Enterprise Financial Economic Crime (FEC) Ecosystem Blueprint¶

Comprehensive Business, Compliance, Architecture, and Engineering Requirements Specification¶

Audience: Business leadership, compliance officers, regulators, software architects, developers, analysts, investigators, model governance teams.

Chapter 1: Executive Overview¶

1.1 Purpose¶

This document defines the architecture, operating model, business requirements, compliance requirements, process model, data model, governance model, and technical design principles for a full enterprise Financial Economic Crime (FEC) ecosystem.

The purpose is to establish a configurable, modular, auditable, enterprise-grade operating platform capable of supporting multiple financial institutions, business lines, jurisdictions, and regulatory environments.

This is not an AML application. This is not a sanctions tool. This is not a workflow product.

It is a Financial Crime Operating System.

1.2 Scope¶

Covered domains:

Customer onboarding / KYC / CDD
Enhanced Due Diligence (EDD)
Periodic review
Event-driven review
Customer risk rating
Name screening
Sanctions screening
Transaction screening
Transaction monitoring
Alert management
Case management
Investigation management
Network analysis
Entity resolution
Regulatory reporting (SAR/STR)
QA / quality assurance
policy governance
model governance
configuration governance
auditability
analytics / MI / KPI frameworks
AI / ML integration
external integrations
multi-business-line support
multi-institution deployment capability

Excluded from initial implementation scope but architecturally supported:

fraud modules
trade surveillance
market abuse
crypto analytics

Chapter 2: Conceptual Operating Model¶

2.1 Core Architectural Philosophy¶

The enterprise FEC ecosystem is based on separation of concerns.

Layer 1: Process Orchestration Layer¶

Controls business journeys.

Examples:

customer onboarding
transaction alert handling
sanctions escalation
periodic review
SAR filing
QA review
exception management

Layer 2: Intelligence / Decision Layer¶

Reusable analytical modules.

Examples:

Name Screening
Customer Risk Rating
Transaction Monitoring
Transaction Screening
Network Analysis
Entity Resolution
Adverse Media Analysis
Risk Scoring
Alert Prioritization
NLP Intelligence
AI Copilot

Layer 3: Operational Execution Layer¶

Human work management.

Examples:

analyst queues
investigations
escalations
evidence review
approvals

Layer 4: Governance Layer¶

Control framework.

Examples:

audit trail
policy governance
configuration approval
model governance
SoD enforcement

Layer 5: Data Foundation Layer¶

Canonical enterprise data fabric.

Chapter 3: Business Process Architecture¶

3.1 Customer Lifecycle Processes¶

Customer Onboarding¶

Canonical flow:

Application intake
Customer classification
Data capture
Document capture
Identity validation
UBO identification
Name screening
Customer risk rating
Network analysis (conditional)
EDD branching (conditional)
Analyst review
Compliance escalation (conditional)
Approval / rejection
Account activation
Audit closure

Variants:

Retail
SME
Corporate
Correspondent banking
Leasing
Wealth
Private banking

Configurable per institution.

Periodic Review¶

Flow:

review trigger
KYC refresh
sanctions refresh
risk recalculation
behavior review
analyst review
remediation / closure

Event Driven Review¶

Triggers:

sanctions changes
ownership changes
high-risk transaction
adverse media
law enforcement request
policy triggers

Exit / Offboarding¶

Flow:

trigger
legal review
compliance approval
notification
closure
retention

3.2 Screening Processes¶

Name Screening¶

Process:

match creation
auto disambiguation
analyst triage
evidence enrichment
escalation
decision
closure

Transaction Screening¶

Real-time process:

payment event
sanctions screening
hold / release
analyst review
escalation
disposition

3.3 Monitoring Processes¶

Transaction Monitoring¶

Flow:

event ingestion
scenario evaluation
alert generation
deduplication
prioritization
routing
triage
investigation
escalation
SAR decision
closure

3.4 Investigation Processes¶

Generic case lifecycle:

intake
assignment
evidence aggregation
enrichment
timeline reconstruction
network exploration
decision
QA
closure

3.5 Regulatory Reporting¶

SAR / STR Process¶

Flow:

suspicion confirmation
narrative drafting
approval
filing
acknowledgement
amendment handling
archival retention

Chapter 4: Escalation Model¶

Escalation Levels¶

Level 0¶

Automated straight-through decisions.

Level 1¶

Analyst review.

Level 2¶

Senior investigator / specialist review.

Level 3¶

Financial Crime Compliance review.

Level 4¶

Regulatory reporting authority.

Level 5¶

Executive / legal escalation.

Escalation Triggers¶

Examples:

sanctions confirmed hit
high-risk jurisdiction
PEP + adverse media
ownership complexity threshold exceeded
missing mandatory documents
analyst uncertainty
transaction value threshold
repeated suspicious behavior
regulator request

Escalation Routing Logic¶

Routing based on:

business line
institution
jurisdiction
alert type
risk score
workload
analyst skill
urgency
SLA breach

Chapter 5: Decision Module Architecture¶

Canonical Module Contract¶

Every module must expose:

Inputs:

request context
entity data
case context
workflow metadata
supporting evidence

Outputs:

decision
score
status
rationale
explanation
audit metadata
version
confidence

Standard Modules¶

Customer Intelligence¶

identity verification
KYC completeness
UBO resolution
risk rating
adverse media

Screening Intelligence¶

name screening
sanctions screening
watchlist screening

Transaction Intelligence¶

monitoring
screening
anomaly detection

Network Intelligence¶

entity resolution
graph analytics
cluster detection

Investigation Support¶

timeline builder
evidence aggregation
narrative generation

Governance Modules¶

policy validator
approval engine
audit recorder

Chapter 6: Data Architecture¶

Canonical Entity Model¶

Core entities:

Customer¶

Attributes:

customer_id
customer_type
legal_name
aliases
DOB/incorporation_date
nationality
residence_country
tax_residency
occupation / industry
onboarding_date
lifecycle_status

Legal Entity¶

entity_id
registration_number
incorporation_country
legal_form
ownership_structure

Individual¶

person_id
identifiers
PEP flags
sanctions indicators

Account¶

account_id
product
currency
status

Transaction¶

transaction_id
amount
timestamp
counterparty
channel
geography

Relationship¶

source_entity
target_entity
relationship_type
effective_dates
confidence_score

Alert¶

alert_id
source_module
alert_type
severity
timestamps

Case¶

case_id
case_type
status
owner
SLA

Decision¶

decision_id
decision_type
actor
rationale

Workflow Instance¶

workflow_id
template_version
state

Task¶

task_id
assignee
due_date

Document¶

document_id
type
storage_ref

Regulatory Filing¶

filing_id
jurisdiction
status

Chapter 7: Configuration Architecture¶

Configurable without code:

workflows
stages
routing
thresholds
questionnaires
approval matrices
module invocation
case schemas
document requirements
escalation policies
SLA rules
regulatory mappings

Governance requirements:

versioning
approval workflow
promotion pipeline
rollback
audit trail
impact analysis

Chapter 8: Compliance & Regulatory Requirements¶

Framework expectations:

AML/CTF
sanctions compliance
KYC/CDD/EDD
suspicious activity reporting
auditability
explainability
record retention
access control
privacy

Capabilities required:

full traceability
reproducibility
evidence preservation
immutable logs
segregation of duties
dual approval controls
jurisdictional flexibility

Chapter 9: Security Architecture¶

Requirements:

RBAC
ABAC
MFA
encryption at rest
encryption in transit
secrets management
session controls
privileged access monitoring
audit logs
data masking
environment segregation

Chapter 10: AI / ML Architecture¶

Requirements:

model registry
model versioning
approval workflow
explainability
threshold governance
drift monitoring
champion/challenger
human review gates

LLM controls:

prompt logging
hallucination controls
approval gating
data isolation

Chapter 11: Case Management UX¶

Analyst workspace must provide:

single-pane investigation view
customer profile
alert summary
network visualization
transaction timeline
evidence panel
notes
decisions
escalation actions

Chapter 12: Technical Architecture¶

Suggested components:

web frontend
API gateway
workflow engine
rules engine
orchestration service
decision gateway
case management service
document service
graph service
audit service
configuration service
auth service
reporting service
event bus
relational DB
graph DB
object storage
search engine

Chapter 13: Non Functional Requirements¶

Performance:

low latency
scalable throughput

Reliability:

HA
failover
recovery

Observability:

logs
metrics
tracing

Maintainability:

modular deployment
versioning
testing

Usability:

low click count
intuitive workflows

Chapter 14: MVP Recommendation¶

Phase 1 MVP:

onboarding workflow
name screening
customer risk rating
basic network analysis
case management
audit trail
configuration engine
RBAC

Chapter 15: Future Expansion¶

Future modules:

transaction monitoring
transaction screening
full SAR automation
fraud
advanced graph intelligence
AI copilots
adverse media NLP
model governance suite
enterprise MI dashboards

Final Architectural Principle¶

The platform must be:

Configurable, composable, explainable, auditable, modular, regulator-ready, and institution-agnostic.