Overview
What is AI AML & KYC automation?
Anti-money-laundering (AML) and know-your-customer (KYC) programs must detect illicit activity across enormous transaction volumes. Legacy rule-based systems generate overwhelming noise, by industry benchmarks, 85–95% of alerts are false positives, each taking 30–45 minutes to review.
AI AML/KYC automation uses machine learning and graph neural networks to model the relationships and behaviors behind real financial crime, sharply reducing false positives (sources report reductions up to ~90%) while surfacing sophisticated patterns rule engines miss, with human investigators making the final call.
What the system analyzes
The Challenge
Compliance teams drown in false positives
More than 90% of transaction-monitoring alerts at most banks are false positives, by McKinsey's estimate, a massive cost that also buries the genuine signals investigators need to find.
Overwhelming False Positives
85–95% of legacy alerts are false positives, each costing 30–45 minutes of review.
Missed Sophisticated Crime
Rule engines miss layered, networked laundering patterns that don't trip fixed thresholds.
Rising Regulatory Pressure
Expectations and penalties grow while transaction volumes climb.
Investigator Burnout
Endless low-value alert review drives cost and turnover in compliance teams.
Education AI Solutions
How Solnix automates AML & KYC
Behavioral Monitoring
Model normal vs. anomalous behavior beyond fixed rules.
Graph Analysis
Detect laundering networks with graph neural networks.
Alert Prioritization
Rank alerts by genuine risk to focus investigators.
PEP & Sanctions Screening
Screen entities against PEP and sanctions lists with entity resolution.
KYC Automation
Streamline onboarding and periodic review.
Case Support
Pre-assemble investigation context for analysts.
End-to-End Implementation
How Solnix implements AML & KYC AI
A phased rollout that runs AI alongside existing rules to prove false-positive reduction and detection gains before cutover, with full explainability for regulators.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Key Capabilities
Key capabilities
Behavioral AI
Learn normal patterns per entity.
Benefits
Graph Detection
Surface networked crime.
Benefits
Alert Prioritization
Risk-rank every alert.
Benefits
Entity Resolution
Resolve identities across data.
Benefits
Explainable Risk
Show why an alert fired.
Benefits
Human-in-the-Loop
Analysts decide.
Benefits
Who Benefits
Outcomes for investigators, compliance, and the firm
For Investigators
Spend time on real risk, not noise.
For Compliance Leadership
Stronger, more efficient programs.
For the Firm
Reduced financial-crime and regulatory risk.
Education Segments
Explore related capital-markets AI
Regulatory Reporting Automation
Connect detection to automated regulatory filings.
Market Sentiment Intelligence
Add external risk signals to monitoring.
Risk Model Enrichment
Unify financial-crime and market risk.
Algorithmic Trade Intelligence
Apply behavioral modeling across the desk.
Enterprise Automation
Automate compliance back-office workflows.
Agent Ecosystem
Build multi-step investigation agents.
Methodology
How Solnix builds for capital markets
01, Trading & Risk Workflow Assessment
01, Trading & Risk Workflow Assessment
We map your desk, risk, and compliance workflows to target the AI interventions with the clearest, measurable edge.
02, Market-Data & Model Architecture
02, Market-Data & Model Architecture
We build governed pipelines over market, reference, and alternative data with the latency and lineage trading and risk demand.
03, Model Validation & Backtesting
03, Model Validation & Backtesting
Every model is backtested and independently validated against historical regimes before it informs a decision.
04, Compliance & Audit by Design
04, Compliance & Audit by Design
Explainability, audit trails, and model-risk-management alignment (SR 11-7) are built in, not bolted on.
05, Continuous Monitoring
05, Continuous Monitoring
Live monitoring tracks model performance and drift across regimes, triggering revalidation as markets change.
FAQ
Questions from CCOs and financial-crime leaders
Get Started
Catch the Crime, Not the Noise.
Talk to a Solnix financial-crime AI specialist about an AML/KYC pilot scoped to your transaction data and compliance program.
Talk to a Financial-Crime AI Specialist →