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1 INTRODUCTION

Global shipping is a foundation of international trade

and supply chain continuity[1,2]. However, maritime

transport is exposed to a wide range of security threats,

including piracy and armed robbery against ships[3–

5]. These incidents can involve boarding, attempted

attack, hijacking, theft, kidnapping, physical violence,

or threat of violence against crew members[6]. The

consequences of piracy are not limited to immediate

security losses[7]. They may also include rerouting,

increased fuel consumption, higher insurance

premiums, delays, additional security measures,

psychological harm to seafarers, disruption of charter

schedules, and legal uncertainty[7–13].

From the perspective of ship operators, piracy risk

is not an abstract security category but a practical

voyage-planning problem[14–17]. A decision to

maintain a route, increase speed, introduce additional

watchkeeping, embark security personnel, or consider

rerouting has direct consequences for cost, safety,

contractual performance, and crew welfare[18–20].

Therefore, piracy risk assessment should not be limited

to counting incidents. It should also consider how

exposed and vulnerable a particular vessel or route is,

and what consequences may follow if an incident

occurs.

The contemporary piracy threat is geographically

uneven[21]. Reported incidents tend to concentrate in

specific maritime areas, including parts of Southeast

Asia, the Malacca and Singapore Straits, the Gulf of

Guinea, and waters near Somalia and the Gulf of

Aden[22,23]. The spatial concentration of incidents

means that piracy risk should not be assessed only at a

global level. Instead, it should be evaluated by route,

region, vessel type, operational profile, and exposure

time.

A purely descriptive approach is insufficient for

decision-making because ship operators need to

compare alternative routes and mitigation strategies.

At the same time, a fully probabilistic model is difficult

to develop because piracy data are incomplete,

A Multi-Criteria Model for Assessing Maritime Piracy

Risk in Global Shipping

S. Rozbiewska

Maritime University of Szczecin, Szczecin, Poland

ABSTRACT: Maritime piracy and armed robbery remain significant threats to global shipping, affecting vessel

safety, crew welfare, routing, insurance costs, and supply chain reliability. This paper proposes a semi-

quantitative multi-criteria framework for assessing piracy risk. The model combines incident probability, vessel

or route vulnerability, and weighted consequences, including crew safety, operational disruption, economic loss,

legal complexity, and supply chain impact. Using selected piracy-prone regions and hypothetical route scenarios,

the framework shows that risk is highest where frequent incidents coincide with high vulnerability and severe

consequences. It offers a transparent, adaptable tool for operators, insurers, security professionals, and

policymakers.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 20

Number 3

September 2026

DOI: 10.12716/1001.20.03.09

612

underreporting may occur, and incident severity varies

significantly. Therefore, this paper pro-poses a semi-

quantitative multi-criteria framework that combines

available incident in-formation with structured expert

assessment.

Despite the availability of piracy incident reports

and maritime security advisories, many operational

decisions still rely on qualitative judgement rather than

transparent comparative assessment. Existing

information sources are useful for identifying piracy-

prone regions, but they do not always provide a

structured method for comparing routes, vessel

exposure, vulnerability, and consequences within one

decision-support framework. This creates a practical

research gap between incident reporting and voyage-

level risk assessment. The present study addresses this

gap by proposing a simple, transparent, and adaptable

multi-criteria model that can be used when precise

statistical probabilities are unavailable or uncertain.

The aim of this paper is to develop an

interdisciplinary risk framework for assessing

maritime piracy risk in global shipping. The

framework integrates probability, vulnerability, and

weighted consequences in order to reflect the multi-

dimensional nature of piracy risk. The research

questions are as follows:

1. What factors should be included in a structured

assessment of maritime piracy risk?

2. How can probability, vessel vulnerability, and

consequences be integrated into a semi-quantitative

model?

3. How can the proposed model support risk-

informed decision-making in global ship-ping?

2 MARITIME PIRACY AS AN

INTERDISCIPLINARY SHIPPING RISK

Maritime piracy is both a security threat and an

operational risk[3]. It affects vessel movement, crew

safety, cargo delivery, insurance arrangements, and

commercial planning[24]. From a maritime security

perspective, piracy is associated with hostile acts

against ships and crews[24]. From an operational

perspective, it may require route deviation, increased

speed, additional watchkeeping, hardening measures,

or the employment of private maritime security

personnel where legally permitted[24]. From an

economic perspective, piracy generates direct and

indirect costs, including delays, ransom risk, security

expenses, higher insurance premiums, and disruption

of contractual obligations[1,8,9,12,19,24].

The legal dimension is also important. Piracy and

armed robbery against ships may fall under different

legal definitions depending on whether the incident

occurs on the high seas, in territorial waters, or in port

or anchorage areas[25–28]. This distinction affects

jurisdiction, reporting procedures, prosecution, and

the responsibilities of coastal states and flag states[29].

Consequently, a meaningful risk assessment must

consider more than the number of incidents. It must

also include exposure, vulnerability, severity, legal

context, and potential impact on maritime supply

chains.

An interdisciplinary approach is therefore

necessary because piracy risk is not produced by one

factor alone. It emerges from the interaction between

external threat conditions and internal vessel

characteristics. For example, the same piracy-prone

area may generate different risk levels for a slow-

moving bulk carrier, a high-freeboard container vessel,

an offshore support vessel, or a tanker carrying high-

value cargo. Similarly, the impact of a piracy incident

may differ depending on crew size, cargo type, charter

obligations, insurance conditions, and the availability

of naval or coastal response.

Figure 1 illustrates the three overlapping

dimensions of maritime piracy risk. The security

dimension encompasses threats to crew safety,

violence, hostage-taking, and armed robbery. The

operational dimension covers voyage disruption,

rerouting, speed management, and charter obligations.

The economic and legal dimension includes insurance

costs, ransom exposure, jurisdictional complexity, and

reporting requirements. The inter-sections between

dimensions reflect the compound nature of piracy risk:

a single incident may simultaneously generate

security, operational, and economic consequences. The

centre of the diagram represents the integrated piracy

risk concept that the proposed frame-work seeks to

capture.

Figure 1. Interdisciplinary dimensions of maritime piracy

risk: security, operational, and economic and legal

perspectives. Author's own elaboration.

3 MATERIALS AND METHODS

3.1 Research Design

This study develops a semi-quantitative multi-criteria

risk assessment framework. The method is designed

for comparative assessment of piracy risk across

selected routes, regions, or vessel scenarios. It does not

attempt to predict individual pirate attacks. In-stead, it

provides a structured scoring model that can support

decision-making where precise probabilities are

unavailable or uncertain.

The proposed model is not intended to replace

official threat intelligence, company security

procedures, or real-time voyage risk assessment.

Instead, it is designed as a pre-liminary comparative

tool that helps organise available information into a

consistent structure. Its value lies in making

assumptions explicit: the assessor must define the

probability level, vessel or route vulnerability,

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consequence scores, and criterion weights. This

improves transparency and allows different scenarios

to be compared under the same methodological logic.

The framework is based on three main components:

− piracy incident probability,

− vessel or route vulnerability,

− weighted consequences.

The model can be applied to geographical regions,

specific voyage legs, or alternative routing options. It is

particularly suitable for preliminary risk screening,

voyage planning, and comparative risk evaluation.

3.2 General Risk Model

The basic model follows the classical understanding of

risk as the interaction between probability and impact.

In this paper, impact is expanded into an

interdisciplinary weighted consequence index, while

vulnerability is introduced as a separate modifying

factor.

The overall piracy risk score for route or region i is

calculated as:

i i i j ij

R P V w C

=  



(1)

where:

Ri — piracy risk score for route or region i,

Pi — piracy incident probability score for route or

region i,

Vi — vessel or route vulnerability score for route or

region i,

Cij — consequence score for criterion j in route or

region i,

wj — weight assigned to consequence criterion j,

m — number of consequence criteria.

The consequence index is defined as:

i j ij

I w C



(2)

Thus, the complete model can also be written as:

i i i i

R P V I=  

(3)

where Ii represents the weighted consequence index.

3.3 Scoring Scale

All input variables are assessed using a five-point

ordinal scale. The scale is intentionally simple to

improve transparency and usability in maritime

decision-making contexts.

Table 1. Five-point ordinal scoring scale used in the

proposed piracy risk assessment framework. Author’s own

elaboration.

Score

Interpretation

Very low

Low

Moderate

High

Very high

The five-point scale was selected because it is

sufficiently simple for practical use while still allowing

differentiation between low, moderate, high, and very

high risk conditions. In maritime operations, decision-

makers often work with incomplete or rapidly

changing information. For this reason, an ordinal scale

may be more realistic than a purely numerical

probability estimate. The scale also allows expert

judgement to be combined with reported incident

patterns, regional advisories, vessel characteristics,

and operational exposure.

The probability score Pi reflects the relative

likelihood of piracy or armed robbery incidents in a

given region or route segment. It may be estimated

using incident reports, regional security advisories,

historical patterns, and current threat intelligence.

The vulnerability score Vi reflects vessel and route

characteristics that influence exposure and

susceptibility. Relevant factors may include vessel

speed, freeboard, type of cargo, time spent in high-risk

areas, distance from shore, watchkeeping

arrangements, anti-piracy measures, and availability of

response forces.

3.4 Consequence Criteria and Weights

Five consequence criteria are proposed to reflect the

interdisciplinary nature of piracy risk. The weights are

normalised so that their sum equals 1.

Table 2. Consequence criteria and normalised weights used

in the interdisciplinary piracy risk model. Author’s own

elaboration.

Symbol

Criterion

Description

Weight

Crew safety impact

Threat to life, injury,

kidnapping, trauma, hostage-

taking

0.30

Operational

disruption

Delay, route deviation,

interruption of voyage, loss of

schedule reliability

0.20

Economic loss

Increased insurance, fuel cost,

security cost, cargo loss, ransom

exposure

0.20

Legal/regulatory

complexity

Jurisdictional issues, reporting

obligations, compliance,

prosecution barriers

0.10

Supply chain

impact

Disruption of cargo flow, port

delays, contractual

consequences

0.20

Crew safety receives the highest weight because

piracy directly threatens seafarers and may involve

violence, hostage-taking, or kidnapping. Operational,

economic, and supply chain impacts receive equal

weights because these dimensions are central to

shipping continuity. Legal and regulatory complexity

is assigned a lower, but still relevant, weight because it

influences incident response and post-incident

management.

The weights can be modified depending on the

decision-making context. For example, an insurer may

assign higher weight to economic loss, while a ship

operator may assign higher weight to crew safety and

operational disruption.

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3.5 Risk Classification

The maximum possible risk score is obtained when

Pi = 5, Vi = 5, and Ii = 5, giving a maximum value of 125.

The following classification is proposed:

Table 3. Proposed classification of piracy risk scores.

Author’s own elaboration.

Risk

score

Risk

level

Interpretation

0–20

Low

Routine monitoring is sufficient

21–40

Moderate

Additional voyage planning and monitoring

required

41–60

High

Risk mitigation measures should be implemented

61–125

Critical

Strong mitigation, rerouting, or enhanced security

measures should be considered

This classification is illustrative and may be

adjusted according to company risk appetite, flag-state

requirements, insurance conditions, or route-specific

security advisories.

4 RESULTS

4.1 Demonstration of the Framework

To demonstrate the proposed model, four illustrative

route or region scenarios are assessed. The scores are

not intended to replace real-time maritime security

intelligence. They provide a methodological

demonstration of how the framework may be applied.

Figure 2 presents a visual summary of the four

illustrative scenarios used to demonstrate the

framework. Each card shows the probability score,

vulnerability score, individual consequence ratings

across criteria C1–C5, the weighted consequence index,

and the final risk score. The colour coding reflects the

assigned risk level: green for low, yellow for moderate,

orange for high, and purple for critical. The cards allow

rapid comparison of scenario profiles and highlight

how differences in input scores translate into divergent

risk classifications.

Table 4. Illustrative scoring of piracy risk variables for

selected route or region scenarios. Author’s own

elaboration.

Scenario

Region / route type

Low-exposure open ocean route

Port/anchorage area with recurrent

robbery incidents

High-density strait with frequent

boarding risk

High-risk piracy area with hijacking

potential

The weighted consequence index is calculated for

each scenario:

1 2 3 4 5

0.30 0.20 0.20 0.10 0.20

I C C C C C= + + + +

Table 5. Calculated weighted consequence indices, piracy

risk scores, and risk levels for the illustrative scenarios.

Author’s own elaboration.

Scenario

Weighted index Ii

Risk score Ri

Risk level

1.90

3.80

Low

2.90

26.10

Moderate

3.70

44.40

High

4.90

78.40

Critical

Figure 3 presents the calculated piracy risk scores

for scenarios A–D in graphical form. The bar chart

confirms the progressive increase in risk across

scenarios, with Scenario A classified as low risk and

Scenario D reaching the critical category. The colour

coding reflects the risk level assigned in Table 3. The

chart illustrates that risk escalates non-linearly: the

difference between Scenario A and Scenario B is

substantially smaller than the difference between

Scenario C and Scenario D, reflecting the multiplicative

structure of the model in which simultaneous increases

in probability, vulnerability, and consequences

produce a compounding effect on the final score.

Figure 2. Risk profile cards for illustrative scenarios A–D, summarising probability, vulnerability, and consequence scores.

Author's own elaboration.

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Figure 3. Piracy risk scores for illustrative scenarios A–D.

Author's own elaboration.

Figure 4 presents the consequence profiles for each

scenario across the five weighted criteria. The radar

chart illustrates that scenarios differ not only in their

overall risk score, but also in the distribution of

consequences across dimensions. Scenario D shows the

highest values across all criteria, with particular

severity in crew safety and supply chain impact.

Scenario A remains consistently low across all

dimensions. Scenarios B and C differ primarily in

operational, economic, and supply chain scores,

reflecting the distinction between port-area robbery

and route-exposure risk.

Figure 4. Consequence profiles for scenarios A–D across five

risk criteria. Author's own elaboration.

4.2 Interpretation of Results

The results show that piracy risk increases significantly

when probability and vulnerability are both elevated.

Scenario A has a low probability score and limited

vulnerability, resulting in a low overall risk score

despite non-zero consequences.

Scenario B represents recurrent robbery risk in port

or anchorage areas. The risk level is moderate because

the probability and vulnerability are higher, but the

expected consequences are not as severe as in

hijacking-prone scenarios.

Scenario C illustrates a high-density maritime route

where boarding incidents may occur more frequently.

Even when crew safety consequences are assessed as

moderate rather than very high, the combined

operational, economic, and supply chain impacts

increase the overall risk score to the high category. This

reflects the importance of route exposure and shipping

density in piracy risk assessment.

Scenario D produces the highest score because it

combines high probability, high vulnerability, and

severe consequences. In this case, the possibility of

hijacking, kidnapping, violence, major operational

disruption, and supply chain effects results in a critical

risk classification. Such a scenario would require

strong mitigation measures, including enhanced

voyage planning, implementation of best management

practices, route evaluation, crew preparedness, and

potentially additional security measures depending on

legal and operational conditions.

Figure 5 presents a heatmap of the weighted

consequence scores across all scenarios and criteria.

Darker shading indicates a higher weighted

contribution to the overall consequence index. The

chart confirms that Scenario D generates the highest

weighted scores across all five criteria, with crew safety

(C1) and supply chain impact (C5) producing the

largest individual contributions. In contrast, legal and

regulatory complexity (C4) produces the lowest

weighted scores across all scenarios due to its lower

assigned weight. The heatmap also illustrates that

Scenarios B and C differ most markedly in their

operational and economic consequence scores, which

reflects the distinction between port-area robbery risk

and route-exposure risk described in Section 4.1.

Figure 5. Weighted consequence scores (wj × Cij) for

scenarios A–D across five risk criteria. Author's own

elaboration.

The comparison also shows that high incident

probability alone does not fully determine the final risk

level. A region with recurrent low-severity robberies

may produce a lower overall score than a less frequent

but more severe piracy environment. This distinction is

important because maritime decision-making must

consider both frequency and consequence severity. In

practice, a route with moderate probability but high

potential for crew harm, hijacking, or prolonged

operational disruption may require stronger mitigation

than a route with more frequent but lower-impact

incidents.

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4.3 Sensitivity of the Model

A key advantage of the proposed framework is that it

allows sensitivity analysis. Since the final score is

multiplicative, changes in probability or vulnerability

can significantly affect the total risk score. For example,

if vessel vulnerability in Scenario D is reduced from 4

to 3 through improved security measures, the risk

score decreases from:

4 4 4.90 78.40

R =   =

to:

4 3 4.90 58.80

R =   =

This moves the scenario from the critical category to

the high category. Figure 6 illustrates the sensitivity of

the risk score to changes in vessel vulnerability. The left

panel shows that reducing the vulnerability score in

Scenario D from 5 to 1 progressively lowers the risk

level from critical to low, even when probability and

consequence scores remain unchanged. The right panel

confirms this pattern across all scenarios: vulnerability

reduction has the greatest absolute effect in high-

probability, high-consequence environments such as

Scenario D, while its impact in low-exposure settings

such as Scenario A remains limited.

Figure 6. Sensitivity analysis: impact of vulnerability score on

piracy risk score across illustrative scenarios. Author's own

elaboration.

The example demonstrates that vulnerability

reduction can be an effective risk mitigation strategy

even when external threat probability remains

unchanged. In practical terms, this may include

improved watchkeeping, citadel readiness, razor wire,

water spray, speed management, route planning,

secure communications, and crew drills.

This sensitivity effect is relevant for maritime

security planning because vulnerability is one of the

few components that ship operators can directly

influence. While regional piracy probability depends

on wider political, economic, and security conditions,

vessel vulnerability can be reduced through voyage

planning, crew training, physical protection measures,

communication procedures, speed management, and

compliance with best management practices. The

model therefore links assessment with action: it does

not only classify risk, but also helps identify where

mitigation can reduce the final score.

5 DISCUSSION

The main contribution of this paper is the integration

of three dimensions that are often considered

separately in piracy-related decision-making: regional

threat probability, vessel or route vulnerability, and

interdisciplinary consequences. By combining these

elements in one semi-quantitative framework, the

model provides a practical bridge between descriptive

piracy reporting and operational risk assessment. This

is particularly useful in situations where complete

statistical data are unavailable, but decisions still have

to be made under uncertainty.

The proposed framework supports an

interdisciplinary understanding of piracy risk. Instead

of treating piracy as a single security variable, the

model decomposes risk into probability, vulnerability,

and multiple consequence dimensions. This structure

reflects the operational reality of global shipping,

where the same piracy event may generate different

impacts depending on vessel type, route, cargo, crew

exposure, and contractual obligations.

The model has several practical advantages. First, it

is transparent. Each score and weight can be reviewed,

discussed, and adjusted by maritime experts. Second,

it is flexible. The framework can be applied to different

vessel types, regions, or route alternatives. Third, it

supports comparative decision-making. Ship operators

may compare risk levels for alternative routes or assess

how mitigation measures reduce vulnerability. The

model also highlights the importance of vulnerability

as a controllable factor. External piracy probability is

usually beyond the control of individual ship

operators. However, vulnerability can be reduced

through operational and technical measures.

Therefore, the framework can help distinguish

between risks that require strategic response, such as

rerouting or insurance review, and risks that can be

mitigated through onboard preparedness and voyage

planning.

However, the numerical nature of the model

requires careful interpretation. A key challenge in

using this type of model is the risk of false precision.

Although the final score is numerical, it should not be

interpreted as an exact measurement of piracy risk.

Rather, it is a structured representation of expert

judgement supported by available information. The

purpose of the score is comparative: to identify

whether one route, vessel profile, or mitigation option

appears more exposed than another. Therefore, the

model should be used as a decision-support tool, not as

a deterministic prediction of piracy incidents.

Nevertheless, the framework has limitations. The

use of ordinal scoring introduces subjectivity. Different

assessors may assign different probability,

vulnerability, or consequence values. The proposed

weights are also illustrative and should be validated

through expert consultation, surveys, or analytic

hierarchy process methods in future research.

Moreover, piracy data may be affected by

underreporting, inconsistent classification, and rapidly

changing regional security conditions. Therefore, the

model should be used together with current security

advisories and official reporting sources, not as a

standalone prediction tool.

Despite these limitations, the framework provides a

useful methodological basis for further research and

practical risk screening. Future research should focus

on empirical validation of the framework. This may

include expert surveys among ship operators,

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maritime security professionals, insurers, and

seafarers, as well as comparison with historical piracy

incident datasets. The model could also be extended by

incorporating AIS-based exposure measures, vessel-

specific parameters, seasonal variation, regional

response capacity, fuzzy logic or Monte Carlo

simulation, to better represent uncertainty. Such

developments would allow the framework to move

from an illustrative model towards a more operational

risk assessment tool.

6 CONCLUSIONS

This paper proposed a semi-quantitative multi-criteria

framework for assessing maritime piracy risk in global

shipping. The model combines piracy incident

probability, vessel or route vulnerability, and a

weighted interdisciplinary consequence index. The

framework reflects the fact that piracy is not only a

security issue but also an operational, economic, legal,

human, and supply chain risk.

The demonstration showed that the highest risk

levels occur when high incident probability overlaps

with vessel vulnerability and severe consequences. The

model also showed that reducing vulnerability may

significantly lower the overall risk score, even when

regional threat probability remains unchanged. This

finding is important for ship operators because it links

risk assessment directly with mitigation planning.

The proposed framework may support voyage

planning, maritime security assessment, insurance

analysis, and policy discussions. Future research

should validate the model using expert surveys, real

incident datasets, route-specific exposure indicators,

and sensitivity analysis of weighting schemes.

ABBREVIATIONS

AIS Automatic Identification System

BMP Best Management Practices

ICC International Chamber of Commerce

IMB International Maritime Bureau

IMO International Maritime Organization

UNCLOS United Nations Convention on the Law of the

Sea

UNCTAD United Nations Conference on Trade and

Development

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