Most deceptive financial schemes are recognized only after the conversation reaches its most obvious stage. A stranger recommends a dubious trading platform, promises unusually high returns, or asks for an urgent transfer. By then, however, the psychological groundwork has often been laid over days or even weeks of apparently ordinary communication.

Recent academic research suggests that the earliest messages may contain valuable warning signals long before money enters the discussion. Instead of analyzing a single suspicious text, researchers are beginning to study how fraudulent conversations evolve over time. The result is a new approach to detection that focuses on behavioral progression rather than isolated keywords.

Why the First Messages Matter More Than the Last

Traditional fraud detection usually works backward. Security systems flag a message because it contains highly specific phrases, such as:

• "guaranteed profit"
• "limited investment opportunity"
• "verify your account"
• "send your payment"
• "urgent transfer"

These indicators remain useful, but they appear relatively late. By the time an investment scam openly requests money, the attacker has already achieved several crucial objectives:

[Initial Contact] ➔ [Establish Credibility] ➔ [Build Rapport] ➔ [Identify Financial Interests] ➔ [Introduce Proposal]

In other words, the request for capital is not the beginning of the fraud — it is the calculated result of a carefully managed sequence.

Unlike phishing campaigns that attempt to trigger immediate panic, investment fraud relies heavily on trust. The target must believe that the other person possesses genuine market knowledge, insider tips, or successful trading experience. That confidence rarely develops after a single message; instead, it is nurtured through repeated interactions that seem increasingly natural.

An early conversation may cover topics that appear completely harmless on their own:

• Local or global financial news
• Cryptocurrency market trends
• General business opportunities
• Career backgrounds and trading experience
• Personal hobbies, lifestyle, or travel

The warning emerges not from the subjects themselves, but from how they are combined. A bad actor is often less interested in discussing assets than in discovering how receptive the other person is. Casual questions are used as strategic tools:

"Have you invested before?" "Do you prefer stocks or crypto?" "Are you interested in passive income?"

Each answer helps the scammer decide which narrative is most likely to succeed later, turning the early dialogue into a form of psychological reconnaissance.

Also Read: Pig Butchering Scam: How It Works and How to Avoid It

What the PreScam Research Actually Studied

Rather than asking whether a single message looked suspicious, the researchers behind the PreScam study asked a different question: Can the future direction of a conversation be predicted while the scam is still developing?

To investigate this, they collected large numbers of user-submitted reports and transformed them into a structured research dataset. The resulting benchmark contains:

• 177,989 raw conversational messages
• 11,573 complete scam conversations
• 20 distinct fraud categories
• Hierarchical annotation based on a proposed scam kill chain
• Turn-level labels describing the psychological actions of the perpetrator and victim responses

This is important because conversational fraud does not evolve randomly. The authors argue that many schemes follow highly recognizable stages. Early messages focus on engagement, while subsequent ones build credibility. Only after sufficient trust is established does the discussion move toward financial commitment.

Traditional Detection vs. Conversation Progression Analysis

The practical implication is significant. While people often ask whether fraud can be recognized from the opening exchange, research suggests the answer is yes—not because the first message is obviously malicious, but because the order of questions, the pace of trust-building, and the shifting objectives reveal a clear manipulation pattern.

The Scam Kill Chain in Conversations

One of the most valuable contributions of the PreScam research is the concept of a Scam Kill Chain — a conversational framework inspired by cybersecurity models that map how cyberattacks evolve through sequential stages.

Initial Contact

The attacker establishes communication. At this stage, there is absolutely no mention of investments or money. The sole objective is to capture attention and secure a reply. Typical openers include random greetings, comments on shared interests, professional networking requests, or casual discussions about current events.

Engagement

Once a regular flow of communication is established, the perpetrator gradually builds psychological influence. Rather than pushing an asset immediately, they focus on creating familiarity, credibility, reciprocity, and emotional comfort. Financial opportunities are eventually introduced as casual, natural suggestions rather than aggressive pitches.

Termination

Only after establishing deep rapport does the conversation shift toward its true objective. Depending on the scheme, this culminates in:

• Transferring funds directly
• Registering on a rigged trading platform
• Downloading malicious portfolio software
• Sharing sensitive API keys or seed phrases
• Authorizing smart contracts or crypto transfers

Because traditional security filters often focus strictly on this final stage, they miss the behavioral transitions that happen much earlier.

Psychological Tactics Before the Money Request

The PreScam study also highlights that instead of analyzing what is said, we must look at the intent behind the words. Each turn in a conversation serves a specific behavioral objective, such as:

• Establishing a shared bond (rapport)
• Projecting authority and expertise
• Stimulating curiosity
• Lowering the target's perception of risk
• Testing the victim's willingness to comply

This perspective explains why many investment scams remain highly convincing. A message like "I've been trading for several years" or "How do you usually manage your portfolio?" is not suspicious on its own. Collectively, however, they form a systematic persuasion process.

The researchers discovered that these psychological techniques change predictably over time. Early stages prioritize friendly bonding, while later stages focus on preparing the victim for compliance.

Interestingly, the study evaluated the predictive capabilities of advanced language models. While general-purpose LLMs proved excellent at summarizing dialogues, they were considerably less effective than specialized, supervised models at forecasting how a manipulation strategy would unfold in future turns. Recognizing conversational dynamics proved to be a far more complex task than simply identifying suspicious vocabulary.

Also Read: Why Modern Ponzi Schemes Recruit Through LinkedIn: The Rise of Professional Social Engineering

Why Investment Scams Look Harmless at First

Investment fraud depends entirely on credibility. Unlike bulk email scams, it rarely succeeds overnight. The perpetrator must first become a trusted contact before transitioning into a financial advisor. This shift is often so gradual that the target fails to notice when the nature of the relationship changes.

For retail investors, waiting for an obviously fraudulent message to appear means waiting too long. The stronger warning sign is the structural sequence itself.

Ultimately, these schemes are rarely defined by a single deceptive sentence. They are defined by how the relationship and dialogue transform over time.

Can AI Safely Predict a Scam Early?

The question of whether artificial intelligence can reliably stop a scam before financial damage occurs is highly nuanced. The PreScam research does not claim that every fraudulent exchange can be flagged after five messages. Instead, it proves that these dialogues follow highly predictable behavioral trajectories long before a transaction is requested.

Therefore, the goal is not absolute certainty, but early risk estimation.

Consider two separate conversations. Both begin with a friendly greeting, discuss market trends, and continue for a week. A traditional filter analyzing only the latest messages might see nothing wrong. A progression-aware system, however, evaluates the pattern:

• Has the interaction frequency become unusually persistent?
• Is trust being built without a logical real-world foundation?
• Has the focus of the dialogue systematically drifted toward investments?
• Are subtle persuasion and urgency tactics becoming more frequent?

While none of these indicators prove malicious intent individually, their combined presence significantly raises the probability of fraud. Much like credit-risk models that look at behavior over time rather than a single transaction, conversational prediction models analyze the complete social engineering lifecycle.

What Retail Investors Can Learn From This Research

Retail investors do not need complex algorithms to protect themselves. The most valuable takeaway is purely psychological: stop waiting for a "smoking gun."

Many victims expect a fraudster to immediately demand cash or promise impossible riches. Because professional perpetrators avoid these obvious traps early on, their approach feels entirely safe. The opening stage is intentionally mundane. Only after enough trust has been accumulated does the attacker introduce high-risk requests.

A safer, more effective approach is to shift your focus from verifying the investment platform to evaluating the interaction itself. Ask yourself:

1. Why did this specific person contact me out of the blue?
2. Why is this stranger showing such an intense interest in my daily life and finances?
3. Why are financial topics being introduced so naturally into casual chats?
4. Why are we moving away from a public, moderated platform to a private messaging app?
5. Why am I being encouraged to trust an individual's word rather than independent, regulated sources?

Focusing on behavioral progression rather than technical indicators is often the most reliable way to spot a trap before any money leaves your account.

Practical Early Warning Checklist

The table below outlines a quick reference guide of behaviors that merit deep suspicion when they begin to overlap.

Conclusion

The nature of online deception is changing. Rather than relying on static, automated spam messages, modern financial scammers deploy long-term conversational strategies designed to systematically dismantle personal boundaries and skepticism.

This shift demands a new defensive posture. While traditional security filters remain excellent at flagging malicious links or blacklisted keywords, progression-based analysis offers an extra layer of defense. By understanding the underlying patterns of conversational manipulation, everyday investors can spot the psychological stages of a scam and break off contact long before any financial harm is done.

FAQ

Can a scam really be detected from only a few messages?

Not with absolute certainty. However, research proves that early dialogues contain distinct behavioral markers. Early detection provides a probability-based risk score rather than a definitive guarantee.

What is the PreScam dataset?

PreScam is a specialized academic benchmark consisting of 11,573 fully mapped scam conversations (comprising 177,989 messages) across 20 distinct categories, detailed with turn-by-turn psychological annotations.

Why don't scammers ask for money right away?

Because immediate financial demands trigger instant suspicion. By investing time in building a personal relationship, the perpetrator significantly increases the likelihood that their eventual investment pitch will be accepted.

What is conversation progression analysis?

It is a security approach that evaluates how a dialogue changes, shifts focus, and escalates over time, rather than examining individual sentences in isolation.

Are investment scams different from standard phishing?

Yes. Phishing typically relies on urgent, automated, one-step messages (like fake delivery alerts). Investment fraud is a high-touch, long-running social engineering campaign built on personal relationships.

Can AI replace human judgment in stopping these schemes?

No. While machine learning can flag suspicious conversational patterns and estimate risk, the ultimate line of defense remains independent personal verification and healthy skepticism.

Sources

• PreScam: A Benchmark for Predicting Scam Progression from Early Conversations. arXiv:2605.12243
• The Anatomy of Scam Scenarios: Large-Scale Behavioral Analysis of Conversational Fraud
• An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory

Need Help?

If you are currently reviewing your recent chat history with an online "trading mentor," "wealth manager," or "platform representative" — and realizing that the behavioral red flags were present from your very first messages — you can request a free consultation with StockView specialists.

Our expert review can help determine if your interactions follow the documented behavioral stages of investment fraud and evaluate your options for recovering funds through bank chargebacks, payment disputes, or formal legal channels.