Worked case · Reference condition · 12 figures

Missing signal states

Preserve missingness and quality

Figure 01 / 12

The population that monitoring actually sees

The population that monitoring actually sees — Missing signal states. Count; feature reads in one day. Exact values are in the figure data below.
Count; feature reads in one day

13,916 of 14,200 source items enter this monitoring calculation. The missing 284 items are a coverage gap, not evidence of low risk. Reconcile stable identifiers and amounts where appropriate before interpreting the alert rate.

Figure data and text version
Population stateItems
Included in monitoring13,916
Absent from this run284

A zero count, an absent record, a stale response, and a failed source are distinct observations. Their operational meaning depends on the feature contract.

The reference case starts with the stated population and a functioning evidence path. The owner is risk operations.

All amounts, rates, capacity limits, and outcomes in this case are synthetic. The three conditions are separate assumptions for comparison. A better result in the response condition is not measured proof that the proposed control causes that improvement. The figures expose the calculation and its limits; a real deployment needs its own evidence.

Read the result

The daily source population is 14,200 items, but 284 are outside the completed monitoring run. The included population creates 250 hits and 205 unique cases. With 17 cases already open and capacity for 240, the queue closes at 0. Coverage, duplicate work, and staffing are separate causes; reducing one number does not prove that the overall control improved.

Model inputs and calculated values

Inputs below are the case-specific values. Each figure states the condition-specific assumptions and units used in its calculation. Calculated values are rounded for display.

InputValue
population14,200
alertRate0.018
capacity240
backlog17
Calculated valueResult
population14,200
covered13,916
missing284
raw250
duplicates45
cases205
opening17
resolved222
closing0
capacity240
Figure 02 / 12

From scenario hits to unique cases

From scenario hits to unique cases — Missing signal states. Count; hit and case units differ. Exact values are in the figure data below.
Count; hit and case units differ

250 raw hits become 205 cases after removing 45 repeated references to the same case under the stated merge rule. Deduplication should reduce duplicate work while retaining the underlying events and reasons. It must not merge unrelated activity merely because values look similar.

Figure data and text version
StageCount
Raw scenario hits250
Duplicate references45
Unique cases205
Figure 03 / 12

The queue balance is an accounting identity

The queue balance is an accounting identity — Missing signal states. Cases per day. Exact values are in the figure data below.
Cases per day

Opening backlog 17 + arrivals 205 − completed cases 222 = closing backlog 0. Completion is capped by both available work and the stated capacity. This identity is useful even when average handling times are uncertain.

Figure data and text version
MovementCasesDefinition
Opening backlog17Unresolved at window start
New cases205Unique arrivals in this window
Completed222Reached a defined completion state
Closing backlog0Unresolved at window end
Figure 04 / 12

Backlog accumulates across uneven days

Backlog accumulates across uneven days — Missing signal states. Cases unresolved at day end. Exact values are in the figure data below.
Cases unresolved at day end

This deterministic six-day example applies a stated daily arrival multiplier and a constant completion capacity. Unused capacity does not carry forward as completed work. The series is a workload illustration; it omits variable case durations and specialist routing. Horizontal positions are the labeled observations or scenarios; equal spacing does not imply equal numerical increments.

Figure data and text version
DayClosing backlog
D10
D20
D36
D474
D560
D60
Figure 05 / 12

Arrivals and completions need separate plots

Arrivals and completions need separate plots — Missing signal states. Cases per day. Exact values are in the figure data below.
Cases per day

The service line cannot exceed the work available that day. A team can complete more cases than arrive while clearing an opening backlog. Conversely, stable staffing can coexist with a growing queue when arrivals remain higher than capacity. Horizontal positions are the labeled observations or scenarios; equal spacing does not imply equal numerical increments.

Figure data and text version
DayArrivalsCompletions
D1205222
D2174174
D3246240
D4308240
D5226240
D6144204
Figure 06 / 12

An illustrative age profile of open work

An illustrative age profile of open work — Missing signal states. Cases; constructed snapshot. Exact values are in the figure data below.
Cases; constructed snapshot

The closing backlog is 0 cases. The 50/30/remainder split is an explicit illustrative age allocation, not a distribution inferred from the arrival model. Production age buckets must come from each case’s actual receipt and status history.

Figure data and text version
Age bucketOpen cases
Under one day0
One to three days0
Over three days0
Figure 07 / 12

Completion outcomes are not criminal labels

Completion outcomes are not criminal labels — Missing signal states. Completed review tasks; synthetic disposition allocation. Exact values are in the figure data below.
Completed review tasks; synthetic disposition allocation

Of 222 completed review tasks, 38 are referred for further assessment, 27 need another evidence action, and 157 close under the stated procedure. These are operational outcomes. None is a probability of money laundering or a substitute for a reporting decision.

Figure data and text version
Review dispositionTasks
Referred for assessment38
Further evidence action27
Closed under procedure157
Figure 08 / 12

Known-event tests assess specific coverage

Known-event tests assess specific coverage — Missing signal states. Synthetic coverage test. Exact values are in the figure data below.
Synthetic coverage test

The injected test set contains 100 known test events designed to exercise preserve missingness and quality. The system surfaces 92. That 92% detection result measures this constructed test set only; it does not establish population-wide detection of illicit activity.

Figure data and text version
MeasureCountInterpretation
Injected known events100Defined test population
Detected by the scenario92Expected evidence reached the control
Not detected8Investigate data, logic, and delivery
Real-world illicit prevalenceUnknownNot inferred from this test
Figure 09 / 12

Data quality has several independent dimensions

Data quality has several independent dimensions — Missing signal states. Count; overlapping field checks. Exact values are in the figure data below.
Count; overlapping field checks

Each row is a different field requirement over the same source population. Completeness alone does not establish that values are accurate or current. The case uses explicit illustrative missing counts to show how data quality can affect scenario coverage.

Figure data and text version
Field requirementPresentAbsent
Party reference14,12971
Event time14,058142
Counterparty context13,774426
Figure 10 / 12

Type the relationship before drawing an inference

Type the relationship before drawing an inference — Missing signal states. Typed evidence relationships. Exact values are in the figure data below.
Typed evidence relationships

This evidence map distinguishes a customer relationship, a transfer, and a case reference. The links support preserve missingness and quality; they do not imply common ownership or intent. A shared data point is a lead whose meaning depends on source, time, and context.

Figure data and text version
FromToRelationship
Missing signal statesCounterparty AObserved transfer
Missing signal statesProfile recordDeclared business
Counterparty ACase recordEvidence reference
Profile recordCase recordContext for review
Figure 11 / 12

Case clocks start from defined events

Case clocks start from defined events — Missing signal states. Illustrative internal timing. Exact values are in the figure data below.
Illustrative internal timing

A legal deadline, an internal response target, and an evidence-expiry date can start from different events. The hours here are internal teaching targets only. They are not BSA, sanctions, consumer-protection, or other statutory deadlines.

Figure data and text version
EventRelative timeOperational meaning
Source eventT0Activity occurred
Data arrivalT0 + 2 hoursThe monitoring system learned it
Case createdT0 + 3 hoursWork entered an owned queue
Internal review targetT0 + 27 hoursIllustrative 24-hour target from case creation
DispositionRecorded separatelyUse actual decision and reporting records
Figure 12 / 12

The end-to-end delivery contract

The end-to-end delivery contract — Missing signal states. Operational control path. Exact values are in the figure data below.
Operational control path

The scenario is incomplete until the intended evidence reaches an owned case. For preserve missingness and quality, verify source coverage, hit creation, queue acceptance, reviewer access, and final disposition separately. A green job status proves only that a job reported completion.

Figure data and text version
BoundaryAcceptance evidence
Source to scenario13916 included source items; 284 missing
Scenario to case250 hits linked to 205 unique cases
Case to reviewerRequired evidence visible under the reviewer role
Reviewer to outcomeDisposition, rationale, and any separate reporting decision retained

Connect the result to the system

Retain explicit quality states and route incomplete evidence through an approved policy.

Check the population, evidence, permitted action, and actual effect together. A balanced calculation can still use the wrong population; a successful response can still leave an unknown financial outcome. The case’s numerical result applies only to its stated assumptions.

Sources and further reading

The chapter sources support the concepts and scope. They do not prescribe the synthetic model rates.

  1. PostgreSQL: transaction isolation
  2. scikit-learn: model evaluation metrics