How to Prevent AI-Driven Financial Crime: Preparing for Modern Criminal Tactics in 2025

In 2024, an employee at Arup’s Hong Kong office authorized transfers totaling approximately $25 million after attending a video call with what appeared to be the company’s CFO and other senior executives. The video call looked and sounded real, but every individual on the call was an AI-generated deepfake.

The employee, convinced of the video's authenticity, followed instructions to send the funds across multiple local bank accounts. The scam was only discovered after the employee sought post-transfer confirmation from the UK headquarters, which revealed that no such request had been made. 

This kind of attack signals a change in how AI-driven FinCrime operates now by being less about breaching systems and more about exploiting human confidence with advanced and automated deception.

This blog explores how these types of threats are changing in 2025, what makes them so difficult to detect, and what financial institutions, regulators, and investigators can do to prepare.

What Is AI-Driven Financial Crime?

AI-driven FinCrime involves using artificial intelligence tools to carry out, support, or hide illegal financial actions. It works by imitating, automating, and misleading in ways that closely resemble legitimate activity, making detection more challenging.

These crimes are increasingly carried out through systems that learn from data. Machine learning models can predict fraud detection thresholds, adapt to new environments, and simulate normal behavior to avoid raising suspicion. 

This automation allows for scalable and targeted attacks to be executed in seconds, with minimal manual effort. Here are the most common forms of AI-driven financial crime:

• Deepfake scams: AI-generated videos or audio recordings that convincingly mimic real people, often used to authorize fraudulent transfers or trick employees into sharing sensitive information.
• Synthetic identity fraud: The creation of fake profiles using both real and invented data. These identities pass verification processes, open accounts, receive credit, and then default, leaving no trace of a real person behind.
• AI-powered phishing: Today’s fraudsters use natural language models to create emails and messages that sound contextually accurate and human. Combined with scraped personal information, these messages feel genuine.
• Behavioral manipulation: AI studies individual or institutional behavior to personalize its scams. For instance, if a system notices that certain types of transactions usually go unchecked, it will replicate that pattern to commit fraud unnoticed.

Emerging Tactics Behind AI-driven Financial Crime in 2025

In 2025, the role of AI-powered FinCrime is vastly different from what it was a few years ago. Criminals aren’t relying on brute force or guesswork. They’re leveraging smart systems, multi-channel deception, and automation to bypass even the most robust compliance programs.

These five emerging tactics show how AI-driven financial crime is transforming and why institutions need to transform faster.

1. Hyper-personalized Phishing Through AI-Scraped Profiles

Phishing doesn’t rely on bulk emails and generic lures anymore. Criminals now use AI to collect and process personal information from public sources like social media, company websites, and leaked databases. They feed this data into generative language models to create highly personalized messages, often referencing actual events, job roles, or colleagues.

This increases the success rate of social engineering, making it more likely for victims to share information, click on malicious links, or approve financial transactions. The messages don’t look suspicious because they sound familiar.

2. Deepfake Conferencing to Authorize Fraudulent Transactions

Video is now a weapon with deepfake technology improving in quality and speed, scammers are creating fake video calls where executives appear to issue instructions in real time. The case of Arup, the construction engineering company, is a good example of how an employee attends a video call with AI-generated impersonations of senior management.

This tactic exploits both organizational hierarchy and urgency. When a “CFO” speaks face-to-face and asks for an immediate transfer, even well-trained employees are at risk of falling in the trap.

3. AI-Led Micro-Fraud Across Multiple Channels

Instead of large fraud attempts, criminals now use AI to execute micro-fraud, small, fast-moving transactions spread across multiple channels, accounts, or jurisdictions. These individual actions often go undetected, but together, they result in substantial losses.

AI enables them to test boundaries across systems quickly. For example, by sending slightly unusual but allowable payment requests, they identify how systems react, then scale that pattern across thousands of accounts.

4. Rapid Laundering Through Crypto and Digital Asset Platforms

AI is accelerating how illicit funds are moved and layered across financial systems. In seconds, it can distribute stolen funds through dozens of cryptocurrency wallets, run obfuscation tools, convert assets across chains, and re-enter funds into fiat via exchanges or shell companies.

The use of AI in this laundering process makes tracing assets exponentially harder. It also complicates reporting and slows down investigation timelines. The anonymity of blockchain, once a challenge on its own, now becomes part of a much faster, more structured evasion strategy.

5. Mimicking Legitimate Behavior With Simulated Transactions

Criminals are training models on real transaction data, which is gleaned through leaks, third-party vendors, or public reporting, to replicate typical customer behavior so that suspicious transactions look convincingly normal.

For example, a scammer may delay sending high-value transfers until a “payday pattern” is detected in the victim’s account. They may also split illegal transfers into amounts that mimic regular vendor payments. The behavior doesn't trigger alarms because it mirrors the customer’s historical data.

The Role of Ethical AI in Financial Crime Prevention

As financial institutions adopt more AI tools to manage rising risks, careful and accountable use is necessary. AI-enabled financial crime raises technical, legal, and ethical concerns that demand thoughtful monitoring.

Without ethical AI practices, even the most advanced systems can fail to meet regulatory expectations or cause unintended harm. This section explores eight key priorities for embedding ethical AI into financial crime prevention, grounded in real operational needs, not abstract principles.

1. Establish Transparent, Auditable Workflows

Regulators and internal audit teams expect clear documentation of how decisions are made. Ethical AI must deliver traceability showing how a system flagged a transaction, what data contributed to the alert, and which steps were taken by both AI and human investigators.

Some platforms now embed audit log panels directly into the investigation workflow. This allows compliance teams to export case histories for review and demonstrate that each alert was processed consistently and within policy. It also simplifies internal oversight, helping senior teams assess system effectiveness in real time.

2. Use AI to Support Investigators, Not Replace Them

AI can handle repetitive, time-consuming tasks at scale, but decision-making still relies on human expertise. In practice, this means using AI to summarize long reports, flag inconsistencies in customer profiles, visualize money flows, or generate structured SAR drafts. The final determination, however, should always remain with a trained investigator.

This approach helps institutions reduce investigation times significantly while ensuring that decisions are grounded in context and experience. It also protects against overreliance on black-box outputs that may not be defensible under scrutiny.

3. Focus on Configurable Risk Models That Reflect Current Threats

As AI-driven fraud becomes more adaptive, the ability to quickly adjust and refine monitoring strategies is essential. Institutions need monitoring systems that allow risk teams to adjust detection thresholds, modify scenarios, and introduce new typologies directly, without relying on long development cycles or external support.

Configurable environments make it possible to react to emerging threats, such as rising phishing attacks or unusual transaction flows, by immediately refining how alerts are triggered and prioritized. This flexibility improves the precision of monitoring, reduces false positives, and ensures that detection remains aligned with the real risk environment.

4. Centralize Multi-Source Intelligence to Build Context

Fraud rarely occurs in isolation. Ethical AI systems should be capable of synthesizing data from various methods, such as transaction monitoring, customer records, third-party alerts, and negative news feeds, into one cohesive view.

This improves the quality of decision-making while reducing the time spent toggling between tools. Centralizing signals and applying automated risk annotations helps analysts grasp the full context of a customer or transaction more quickly, resulting in faster and accurate assessments.

5. Support Safe and Legal Sharing of Risk Insights

Criminal tactics often span institutions, regions, and platforms. While privacy laws prevent the sharing of personal data, general risk patterns, typologies, and emerging threat indicators can often be shared in an aggregate form.

Ethical AI frameworks must enable secure collaboration channels that anonymize data, protect identities, and comply with local regulations. Institutions that participate in intelligence networks, while respecting data boundaries, are often better positioned to respond to systemic risks.

6. Blend Automation With Manual Oversight Where It Matters Most

High-volume alerts and simple task automation are ideal use cases for AI. But when a decision could impact a client relationship, result in a reportable action, or trigger escalation, human review is essential.

Flexible systems should allow teams to automate the workflow, like adverse media checks or PEPs list screening, while giving investigators full control over what gets submitted, flagged, or closed. This hybrid model prevents errors, improves trust in AI tools, and aligns with regulatory expectations for monitoring.

7. Re-evaluate Past Transactions to Surface Missed Risk

AI-driven financial crime often evolves faster than detection models can keep up. Cases that appeared normal when first reviewed may reveal hidden patterns when analyzed later with improved logic. Ethical AI systems should allow institutions to revisit past transactions, reapply updated detection scenarios, and raise cases from historical data if new risks are identified.

This retrospective capability strengthens financial crime prevention by ensuring that missed signals do not stay hidden indefinitely. It also helps institutions demonstrate diligence and continuous improvement in risk monitoring to regulators and auditors.

How Lucinity Helps Address AI-driven Financial Crime in Practice

The challenges outlined in this blog require solutions that are practical, responsive, and built for financial crime teams. Lucinity is designed specifically for these conditions, giving institutions tools that simplify complications and improve regulatory compliance.

Case Manager: When fraud cases span multiple sources of payments, KYC, sanctions alerts, and manual reviews, investigators often waste time stitching together fragmented data. Lucinity’s Case Manager solves this by pulling all relevant information into one unified workspace.

This matters in AI-driven fraud cases where timing, context, and source verification are important. For example, in deepfake scenarios or synthetic identity cases, every alert, transaction, and document needs to be reviewed side-by-side. It also supports in configurable workflows, so institutions can group cases by typology, escalation need, or risk profile.

Customer 360: In AI-driven financial crime, fraudsters often exploit small blind spots in how institutions view customer behavior. Lucinity’s Customer 360 aggregates data from KYC files, transaction records, behavior patterns, and external data sources to provide a dynamic risk profile.

It updates automatically and enables compliance teams to spot inconsistencies, such as a sudden jump in payment activity or a mismatch between declared identity and transaction patterns. In cases where synthetic identities are used to access credit or launder funds, this view makes it easier to detect subtle changes that rule-based systems often miss.

Luci Agent: AI-driven financial crime requires fast interpretation of large datasets, but decisions still need to be evidence-based and defensible. That’s where Luci comes in. As an AI-powered assistant, Luci helps summarize cases, highlight risk indicators, surface negative news, and generate draft SARs.

Every insight is backed by data, and every action is logged. Teams use Luci to enhance focus and ensure consistent outcomes.

Final Thoughts

FinCrime is growing fast, with AI being used by criminals to speed, scale, and increase the complexity of attacks. What used to be isolated scams are now well-coordinated, automated events designed to blend into everyday activity.

As the examples in this blog show, these risks cannot be addressed with outdated systems or fragmented responses. Real solutions depend on AI-powered tools, better workflows, and better-aligned teams. The right technology used responsibly and with proper human monitoring can transform the way institutions detect, understand, and prevent AI-driven financial crime.

1. In early 2024, an employee at a UK engineering firm was tricked into transferring $25 million after a video call with AI-generated deepfakes.
2. From personalized phishing to simulated payment patterns, criminals are using AI to replicate normal behavior and stay below detection thresholds.
3. Static rules and siloed investigations don’t work against evolving fraud. Platforms must allow teams to adjust detection logic and access all case data in one place.
4. AI should simplify workflows, not add risk. Systems need to offer clear logic, maintain traceability, and support rather than replace human decision-making.

To explore how to reduce investigation time and strengthen control over AI-driven financial crime, visit Lucinity.

FAQs

1. What is AI-driven financial crime, and why is it growing?
AI-driven financial crime refers to fraud and laundering schemes that use automation, deepfakes, and behavior mimicry to avoid detection. These tactics are scalable and often bypass rule-based systems.

2. How can financial institutions prevent AI-driven financial crime?
Centralizing investigations, using AI for support tasks, and designing systems that adjust to new risks without lengthy rollout times all contribute to more responsive and efficient operations.

3. Why is human oversight still essential in AI-driven financial crime detection?
AI can surface insights and patterns quickly, but human investigators are needed to assess context, validate findings, and ensure regulatory compliance.

4. What tools help manage AI-driven financial crime investigations effectively?
Unified case managers, dynamic customer profiles, and AI assistants that summarize data and highlight risks all help teams work faster and more thoroughly.