Streamlining the AML Investigation Process to Counteract Fraud-as-a-Service Models

In 2025, fraud has become a built-in threat that’s changing how financial institutions handle compliance. At the core is Fraud-as-a-Service (FaaS), a black-market system where cybercriminals bundle and sell fraud tools. These kits, which often include step-by-step guides and basic support, make it possible for even unskilled users to commit advanced financial crimes.

A recent report found that 11% of mid-sized banks and credit unions experienced fraud losses of over $5 million last year, largely due to scalable, tech-driven scams. In response, there's growing pressure to update outdated AML practices and move toward more efficient, AI-supported investigation methods.

This blog examines how AML investigations are changing in response to FaaS. It looks at AI use, workflow updates, the rise of younger cybercriminals, and cross-border threats, outlining where institutions need to adjust their methods with urgency.

What Is Fraud-as-a-Service?

Fraud-as-a-Service is a commercial ecosystem for FinCrime tools. FaaS providers supply ready-made kits, stolen data, automation scripts, and laundering services to paying customers, ranging from opportunistic individuals to organized fraud networks.

This model has lowered the barrier to entry so significantly that even teenagers can launch complicated attacks. As a result, financial institutions are experiencing more frequent and harder-to-predict fraud events, often orchestrated by previously unskilled actors.

Five Common FaaS Techniques Used in 2025

Fraud-as-a-Service (FaaS) has matured into a robust underground industry, offering turnkey solutions that enable cybercriminals of all skill levels to perpetrate complex fraud at scale. Below are the five most prevalent FaaS techniques in 2025, each of which presents unique challenges for anti-money laundering (AML) and fraud detection systems.

Phishing Kits

Phishing kits are bundled tools that mimic banking or customer service portals. They include site templates, message scripts, and stolen contact lists, enabling low-skill attackers to launch phishing campaigns. Priced under $25 on the dark web or Telegram, these kits often feature drag-and-drop builders and automation, making large-scale attacks easy. Their widespread use has driven spikes in data breaches and malware, allowing criminals to steal credentials, financial data, or take over devices. The automation and accessibility of these kits have significantly scaled up phishing operations.

Stolen Credential Marketplaces

Credential theft has surged, turning underground marketplaces into active hubs for stolen usernames, passwords, and identity data. Fueled by breaches and malware, credential theft attempts tripled between 2023 and 2024. Attackers use these credentials in stuffing attacks to breach multiple services. The resale of stolen credentials enables even low-level criminals to access high-value accounts, fueling identity fraud and network intrusions. This commoditization poses escalating threats to financial firms and consumers alike.

Account Takeover Automation

Account takeover (ATO) attacks use bots, scripts, and proxies to hijack user accounts at scale. Credential stuffing tools, fed by stolen data, test login pairs en masse, bypassing basic security. Once in, attackers can drain funds, change settings, or reroute transactions. Sophisticated techniques - like device spoofing, IP rotation, and behavioral mimicry - make detection harder. While security tools monitor fingerprints, IPs, and user behavior, the speed and complexity of attacks often overwhelm defenses.

Synthetic Identity Creation

Synthetic identity fraud combines real and fake personal data to create new, fake identities. FaaS platforms automate this using identity manipulation, compilation, or fabrication. These fake profiles pass KYC checks, open accounts, and build credit before committing fraud. Document forgery makes this easier. Because synthetic identities can go undetected for years, they pose a long-term risk. U.S. losses from this fraud type could exceed $23 billion by 2030.

Money Mule Recruitment Services

Money mule networks help FaaS operators move stolen funds by recruiting people - often via fake job ads or phishing - who unknowingly assist in laundering money. Recruits transfer illicit funds across accounts and borders, keeping a cut. Operators provide scripts, instructions, and onboarding to minimize detection. Known as "layering," this tactic obscures money trails and hinders AML efforts. Mules are key to legitimizing and moving criminal proceeds, complicating investigations.

Why Advanced Fraud Detection Systems Are The Need of The Hour

Federal Trade Commission data for the last year reveals that consumers reported over $12.5 billion in fraud-related losses, marking a 25% increase compared to the previous year. Notably, the overall number of fraud reports remained steady. The percentage of individuals who reported losing money in the last year rose to 38%.

Moreover, consumers reported the highest financial losses to investment scams, totaling $5.7 billion, a 24% increase from the previous year. Imposter scams followed, with reported losses reaching $2.95 billion. That year, more money was lost through scams involving bank transfers or cryptocurrency than through all other payment methods combined.

The Evolving Role of AML Investigation in Tackling Fraud-as-a-Service

The AML investigation function has shifted from a routine regulatory task to a direct line of defense against a growing, tech-driven fraud economy. As Fraud-as-a-Service (FaaS) grows more structured and widely available, financial institutions must transform to faster, insight-driven investigation methods.

The list below outlines eight key ways this transformation is unfolding in 2025.

1. AML Investigation and the Rise of On-Demand Fraud Tools

FaaS has turned cybercrime into a business model, selling fraud kits, account takeover tools, and laundering services to anyone who can pay. These services are often bundled with step-by-step instructions and customer support, enabling even first-time offenders to bypass legacy security systems. 

AML investigation teams are now dealing with cases linked to toolkits hosted on encrypted platforms, sold through Telegram, and advertised on social media. These channels make early detection significantly more difficult.

2. Speed and Accuracy in AML Investigation Processes

Legacy investigation workflows, often reliant on manual checks and siloed systems, are no match for the real-time fraud cycles powered by FaaS. Case resolution delays are not tolerable when fraudsters can scale attacks in hours.

Institutions are replacing spreadsheet-based reviews with centralized case managers, automating the most repetitive steps in the investigation lifecycle. This change enables analysts to triage, assess, and escalate cases faster, before damage is done.

3. Using Real-Time Data to Strengthen AML Investigation

To respond to the changing tactics behind FaaS threats, AML systems need to handle a broader range of data, including transaction speed, device signals, and unusual behavior patterns. 

Updated investigation platforms bring these inputs together in real time and generate risk scores that adjust as new details come in. This enables institutions to flag and pause transactions within seconds to FaaS-related fraud, which can cause major losses in minutes.

4. Detecting Fraud Patterns in AML Investigation at Scale

With FaaS operations mimicking legitimate customer behavior to avoid detection, institutions need AI-driven models capable of recognizing nuanced patterns across customer segments. These models now evaluate how a transaction aligns with a customer’s typical behavior, how similar patterns appear across the network, and what external threats may correlate. 

This level of precision transforms AML investigation from reactionary to preemptive, shifting the focus from what happened to what’s likely to happen next.

5. Data Sharing Models to Improve AML Investigation Coverage

Banks are notoriously protective of their data, but FaaS doesn’t operate within one institution’s boundaries. In response, federated learning models are emerging, allowing multiple institutions to share learning outcomes without sharing raw data. 

This gives AML investigators access to broader fraud insights, helping them identify coordinated campaigns earlier. In 2025, many global banks are participating in shared utilities for pattern recognition, fraud typologies, and mule account detection.

6. Applying Generative AI in AML Investigation Workflows

Generative AI has moved from trial use to a core part of modern AML investigation. Compliance teams use it to summarize cases, draft SAR narratives, and quickly map transaction flows. 

Investigators can review high-priority alerts with a complete risk view that includes context, media checks, and recommended actions. This speeds up decisions and supports consistent, auditable processes, which are essential in regulated settings.

7. Updating AML Investigation Policies for AI Involvement

As AI changes the way investigations are handled, governance frameworks are being revised. Financial institutions are adjusting AML policies to address AI-generated insights, model transparency, and monitoring standards. 

Governance teams are responsible for making sure AI use remains clear, supported by evidence, and fully auditable. This involves routine stress testing, clear escalation procedures, and defined checkpoints for human review, all of which must be documented and ready for regulatory scrutiny.

8. AML Investigation Collaboration Models that Break Institutional Silos

FaaS threats involve multiple layers of risk that no single team can address in isolation. Tackling them effectively requires cooperation across departments such as fraud, cybersecurity, IT, and legal. To support this, organizations are creating shared frameworks that promote continuous information exchange and joint decision-making.

Fraud analysts contribute to AML case reviews, while compliance findings help guide security updates and threat assessments. This structure helps reduce gaps in awareness and improves response times when dealing with high-risk threats.

Structuring AML Investigation Processes for Modern Threats

With fraud becoming more adaptive and AI-driven, AML investigation workflows need to prioritize flexibility and speed. Rigid rules and disconnected systems are no longer effective. Compliance teams are now building investigation setups that can adjust quickly and process information as it comes in. 

The following outlines how financial institutions are structuring their AML structures in 2025 to keep pace with these demands.

1. Structuring AML Investigation Workflows for Flexibility

Effective AML investigation starts with thoughtful process design. Many institutions are now using modular workflows that break down data intake, alert review, investigation, and resolution into separate, linked stages. 

Each part is built to handle updates, whether related to regulations, risk patterns, or new fraud methods. This structure allows investigations to scale and adapt without requiring a full system overhaul each time conditions change.

2. Using Automation to Streamline AML Investigation Tasks

Automation in AML investigation aims to speed up routine tasks without removing human oversight. This includes automating SAR templates, transaction summaries, and negative news checks, which are tasks that often slow down analysts. 

Institutions applying automation in these areas have reported a reduction in investigation time. The purpose is to streamline work so analysts can focus their attention where judgment and experience are most needed.

3. Simplifying SAR Filing in AML Investigation

Suspicious Activity Reports (SARs) are a key point where fraud detection and compliance intersect. In older systems, preparing a SAR can take hours and often results in inconsistencies. In 2025, many institutions are embedding SAR builders directly into their case management tools. 

These platforms pull in verified case data and risk details in a structured format, making it easier for analysts to review and submit reports efficiently while maintaining regulatory standards and report quality.

4. Centralizing Case Management in AML Investigation

Investigation silos create inefficiencies. When fraud prevention, cybersecurity, and compliance teams work separately, important data can be missed and signals overlooked. To address this, institutions are adopting integrated case management systems that bring together alerts from AML, fraud, sanctions, and KYC reviews on a single platform. 

This setup reduces the need to switch between systems, improves coordination across teams, and helps resolve cases more quickly by providing a full view of each situation.

5. Tracking Results with Clear AML Investigation Metrics

A results-driven approach to AML investigation depends on updated benchmarks. Key metrics now include how quickly cases are resolved, how effectively false positives are reduced, and how accurate AI-assisted decisions are during reviews.

Institutions measuring investigator efficiency by hours saved, rather than just the number of SARs filed, are uncovering more useful insights. These indicators help compliance leaders support investment decisions, assess team output, and identify areas where processes need improvement.

The Strategic Future of AML Investigation

Financial crime is changing faster than many traditional responses can keep up. To stay effective, AML investigation needs to operate as a strategic function, designed to detect risks and adjust quickly. Institutions are now focusing on building compliance operations that are more resilient, better connected to technology, and centered on measurable results.

1. Building AML Investigation Systems That Work Across Platforms

Financial institutions often work with disconnected systems. KYC tools, fraud detection engines, and case management platforms are frequently siloed and lack smooth integration. 

In 2025, more institutions are focusing on interoperability, making sure their AML platforms can connect with legacy systems, cloud services, and third-party data sources. This helps reduce manual work and supports a more complete view of fraud activity.

2. Creating Tiered Response Models in AML Investigation

As AML programs scale, they must also mature. Tiered response models are becoming the norm, where low-risk alerts are handled through automation and higher-risk cases receive analyst review.

This segmentation prevents overloading teams with low-priority work while ensuring deeper focus on emerging risks. Institutions adopting this model report improved investigator throughput and more defensible decision-making frameworks.

3. Evolving Analyst Roles in AML Investigation Teams

The role of the analyst in AML investigation is changing. Today’s investigators are expected to work with AI tools, understand predictive models, and make decisions that balance data analysis with regulatory requirements. 

As a result, hiring priorities are changing toward candidates with strong investigative skills, comfort with digital systems, and the ability to adapt quickly. Training programs now routinely cover AI literacy and regulatory updates as standard components.

4. Setting AML Investigation Priorities for 2026

Planning for the year ahead means more than just meeting compliance requirements. Institutions are setting budgets for real-time data infrastructure, building internal AI capabilities, and joining international fraud intelligence networks. 

Treating AML investigation as a strategic function rather than a back-office expense puts financial firms in a good position to respond quickly and effectively to emerging threats.

How Lucinity Enhances AML Investigation Against Evolving Fraud Threats

Addressing Fraud-as-a-Service in 2025 requires AML tools that are precise, flexible, and efficient. Lucinity meets these needs with systems built to support real investigative work, where timing and context are essential.

Centralized Case Management: Lucinity’s Case Manager brings together alerts from fraud, AML, KYC, and sanctions into one unified investigation flow. Instead of jumping between disconnected systems, analysts can see the full customer picture in a single interface, accelerating triage and enabling faster, more informed decisions.

Luci AI Agent: Lucinity’s investigation platform features Luci, an AI assistant built to support case reviews. Luci helps draft SARs, summarize complicated cases, map transaction patterns, and highlight risk signals, significantly reducing manual effort. 

Luci Plug-in AI Agent: The Luci Plug-in extends Luci’s AI features into commonly used tools like Excel, CRM platforms, and browser-based case systems, without requiring system changes or long setup times. Institutions using the plug-in have seen productivity improvements of up to 90%, especially useful for teams managing large volumes of fraud cases across different locations.

Final Thoughts

As Fraud-as-a-Service becomes more structured and widely available, financial institutions need to change their AML investigation approach from a narrow compliance focus to one built for active threat detection. The demand to speed up investigations, lower false positives, and deliver better results is increasing, particularly as mid-sized banks report fraud losses in the millions.

Here are four priorities institutions should focus on when improving their AML investigation efforts in the coming years:

1. FaaS is driving a transformation in fraud volume and tactics, requiring faster and smarter AML investigation processes.
2. AI adoption is growing, but effectiveness depends on workflow integration, automation of repeat tasks, and strong governance.
3. Young, inexperienced fraudsters are leveraging FaaS platforms, increasing unpredictability, and speeding up attack cycles.
4. Lucinity’s AI-supported AML investigation tools help teams scale efficiently, offering faster reviews, more accurate case files, and full regulatory alignment.

To explore how Lucinity transforms AML investigations to counter today's Fraud-As-A-Service (FaaS) environment, visit Lucinity today!

FAQs

Q1. How has AML investigation changed in response to Fraud-as-a-Service?
AML investigation has transformed from rule-based alerts to intelligence-driven analysis using real-time data and AI-powered tools to solve scalable fraud.

Q2. Why is Luci AI Agent important for AML investigation?
Luci supports analysts by summarizing case data, drafting SARs, and visualizing flows to reduce hours of manual work and helping teams handle more cases.

Q3. What role does automation play in modern AML investigation?
Automation speeds up tasks like alert triage and SAR preparation, allowing human investigators to focus on complicated cases and reduce investigation times significantly.

Q4. Can Lucinity’s AML investigation tools work with our existing systems?
Yes, Lucinity’s platform is designed for easy integration, and the Luci Plug-in can run on top of web-based tools like CRM or Excel without the need for infrastructure changes.