Why My First End‑to‑End Case Management Workflow Collapsed in Dhaka – The 9 Fixes That Saved My AML Team
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High‑Stakes Hook
It was 02:17 am on a rainy Tuesday. My phone buzzed with a red badge: “STR # B‑2026‑0147 – BDT 9.8 million structured over 48 hours.” The alert came from the MFS monitoring engine at bKash, but the case file was empty, the audit log showed a dead‑end, and the senior compliance officer was already on a conference call with the BFIU.
Within minutes the whole desk was on fire. The senior analyst shouted, “Where are the supporting documents? Why is the workflow stuck?” The system had thrown a “case‑state‑transition error” after the third escalation, and every analyst downstream was staring at a blank screen. The regulator was breathing down our necks, and the potential penalty for a missed SAR could be BDT 5 million plus reputational damage.
The Hidden Problem
Most Bangladeshi fintechs build a case management layer on top of an off‑the‑shelf ticketing system. They assume the generic “open → assign → resolve” flow will catch everything. In practice, the BFIU’s Guidelines on SAR Filing (2023) demand:
- Three distinct review tiers (analyst, senior analyst, compliance manager).
- Automatic escalation after 24 hours of inactivity.
- Evidence‑attachment validation before final filing.
Our legacy workflow ignored two of those mandates. It let a case sit in “Pending Review” forever if the analyst never clicked “Save”. It also let duplicate alerts slip through because the deduplication step was a simple hash of customer_id+timestamp, which fails when the same customer splits the amount across multiple wallets.
Rule 12 of the BFIU SAR guidelines: “All alerts must be linked to a unique case identifier; duplicate cases shall be merged before escalation.”
The result? A mountain of orphaned alerts, missed deadlines, and a regulator who could smell the stench of non‑compliance from miles away.
Technical Breakdown & Logic Flow
We rewrote the workflow from the ground up. The new design follows a state‑machine pattern with explicit transitions and guard clauses. Each transition emits an event that a Kafka topic picks up, feeding a lightweight Python microservice that enforces BFIU rules.
Key ideas:
- Deterministic case IDs. Use a UUIDv5 derived from a canonical string of
customer_id+alert_hash+date. Guarantees the same case ID for duplicate alerts. - Three‑tier review queue. A PostgreSQL table
case_stateholds the current tier and a timestamp of the last action. - Auto‑escalation daemon. Runs every 5 minutes, checks for
now() - last_action > INTERVAL '24 hours', pushes the case to the next tier. - Attachment validator. A tiny FastAPI endpoint that inspects uploaded PDFs, checks for OCR‑readable text, and rejects empty files.
- Audit trail. Every state change writes a JSONB record to
case_auditwith user, timestamp, and diff.
Why not just stick a “while True” loop in the existing ticketing system? Because that approach locks the DB, creates race conditions, and makes it impossible to scale beyond a single node. The event‑driven model decouples the UI from the enforcement engine, letting us add new rules without touching the front‑end.
Python Implementation
Below is the core microservice that listens to the case.transition Kafka topic, validates the transition, and writes the new state. Notice the extensive inline comments – I wrote them for the junior dev who was still learning the BFIU rulebook.
import uuid, json, datetime
from kafka import KafkaConsumer, KafkaProducer
from sqlalchemy import create_engine, Table, MetaData, select, update
from sqlalchemy.dialects.postgresql import JSONB
# ---------------------------------------------------------------------
# Configuration – keep it in env vars in production, hard‑coded for demo.
# ---------------------------------------------------------------------
KAFKA_BROKERS = ['kafka1.bdfintech.local:9092']
CASE_TOPIC = 'case.transition'
PRODUCER_TOPIC = 'case.transition.result'
DB_URL = 'postgresql://aml_user:secret@db.bdfintech.local/aml'
engine = create_engine(DB_URL)
metadata = MetaData()
case_state = Table('case_state', metadata, autoload_with=engine)
case_audit = Table('case_audit', metadata, autoload_with=engine)
consumer = KafkaConsumer(
CASE_TOPIC,
bootstrap_servers=KAFKA_BROKERS,
value_deserializer=lambda m: json.loads(m.decode('utf-8')),
group_id='case_engine_group'
)
producer = KafkaProducer(
bootstrap_servers=KAFKA_BROKERS,
value_serializer=lambda m: json.dumps(m).encode('utf-8')
)
def deterministic_case_id(customer_id: str, alert_hash: str, date: str) -> str:
"""Generate a UUIDv5 based on a namespace and a canonical string.
This guarantees the same ID for duplicate alerts.
"""
namespace = uuid.NAMESPACE_URL
name = f"{customer_id}|{alert_hash}|{date}"
return str(uuid.uuid5(namespace, name))
def validate_transition(current_state: str, target_state: str, user_role: str) -> bool:
"""Enforce BFIU three‑tier rule and role permissions.
Returns True if the transition is allowed.
"""
tier_map = {
'analyst': ['pending_review', 'escalated'],
'senior_analyst': ['pending_review', 'escalated', 'ready_for_manager'],
'compliance_manager': ['ready_for_manager', 'approved', 'rejected']
}
# Simple guard: user must be allowed to move FROM current_state TO target_state
allowed = target_state in tier_map.get(user_role, [])
return allowed
def attach_validator(attachments: list) -> bool:
"""Very lightweight check – ensure each file has size > 0 and .pdf extension.
Real‑world would run OCR and checksum verification.
"""
for att in attachments:
if not att.get('filename', '').lower().endswith('.pdf'):
return False
if att.get('size', 0) <= 0:
return False
return True
for msg in consumer:
payload = msg.value
case_id = payload['case_id']
target_state = payload['target_state']
user = payload['user']
role = payload['role']
attachments = payload.get('attachments', [])
with engine.begin() as conn:
# Fetch current state atomically
cur = conn.execute(select([case_state.c.state, case_state.c.last_updated])
.where(case_state.c.id == case_id))
row = cur.fetchone()
if not row:
# New case – create entry with deterministic ID if not supplied
case_id = deterministic_case_id(payload['customer_id'], payload['alert_hash'], payload['date'])
conn.execute(case_state.insert().values(
id=case_id,
state='pending_review',
last_updated=datetime.datetime.utcnow()
))
current_state = 'pending_review'
else:
current_state = row.state
# Guard clauses
if not validate_transition(current_state, target_state, role):
producer.send(PRODUCER_TOPIC, {
'case_id': case_id,
'status': 'rejected',
'reason': f'Role {role} cannot move from {current_state} to {target_state}'
})
continue
if target_state in ('approved', 'rejected') and not attach_validator(attachments):
producer.send(PRODUCER_TOPIC, {
'case_id': case_id,
'status': 'rejected',
'reason': 'Attachment validation failed'
})
continue
# Perform state update
conn.execute(update(case_state)
.where(case_state.c.id == case_id)
.values(state=target_state, last_updated=datetime.datetime.utcnow()))
# Write audit log
conn.execute(case_audit.insert().values(
case_id=case_id,
event=json.dumps({
'user': user,
'role': role,
'from': current_state,
'to': target_state,
'timestamp': datetime.datetime.utcnow().isoformat()
}),
created_at=datetime.datetime.utcnow()
))
# Notify UI
producer.send(PRODUCER_TOPIC, {
'case_id': case_id,
'status': 'accepted',
'new_state': target_state
})
The service runs in a Docker container, auto‑restarts on failure, and logs to ELK. We deployed it on a Kubernetes node in the same VPC as the MFS data lake, keeping latency under 150 ms per transition.
Local Application
How does this fit the Bangladeshi regulatory puzzle?
- BFIU SAR filing window. The auto‑escalation daemon guarantees that no case stays longer than 24 hours without senior review, satisfying the “within 24 hours of detection” clause.
- BDT 100,000 MFS threshold. Our alert generator tags any transaction > BDT 100,000. The case engine automatically creates a case with the deterministic ID, merging any later alerts that share the same customer and day.
- Evidence attachment rule. The validator rejects empty PDFs, preventing the dreaded “no supporting docs” audit finding that once cost my bank BDT 1.2 million.
We also integrated the workflow with the BFIU’s API for pre‑screening of sanctioned entities. Before moving to the “ready_for_manager” state, the service calls /sanctions/check and blocks the transition if a hit is returned.
Common Pitfalls & Edge Cases
Even with a solid engine, reality bites.
1. Duplicate alerts across wallets
Customers often own both a bKash and a Rocket wallet. Our hash originally only looked at customer_id, which is wallet‑specific. The fix: map all known wallet IDs to a master customer reference from the KYC table before generating the case ID.
2. Midnight roll‑over
Our 24‑hour timer used UTC. Bangladesh is UTC+6, so a case created at 22:00 local time would auto‑escalate at 04:00 local, catching analysts off‑guard. Solution: store timestamps in local time zone and use INTERVAL '24 hours' AT TIME ZONE 'Asia/Dhaka' in the SQL query.
3. Attachment size limits
FastAPI’s default max payload is 100 MB. Some SARs include full transaction logs that exceed this. We raised the limit to 500 MB and added chunked upload support.
4. Kafka consumer rebalancing
During a cluster upgrade, the consumer group lost its offset and re‑processed old events, creating duplicate state changes. Adding a consumer.seek_to_end() on partition assignment solved the duplication.
Counterintuitive Insight
When we first rolled out the new workflow, we expected the false‑positive rate to drop dramatically. Instead, it rose by 12 % in the first week. Why? The stricter attachment validation forced analysts to re‑upload PDFs that were already in the system, creating duplicate “attachment‑added” events that the UI interpreted as new evidence, triggering a second SAR filing.
The fix was a tiny idempotency check: before creating a new “attachment” record, verify that the SHA‑256 hash of the file already exists for that case. After that, the false‑positive spike vanished.
Conclusion & CTA
Designing a case management workflow for Bangladeshi compliance teams is less about buying the flashiest tech stack and more about honoring the BFIU’s concrete rules, handling local wallet fragmentation, and building guardrails that survive midnight and rainstorms alike. The nine fixes above turned a collapsing system into a reliable, auditable pipeline that saved my bank BDT 3 million in potential fines.
If you’ve ever watched a case stall in limbo, or wrestled with duplicate alerts that make you want to pull your hair out, try swapping the generic ticketing engine for a state‑machine backed by events. Drop a comment below with the worst workflow nightmare you’ve faced, and let’s troubleshoot together.
Also, check out our AML playbooks for deeper dives into SAR filing automation and BFIU compliance checklists.
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