How to use generative AI in KYC

Traditional KYC was designed in a world where paper forms and in-person banking were the norm. Today, compliance teams face completely different challenges from encrypted messaging apps, shell companies with offshore accounts, deepfake IDs, and fraud networks that span multiple countries and platforms in mere seconds.

These challenges are even more complex in crypto platforms and virtual asset service providers (VASPs), where customer identities are harder to verify and transactions move anonymously across borders. Legacy KYC tools, such as rule engines, database lookups, and manual investigations, weren’t designed for this volume or velocity.

Enter generative AI. These models can ingest documents, summarize risk information, analyze open-source intelligence (OSINT), answer investigator questions, and generate ready-for-regulators narratives, at speed, scale and all in real time.  However, AI should never replace human judgment, and it should never be given any decision-making power. Ethical oversight is critical to ensure that final compliance decisions are always made by qualified human investigators.

This guide unpacks how compliance teams can leverage generative AI for KYC, where it has real value, how to deploy it ethically and responsibly, and how platforms like ShadowDragon can transform raw OSINT into actionable and defensible intelligence.

KYC as a concept was designed for a banking era when customers came into a branch in person to provide physical documents and wait weeks for approval. That’s not the world that any of us live in today. Digital onboarding, instant payments, cross-border payments, and complex international fraud rings have all outpaced traditional KYC verification processes.

What traditional KYC looks like

Typically, most banks and financial institutions approach KYC in the same manner:

1. Onboarding: Capture IDs, proof of address, and basic biometric analysis.
2. Screening: Check customers against sanctions lists, politically exposed person (PEP) lists, and adverse media. Politically exposed persons are public-facing individuals with roles in government or at high-profile companies who may be more vulnerable to bribery or corruption.
3. Periodic monitoring: Re-screen customers monthly, quarterly, or annually based on risk score.

This process is sufficient for checking compliance boxes, but not so much for detecting today’s sophisticated financial crimes.

Limitations of traditional KYC approaches

Legacy KYC systems are ineffective due to three main factors:

• Too much human effort, not enough time. Reviewing documents, scanning news articles, and manually triaging alerts is tedious. It delays onboarding, angers customers, and drives up compliance costs.
• High noise, low precision. Basic keyword searches and rigid rule-based logic produce huge quantities of false positives. At the same time, many real threats (layered networks, well-disguised identities) are missed as false negatives.
• Shallow data, blind spots. Reliance on static databases and form-based data collection can result in missing synthetic identities, stolen but valid credentials, and hidden social or digital connections (e.g., forums, social media, the dark web).

Why generative AI is stepping in

Generative AI is helping to fill the gaps in traditional, rule-based KYC processes, not to replace them, but to arm compliance teams with more precise tools to meet the challenges of today’s increasing complexity.

Generative AI systems are not limited to working with structured inputs (such as watchlists and database columns) in the same way as rule-based engines. They can read and interpret messy, unstructured data, such as ID documents, corporate registries, sanctions bulletins, PDFs, emails, call transcripts, social media, and non-English language news reports, and turn them into analysis in minutes rather than hours.

For compliance teams, that means:

• Scalable investigation, not just faster data collection. Generative AI can extract fields and provide them with context. It writes risk summaries, flags contradictions across documents, and drafts narratives, such as “Customer X is linked to Y through Company Z, a known shell entity.”
• Interactive, smarter screening. New platforms enable analysts to ask natural-language questions (“Show me any media that links this client to sanctioned entities”) and receive synthesized answers rather than raw hits.
• Improved use of unstructured and OSINT data. Generative AI can provide deeper context with intelligence gathered through OSINT tools like ShadowDragon Horizon™. For example, it can help reconcile disparate identifiers and correlate data points across usernames, email addresses, company directors, or social media profiles to assist with concealed links or synthetic identities.
• Better adverse media and social media monitoring. Generative AI doesn’t just return long lists of articles that have vague relevance. It groups results into themes (fraud, corruption, sanctions exposure), ranks them by relevance, and explains why it matters.

Traditional KYC workflows have been critical for regulatory compliance; however, they can’t keep pace with the speed and scale of global data alone. Generative AI enables compliance teams to dig deeper and faster, while also addressing bottlenecks in manual review, when paired with the right controls, auditability, and human-in-the-loop oversight.

The table below compares traditional KYC processes with GenAI-enabled KYC to highlight how capabilities, speed, and data depth differ across both approaches.

Generative AI helps the way compliance teams process data, investigate alerts, and document decisions. It goes beyond passive scoring to become an asset that explains risk, summarizes complex findings, and helps draft regulatory-ready reports. In this section, we’ll discuss the areas where GenAI makes the biggest impact.

Interactive smart screening and alert triage

Generative AI transforms screening alerts from static match lists into a conversation with the system. Rather than “accept” or “decline” alerts, analysts can ask questions like “Why is this name flagged as a potential PEP?” and receive explanations of the match logic, political exposure, family connections, and the confidence level of each data source.

AI also guides analysts through reducing false positives, not by hiding or deleting them, but by highlighting the meaningful ones while walking analysts through why a record should (or shouldn’t) be escalated. This results in reduced clicks, faster decisions, and an auditable trail of the reasoning behind every cleared or escalated alert.

Generative AI as a casework copilot

Writing EDD reports, SAR narratives, or customer risk summaries is a tedious, repetitive task. Generative AI does the drafting. It ingests customer data, alert history, transactions, OSINT, and regulatory language, generating a coherent narrative that meets compliance standards.

Analysts are still in control. They can edit, approve, or dispute the draft. But consistency and efficiency are dramatically enhanced, particularly at scale across large teams. This eliminates the confusion caused by differing writing styles and incomplete reports.

Adverse media and OSINT summarization

Generative AI can digest thousands of news articles, social posts, court documents, or ShadowDragon OSINT sources, and then summarize all of it so that analysts generate useful intelligence. Instead of burdening investigators with a rabbit hole of tabs, it produces something like:

• “Local news accuses this individual of fraud, but they have not been convicted yet.”
• “This customer’s business partner was found to have ties to a sanctioned entity in 2022.”
• “Alias ‘A.Morales_92’ corresponds to a matching social account identified through ShadowDragon.”

It separates unverified claims from proven or substantiated claims, identifying tone, relevance, the entities involved, and timelines, thereby eliminating the need for manual sorting through media noise.

Client outreach and document collection

KYC requests for missing documents don’t have to sound robotic or vague. Generative AI can create clear, personalized outreach messages that also adhere to regulatory guidelines.

GenAI can answer client questions about onboarding, source of funds, or ID requirements, citing internal KYC policies and jurisdictional rules so frontline teams don’t have to formulate answers on the spot.

Dynamic risk review and ongoing monitoring

Generative AI doesn’t have to wait for the next review cycle to monitor for events; it can watch for changes to customer behavior or public information as they happen in real time. When something material occurs, it summarizes just the relevant information. For example:

“A new lawsuit was filed in Singapore today involving Director X related to a money laundering investigation. A risk review is recommended.”

The table below breaks down where generative AI fits across the KYC lifecycle.

Generative AI can be a force multiplier in KYC, but it has to play by some hard rules. Machine-generated outputs, in the absence of human oversight, source traceability, and privacy controls, introduce legal and compliance risk, rather than adding value.

Every compliance team should implement the following guardrails.

• Keep humans in the loop. AI can assist in drafting risk narratives or recommended actions; however, each alert decision has to be made by an analyst and risk scores should be reviewed, edited, and validated by an analyst. Machines don’t bear legal responsibility; humans do.
• Traceability and auditability for every output. Every narrative, alert decision, or risk score outputted by the AI should be able to link back to the source data and the processing logic used. For example: What was generated? Which data did the model draw from? Who approved or rejected it?
• Ground all outputs in verified data. To avoid hallucinations, only allow models to draw from verified sources like ShadowDragon Horizon™, internal customer data, or regulated databases. If the system can’t verify information, it must be flagged as uncertainty, not presented as fact.
• Data privacy and retention rules remain in effect. Generative AI systems must comply with the GDPR, CCPA, and banking secrecy laws. In practical terms, this means storing no unnecessary personal data in AI memory, implementing encryption in transit and at rest, and establishing clear controls on data retention and deletion. In addition, vendor AI models must meet the same privacy and security standards as internal systems.

Used in conjunction with these controls, GenAI can stay on the right side of compliance, helping to support organizations’ KYC processes without creating new risks.

The table below outlines key risk areas when using generative AI in KYC, along with the necessary guardrails and how to apply them in practice.

The smartest approach to implementing generative AI in KYC is to start small, prove value, and then scale.

Step 1: Start with low-risk workflows

Start where mistakes have the least regulatory impact. In KYC, that may include using generative AI to summarize alerts, break down adverse media findings, or create first-draft narrative sections. The analyst still approves before submission, but they don’t have to start from a blank screen.

Step 2: Connect your data sources

AI is only as good as the data it has access to. Link your internal KYC platforms, customer profiles, case notes, policy wordings, and external feeds from OSINT tools such as ShadowDragon. This enables AI responses to be grounded in real evidence, rather than general assumptions.

AI security should be a top concern when integrating to any other data sources. Ensure they’re always running in a secured container/sandbox environment with least privilege access control, encryption at rest and in transit. Audit logging allows you to track who accessed what data and when, and what they did with it.

Implement data governance rules around how certain sensitive data can be stored and used. Lastly, run data integrity checks so all AI data can be traced back to its original source.

Step 3: Deploy AI copilots to analysts

Introduce generative AI as a copilot in existing tools, such as case management, screening dashboards, or onboarding portals. Analysts should be able to ask questions, get summaries or reports, or trigger actions within the context of their workflows.

Step 4: Track ROI and measurable outcomes

Don’t scale generative AI until the numbers make sense. Measure:

• Time saved per case
• Alert clearance speed
• Reduction in false positives
• Consistency of report quality

Step 5: Scale to higher-risk workflows

After successfully deploying generative AI in low-risk processes, expand it to EDD, conduct periodic reviews, and implement continuous monitoring. The AI then continuously generates risk refresh summaries, detects changes in customer behavior, and surfaces new OSINT signals.

This roadmap enables compliance teams to maintain control, leveraging GenAI for quantifiable impact, rather than noise.

Generative AI is reinventing KYC, not by replacing compliance teams, but by eliminating the tedious, manual tasks that have prevented them from connecting the dots. It can ingest documents, summarize alerts, draft risk narratives, and perform continuous customer monitoring. But where GenAI really adds value is when it’s used in conjunction with high-quality intelligence, and that’s where ShadowDragon steps in.

ShadowDragon Horizon™ provides the data, patterns and digital evidence. Generative AI handles only the tedious tasks of sorting and summarising that information, but it plays no role in decision-making. Every decision remains firmly in the hands of human analysts. Horizon™ uncovers public signals that often don’t appear in traditional KYC systems, such as usernames, breached data, email addresses, corporate connections, social graphs, and darknet artifacts.

Instead of sifting through false positives and waiting for periodic review cycles, analysts can  start with context. They’ll know why a customer is risky, how they’re connected to bad actors, and where it’s all coming from.

For compliance leaders, this is the difference between reactive and proactive risk management. Book a demo with ShadowDragon to learn more about how OSINT can transform your KYC processes.