Below is a concise but complete blueprint you can use to design, evaluate, or pitch an end-to-end AML (anti‑money‑laundering) compliance platform that provides real‑time alerts.

1. Value proposition (one line)

• A single platform that detects, investigates, and reports suspicious activity in real time across channels and products, reducing false positives, accelerating investigations, and ensuring regulatory compliance (SAR/STR filing, KYC/EDD, transaction monitoring, sanctions screening).

2. Core capabilities

• Data ingestion & normalization: ingest transactions, transfers, account metadata, KYC data, device signals, payment rails, trade/order data, and external watchlists; normalize to a unified schema.
• Real‑time transaction monitoring: stream analytics and rule/ML scoring on each event with sub‑second or configurable latency.
• Rules engine: configurable, versioned rules (Boolean, threshold, temporal patterns, aggregation windows, nested conditions).
• Machine learning & risk scoring: supervised/unsupervised models for anomaly detection, behavioral scoring, network/graph analytics, typology-based detectors; model explainability and drift monitoring.
• Sanctions & PEP screening: instant screening against global sanctions lists, PEPs, RCA (relatives/close associates) and adverse media feeds.
• Entity resolution & network graphing: deterministic and probabilistic entity resolution to link accounts, devices, beneficiaries; visualize networks and money flows.
• Case management & workflows: auto-open cases from alerts, assignable tasks, built‑in investigation workspace with timeline, evidence attachments, audit trails.
• Real‑time alerting & triage: prioritized alerts (risk score + rule), multi‑channel notifications (email, Slack, SIEM, SMS), dynamic throttling and escalation policies.
• SAR/STR filing & regulatory reporting: templated filing outputs, regulatory formatting, secure submission, and audit logs.
• KYC / CDD / EDD automation: risk‑based KYC checks, trigger EDD workflows with required documents and analyst notes.
• Explainability & auditability: for each alert show triggers, rule versions, scores, feature contributions, and model provenance.
• Data retention & compliance: retention policies, legal hold, data minimization, and encryption at rest/in transit.
• Analytics & dashboards: KPI dashboards (alerts, disposition rates, time-to-close, false positive rate), root-cause analysis, and monthly/quarterly reports.
• Integrations & APIs: connectors for core banking, payment processors, ledger/blockchain nodes, identity providers, SIEM, ticketing, and regulators.

3. Typical architecture (high level)

• Ingest layer: streaming (Kafka / Kinesis) + batch ETL; connectors for APIs, SFTP, webhooks.
• Normalization & enrichment: mapping, address normalization, risk enrichment (geo, device fingerprinting, adverse media).
• Real‑time processing: stream processing (Flink, Spark Structured Streaming) with rules engine + model scoring microservices.
• Storage: hot store for recent events (NoSQL), cold store (data lake) for historical analytics.
• Graph store: specialized DB for network queries (Neo4j, TigerGraph).
• Case management & UI backend: microservices + RBAC.
• Notification & orchestration: event bus + notification microservices.
• Security & compliance: HSM, KMS, logging, SIEM integration.
• Deployment: cloud-native (K8s) with multi-region failover or hybrid on-prem connectors.

4. Real‑time alerting specifics

• Latency targets: e.g., 100ms–2s for per‑transaction screening; up to minutes for aggregated/behavioral alerts.
• Alert prioritization: composite score = rule severity * risk score * transaction value * business impact.
• Alert enrichment: attach KYC snapshot, recent transactions, device metadata, sanctions hits, and graph neighbors.
• Notification channels: push to analyst dashboard, email, Slack/Teams, webhook to SOAR/SIEM, SMS for critical escalations.
• Deduplication & suppression: suppress duplicate alerts within configurable windows; correlate related alerts into single case.
• Rate limiting & backpressure: elastic scaling to avoid missed events under load; prioritized queues for high-risk flows.

5. Detection methods & typologies

• Simple rules: velocity limits, amount thresholds, blacklisted accounts.
• Temporal patterns: smurfing, structuring, rapid in/out.
• Behavioral analytics: deviations from historical baseline, device switching, abnormal geolocation.
• Graph analytics: hub/spoke, sudden centrality changes, circular flows.
• Unsupervised models: clustering outliers, autoencoders for novel patterns.
• Supervised models: fraud/AML classifiers trained on labeled SARs.
• Hybrid: rules + models + manual feedback loop.

6. Investigator workflow (example)

• Alert arrives (real‑time) → system shows short summary and risk score → auto-enrich with documents & history → analyst triages (dismiss, escalate, request EDD) → if escalate, auto-open case and assign → investigation steps tracked → if suspicious → generate SAR/STR with prefilled fields → submit to regulator and archive.

7. Regulatory & compliance considerations (US-focused but applicable broadly)

• SAR/STR filing requirements, FinCEN formats (if US), suspicious activity thresholds, KYC/CDD/EDD rules, sanctions compliance (OFAC and global lists), GDPR/CCPA data protections, recordkeeping timelines.
• Provide configurable compliance rules per jurisdiction and export-ready filings.
• Audit logs, immutable evidence chain, role-based access, separation of duties.

8. Security, privacy & governance

• Encryption in transit/TLS and at rest (AES‑256).
• Key management (KMS/HSM) and tokenization for PII.
• RBAC + MFA for workspace access.
• Field-level masking in dashboards for low‑privilege users.
• Audit trail with immutable logging and tamper-evident storage.
• Model governance: versioning, validation, performance monitoring, explainability reports.

9. Operational considerations & SLAs

• Uptime: 99.9%+ for core monitoring pipelines.
• Processing SLA: real‑time screening 99% within target latency (e.g., <2s).
• Alert throughput: scaling architecture to handle peak TPS with graceful degradation.
• False positive / alert fatigue management: continuous tuning, analyst feedback loop, adaptive thresholds.
• Business continuity & DR: multi-region replication, regular runbooks & drills.

10. Metrics / KPIs to track

• Alerts per 1,000 transactions.
• True positive rate (SARs filed / alerts closed as suspicious).
• False positive rate.
• Average time to triage and time to close.
• SAR filing rate and acceptance/rejection rates (if available).
• Model accuracy, precision, recall, and drift indicators.

11. Integrations to prioritize

• Core banking / ledger systems, payment gateways, SWIFT/ACH rails.
• Identity verification (IDV), sanction/PEP providers, adverse media data providers.
• SIEM, SOAR, ticketing systems (ServiceNow, Jira), and reporting/regulatory portals.
• Blockchain explorers / node APIs for crypto businesses.

12. Implementation roadmap (90–180 days typical M‑VP)

• Phase 0 (planning): requirements, data mapping, compliance spec (2–3 weeks).
• Phase 1 (core): ingest + normalization + simple rules + UI + case mgmt + sanctions screening (6–8 weeks).
• Phase 2 (real‑time & enrichment): streaming pipeline, latency targets, KYC enrichment, notifications (4–6 weeks).
• Phase 3 (ML & graph): deploy initial ML models, entity resolution, graph visualizer (6–8 weeks).
• Phase 4 (hardening): governance, model ops, DR, performance tuning, regulatory reporting templates (4–6 weeks).
• Ongoing: continuous model retraining, rule tuning, new integrations.

13. Typical tech stack suggestions

• Messaging/streaming: Kafka, Kinesis.
• Stream processing: Flink, Spark Structured Streaming.
• Storage: Cassandra / DynamoDB (hot), S3 / data lake (cold).
• Graph DB: Neo4j, TigerGraph.
• ML infra: Python, scikit-learn, XGBoost, PyTorch; model serving via TorchServe or Seldon.
• UI: React + TypeScript.
• Auth & secrets: OAuth2/OpenID Connect, Vault.
• Container orchestration: Kubernetes.

14. Common risks & mitigations

• Data quality gaps → implement validation, enrichment, and fallback rules.
• High false positives → gradual rollout, analyst feedback loop, supervised learning on labeled outcomes.
• Regulatory changes → modular compliance rules and frequent legal review.
• Privacy breaches → encryption, least privilege, monitoring.
• Scalability under peak → autoscaling and priority queues.

15. Example alert types (with suggested triggers)

• High‑value outbound transfer: single transfer > $X or cumulative > $Y in 24h.
• Velocity/structuring: >N deposits <M hours just below reporting threshold.
• Sanctions hit: beneficiary or downstream institution matches sanctions list.
• Rapid account activity change: >Zx deviation vs 90‑day baseline.
• Network loop: funds routed through N+ accounts back to origin within T hours.
• Crypto bridging: on/off ramp involving high‑risk mixers or privacy coins.
• KYC mismatch: identity document failed verification but transactions continue.

16. Sample alert payload (fields)

• alert_id, timestamp, alert_type, score, triggered_rules[], transaction_ids[], account_snapshot{KYC_level, name_hash, risk_rating}, enrichment{sanctions_hits, adverse_media_score, geolocation}, graph_neighbors[], analyst_notes[], case_id(if any), links_to_evidence.

17. Pricing & licensing considerations (models)

• Per‑event / per‑transaction pricing for screening, plus seat-based pricing for analysts.
• Add‑ons for premium data (adverse media, sanctions feeds), dedicated support, on‑premise deployment.
• Consider cost caps or blended pricing for high‑volume customers.

18. Deliverables you can ask for or expect

• Functional spec & compliance mapping.
• Data dictionary and integration plan.
• M‑VP with configurable rules and real‑time alerts.
• Onboarding & training for compliance team.
• SLA and runbook documentation.
• Ongoing managed tuning & model retraining agreement.

If you want, I can:

• Draft sample rule sets for a particular business (bank, fintech, crypto exchange).
• Create a one‑page technical architecture diagram or a mockup of the alert UI.
• Produce a compliance mapping for a specific jurisdiction (e.g., US FinCEN).

Which of those would be most useful next?