation heavily, and 82% demand knowledgeable partners

The Fraud Problem Keeps Growing

Account takeover (ATO) has become the dominant fraud threat facing financial institutions. Five years ago, ATO cost the industry $6 billion annually and affected 4 million consumers. In 2024, those numbers jumped to nearly $16 billion in losses affecting 5 million consumers.

But ATO isn't the only concern. All major fraud categories tracked by Javelin Strategy & Research increased from 2023 to 2024:

• Account takeover: $12.7B to $15.6B

• Existing card fraud: $9.9B to $11.6B

• Existing non-card fraud: $7.9B to $9.3B

• New-account fraud: $5.2B to $6.2B

Organizations Know Where the Threats Are

Credit card fraud topped organizational concerns in 2023, with 35% of businesses focused on resolving this issue. Debit card fraud followed at 30%, then new-account fraud (26%), account takeover (18%), and synthetic identity fraud (16%).

Balancing Fraud Prevention and Customer Experience

The disconnect? While organizations identify the right problem areas, they're not prioritizing solutions that match the threat landscape. Account takeover dominates fraud losses but ranks only fourth in organizational focus.

Modest Budgets Demand Strategic Thinking

Financial institutions face an uncomfortable reality: fraud budgets haven't kept pace with fraud growth. In 2023, 45% of organizations allocated less than $50,000 toward fraud, authentication, and identity verification solutions. Another 17% allocated between $50,000 and $99,999.

Among these modest budgets, allocation splits fairly evenly:

• 38% toward fraud prevention tools

• 31% toward identity verification solutions

• 31% toward authentication solutions

The Tool Ownership Gap

Low ownership rates for critical fraud solutions reveal a significant vulnerability. In 2023:

• Authorized push payment fraud solutions: 32% ownership, only 18% planning to purchase

• Real-time payment fraud solutions: 30% ownership, 20% planning to purchase

• Synthetic ID fraud tools: 29% ownership

• Chargeback fraud solutions: 27% ownership

• Decision engine solutions: 25% ownership

• P2P fraud tools: 25% ownership

• Geolocation services: 22% ownership

The gap is particularly striking for decision engines. Three-quarters of organizations aren't using decision engine tools despite the growing sophistication of fraud attacks. Without decision engines analyzing threats and building user risk profiles in real time, organizations must create static rules manually—wasting precious time on regular updates and manual transaction reviews.

Building Comprehensive Fraud Detection

The sophistication of modern fraud demands equally sophisticated defenses. Static solutions, outdated proprietary code, and rules accessing only internal data won't cut it anymore. Superior fraud detection operates on multiple levels, with technologies working together to build comprehensive user profiles and real-time risk assessments.

Risk-Based Decisioning

Decision engines represent the foundation of modern fraud prevention. They analyze data collected on users—both historically and during transactions—to automate decision-making in real time. Unlike static rules, decision engines work dynamically with data, managing complex processes that incorporate multiple data sources beyond just internal systems.

This automated approach gives financial institutions confidence in transaction decisions while reducing time spent on manual reviews.

AI and Machine Learning

Decision engines gain agility when powered by artificial intelligence. Machine learning handles the heavy lifting in trend and data analysis, maintaining relevant and updated risk management processes.

But machine learning only works with what it's given. Organizations relying solely on internal data severely limit their view into consumer risk levels.

Data Sharing and Consortium Intelligence

Secure data consortiums reveal critical fraud intelligence that single organizations can't see alone. Shared industry data enables better identification of suspicious behaviors. Organizations knowing only their own activity miss patterns of anomalous behavior indicating fraud.

Transaction consortium data further verifies identity and typical user actions, enabling better-informed decisions. This level of industry collaboration isn't optional in today's fraud landscape—it's essential.

Configurable Rules

Rules-based decisioning is static on its own, but rules retain value when optimized by AI and machine learning. Configurable rules become agile assets, readily adapting to real-time scenarios.

Pairing risk-based decisioning with configurable, dynamic rules allows institutions to establish risk tolerance for specific scenarios. This removes ambiguity from decision-making and builds confidence in fraud solution performance.

Historical Consumer Account Data

Account age, balance information, profile changes (address, phone number, email updates), and authorized user changes form key building blocks for understanding who's on the other end of a transaction.

But account history alone isn't enough. Criminals gaining unauthorized access don't need to spoof account age or history—they simply take over accounts and operate within existing parameters.

Behavioral Biometrics

Behavioral biometrics change the game by bolstering traditional consumer data signals in ways much harder for criminals to imitate. Device orientation and movement, keystroke patterns, and mouse dynamics represent just the beginning of what behavioral biometrics detect.

The rise of unauthorized fraudulent activity proves criminals are adept at impersonating consumers. Behavioral biometrics add a layer that's uniquely difficult to fake.

Consumer Trust in AI-Powered Protection

As financial institutions incorporate more AI into fraud mitigation, identity verification, and authentication, consumer understanding becomes critical. The research reveals encouraging trends:

While just under half (47%) of consumers feel generally knowledgeable about AI and how it works, 81% of those consumers are comfortable with their financial institution using AI for account security and fraud protection.

This matters. Financial institutions rank among the most trusted entities by consumers. Maintaining that trust requires ensuring consumers understand protective measures. As organizations adopt AI-powered solutions, demonstrating successful fraud mitigation will prove paramount in gaining consumer willingness to adopt more secure authentication methods.

Strategic Investment Recommendations

Organizations facing budget constraints can still make strategic fraud prevention progress by following several key principles:

Seek solutions that efficiently solve multiple issues. Be wary of tools claiming to resolve everything but don't discount platforms that effectively layer complementary technologies addressing relevant industry challenges.

Know exactly what to solve. Organizations should narrow vendor options quickly by identifying specific issues. Vendors should tailor demonstrations to highlight features addressing each prospect's particular needs.

Employ risk-based decision engines. These tools manage complex processes incorporating multiple data sources across more than just internal systems. They automate decision-making dynamically, building confidence in final actions.

Source AI-powered solutions. Machine learning handles heavy lifting in trend and data analysis, maintaining relevant and updated risk management. Solutions like decision engines gain agility and efficiency when partnered with artificial intelligence.

Invest in solutions rich in data. This includes transaction consortium data that reveals patterns invisible to individual organizations. Shared industry data leads to better identification of suspicious behaviors.

Build comprehensive user profiles. Historical transaction data, consumer account data, behavioral biometrics, rules, and risk engines each provide value. Combined, these tools offer peace of mind that organizations are effectively managing risk and preventing fraudulent activity.

The Path Forward

The financial services industry stands at a crossroads. Fraud losses continue rising while budgets remain constrained. Yet this challenge creates opportunity for organizations willing to think strategically about fraud prevention investments.

Success doesn't require massive budget increases—though organizations able to invest more should do so immediately. Success requires understanding which capabilities deliver the greatest impact, choosing vendors who truly understand your challenges, and implementing comprehensive solutions rather than disconnected point tools.

Organizations that view fraud prevention as infrastructure rather than cost center position themselves for sustainable success. The right investments today—in decision engines, AI-powered analytics, consortium data, and behavioral biometrics—pay dividends through reduced losses, improved customer trust, and operational efficiency.

Want deeper insights into fraud prevention strategies and budget optimization? Download the full Javelin Strategy & Research report to access detailed survey data, comprehensive analysis of purchasing drivers, and specific recommendations for maximizing your fraud prevention investments.

Frequently Asked Questions

How much should financial institutions budget for fraud prevention?

While 45% of organizations allocated less than $50,000 in 2023, this modest spending level doesn't match the threat landscape. Identity fraud losses exceeded $27 billion in 2024, representing a 19% increase. Organizations should assess their fraud exposure, transaction volumes, and customer base to determine appropriate investment levels. More important than total budget is strategic allocation—choosing comprehensive solutions over disconnected point tools delivers better results per dollar spent.

What's the difference between rules-based and risk-based decisioning?

Rules-based decisioning applies predetermined criteria (like declining transactions over certain amounts from specific locations) in a static way. These rules require manual updates and can't adapt to new fraud patterns without human intervention. Risk-based decisioning uses decision engines that dynamically analyze multiple data sources, build real-time risk profiles, and automate decisions. When powered by AI and machine learning, risk-based decisioning adapts to evolving threats without constant manual rule updates.

Why is account takeover fraud increasing so dramatically?

Account takeover jumped from $12.7 billion in 2023 to nearly $16 billion in 2024 for several reasons. Criminals target diverse accounts (checking, credit, email, digital wallets, mobile phones, social media) and lax authentication standards like optional multifactor authentication or permissive password policies have enabled this growth. Cybercriminals successfully evade detection by both consumers and account proprietors. The wide attack surface and weak authentication make ATO particularly lucrative for fraudsters.

What are the most underutilized fraud prevention tools?

Decision engines represent the biggest gap—only 25% of organizations employ them despite their critical role in modern fraud prevention. Other underutilized tools include authorized push payment fraud solutions (32% ownership), real-time payment fraud solutions (30%), synthetic ID fraud tools (29%), and behavioral biometrics. These tools address growing threat areas but haven't achieved broad adoption, creating vulnerabilities for organizations without them.

How does data sharing improve fraud detection?

Organizations knowing only about activity on their own premises are severely limited in detecting patterns indicating fraud. Shared industry data through secure consortiums reveals behaviors invisible to individual institutions. For example, if a fraudster hits multiple financial institutions with similar tactics, consortium data identifies the pattern while individual institutions might miss it. This collaborative approach verifies identities and typical user actions more effectively than isolated data analysis.

What role does AI play in fraud prevention?

AI and machine learning automate trend and data analysis, maintaining relevant and updated risk management processes. Machine learning powers decision engines to identify fraud patterns in real time, adapt to new attack vectors without manual rule updates, and build comprehensive user risk profiles. AI excels at analyzing hundreds of signals simultaneously—transaction patterns, device fingerprints, behavioral biometrics, velocity checks—to score risk more accurately than static rules. The technology handles heavy lifting that would be impossible for fraud analysts to perform manually at scale.

How can small financial institutions compete in fraud prevention with limited budgets?

Strategic selection matters more than budget size. Rather than buying multiple point solutions, smaller institutions should seek comprehensive platforms that layer complementary technologies addressing their specific challenges. Prioritize solutions offering decision engines with AI capabilities, access to consortium data, and behavioral biometrics—these provide outsized impact. Consider managed services where vendors handle operations, reducing the need for large internal fraud teams. Partner with processors or core banking providers offering fraud prevention as integrated services rather than building entirely custom solutions.

Related Resources:

• Payment Risk Platform

• Fraud Operations Services

• Cyberbank Core Platform

Galileo Financial Technologies, LLC is a technology company, not a bank. Galileo partners with many issuing banks to provide banking services in North and Latin America.