Assessing Money Laundering Risk of Financial Institutions with AHP: Supervisory Perspective

doi:10.4236/jfrm.2013.21004

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Journal of Financial Risk Management

2013. Vol.2, No.1, 29-31

Published Online March 2013 in SciRes (http://www.scirp.org/journal/jfrm) DOI:10.4236/jfrm.2013.21004

Assessing Money Laundering Risk of Financial Institutions with

AHP: Supervisory Perspective

Ke Jia1,2, Xi Zhao1, Ling Zhang3

1School of Management, Tianjin University, Tianjin, China

2Anti-Money Laundering Section, The People’s Bank of China, Tianjin Branch, Tianjin, China

3Human Resources Department, Tianjin University, Tianjin, China

Email: [email protected]

Received January 6th, 2013; r evised February 7th, 2013; accepted February 14th, 2013

This paper proposed a risk assessment model with which supervisory authorities can calculate the money

laundering risk (MLR) level of financial institutions and make comparisons among multiple institutions.

The model is based on the Analytic Hierarchy Process (AHP) and decomposes MLR into two second-tier

criteria, i.e. Inherent Risk & Control Risk. AHP pair wise comparisons made by the experts from various

fields are processed through AHP software to get the weight of each factor. Using this model, MLR of

each financial institution could be obtained and certain comparison among them could be carried out.

Keywords: Money Laundering Risk; Assessment; AHP

Introduction

Money laundering risk (MLR) is newly recognized as a serious

risk endangering the financial sector as well as the whole society ,

and is drawing increasing attention in recent decades on both

regulation and supervision. (Fer werda, Kattenberg, Chang, Unger,

Groot, & Bikker, 2013; Stoke s, 2012; Kishor & Lescuyer, 2012)

To appropriately apply the risk-based approach recommended in

International Standards on Combating Money Laundering and

the Financing of Terrorism & Proliferation by The Financial

Action Task For ce on Money Laundering ( FATF) and efficiently

allocate supervisory resources, national supervisory authorities

need to accurately assess the MLR levels of financial institutions.

MLR of an institution could be affecte d by many facto rs, in-

cluding institution size, internal rules, management attitude, and

so on. In China, the assessment of MLR are mostly carried out

by certain reviewers grouped with supervisors and specialists

simply giving marks considering some factors (Cai & Liu,

2011). However, as risk factors are distinct from each other in

their natures and weights (Wang & Yang, 2007), an overall ac-

curate assessment could not be obtained using this method,

consequently it is hard to make comparison within institutions.

Given that reviewers could not only raise the examining fac-

tors, but also point out the inherent relationship of these factors,

which could then be analysed using Analytic Hierarchy Process

(Saaty, 1990), the weight of each factors as well as reasonable

marks could be obtained.

Locating MLR factors and building-up MLR structure have

been challenging assessors and researchers in the worldwide in

that the elements composing MLR are complicated (IIROC,

2010). This research created a MLR assessment model which

enables reviewers to evaluate and compare the MLRs of finan-

cial institutions. The core task is to find the most significant

risk factors and establish a logical MLR assessing model.

Method

AHP theory was proposed by Thomas. L. Saaty in 1970s, by

which complex issues can be structured and analyzed by hier-

archical division, and subjective decision according to objective

conclusions would be made. In this analysis, AHP software

with version 0.5.2 was used to obtain the weights of index

automatically.

Decomposing MLR into a Hierarchy of Factors

In reality, during the process of assessment, supervisory and

management department always divide MLR (A) of a financial

institution into two components, Inherent Risk and Control

Risk, which could be deemed B1 and B2, respectively, as the

second level of this AHP model.

The hierarchical structure of MLR is shown as Figure 1.

Figure 1.

MLR structure.

K. JIA ET AL.

Inherent Risk (IR, B1)

IR is the susceptibility of a financial institution to money

laundering occurred given inherent and environmental charac-

teristics, but without regard to the internal control structure. IR

comprises a number of elements among which the following

three are the most significant.

 The size of the institution (C1). A multi-national bank has a

higher possibility of being misused in laundering money

than a local saving bank (Reserve Bank of New Zealand,

2011). Although the measurements of institution size are

various (e.g. by asset, capital, revenue, profit, employee

number or branch number, etc.), the number of customers is

the most relevant indicator in analyzing the interaction be-

tween size and MLR of an institution because all money

launderings are eventually committed by “customers”, and

thus could be used here to define the size of institution.

 The geographic location of the institution (C2). This ele-

ment actually concerns where the customers come from. In-

stitutions operating in the regions with high crime rate

would face more potential money-launderers and thus have

higher MLR (Federal Financial Institutions Examination

Council, 2010).

 The business nature of the institution (C3). Institutions with

high proportion of cash deposit or withdrawal, cross-border

wire transfer and non-face-to-face businesses are normally

more vulnerable to money laundering. (Council of Europe,

2010).

Control Risk (CR, B2)

CR is the risk that money laundering may occur and not be

prevented or detected on a timely basis by the internal control

structure of the institution. CR is determined by the factors

inside an institution and can be controlled by the institution.

This paper identified the following seven fundamental factors

which directly affect CR level and from which other inside

factors are derived (Ma, 2009).

 Management attitude and knowledge (C4). Reviewers can

assess the senior executives’ attitude and knowledge about

AML by interviewing the executives as well as the em-

ployees or by checking the written responsibilities of the

executives.

 Procedures and measures (C5). Reviewers can assess the

validity of the AML procedures and measures in an institu-

tion by off-site reviews.

 Computer system (C6). The two core roles that the computer

system is expected to play in the AML structure of an insti-

tution are storing customer identification information and

transaction records and analyzing abnormal transactions.

On-site test is needed to assess the efficiency of the AML

computer system in an institution.

 Resources allocated (C7). The resource allocated in AML

can be measured by the total working hours of all AML

staff in the institution or the amount of funds spent on

AML.

 Performance of customer due diligence (C8). On-site in-

spection is needed to assess whether the performance of

customer due diligence regulatory requirements or internal

procedures are fully implemented within an institution, in-

cluding identifying and verifying the identity of the cus-

tomer and the beneficial owner, recording the basic identity

information of the customer, and so on.

 Performance of suspicious transactions report (C9). On-site

inspection is needed to assess whether STR regulatory re-

quirements or internal procedures are fully implemented

within an institution, including analyzing abnormal transac-

tions, filing reports and making them to the financial intel-

ligence unit.

 Trainings (C10). To be assessed by interview or examina-

tion.

Making Pair-Wise Comparisons and Obtaining the

Judgmental Ma t ri x

After building AHP model, the priorities have been decided.

Elements are compared pair-wise and judgments on compara-

tive attractiveness of elements are captured using the traditional

9 rating scale, with 9 indicating “extreme importance”, 7 indi-

cating “very strong or demonstrated importance”, 5 indicating

“strong or essential importance”, 3 indicating “fairly impor-

tance”, 1 indicating “equal importance” when give the intensity

of importance. Scores of 2, 4, 6, 8 demonstrate intermediate

values and reciprocals show inverse comparison.

16 experts were invited to give the relative importance, and

for the convenience of calculation, the average value is round

number.

Results and Discussion

Pair wise comparisons are carried out with AHP software

and the result is shown as Table 1.

As indicated by the table, with less one third contributed by

IR (B1, 30.2%) and most proportion determined by CR (B2,

69.8%), the ML is basically “controllable” provided that the

institution has a strong internal control system. Reviewers

should thus focus more attentions on the CR control of a finan-

cial institution. Regarding the lowest hierarchy of factors, the

primary task for a financial institution in mitigating MLR is to

strictly conduct Performance of customer due diligence (C8,

19.1%) and Performance of suspicious transactions report (C9,

17.3%) measures, and supports from management (C4, 9.4%) is

also considerable important followed by valid internal rules (C5,

9.1%). Although not controlled by the AML arrangement of the

institution, the size of the institution also plays a significant role

(C1, 15.9%) in determining the MLR of the institution.

Table 1.

The weights of MLR factors.

1st

hierarchy 2nd

hierarchy Weights

to 1st 3rd

hierarchy Weights

to 2nd Weights

to 1st

C1 0.525 0.159

C2 0.200 0.060 B1 0.302

C3 0.275 0.083

C4 0.134 0.094

C5 0.131 0.091

C6 0.072 0.050

C7 0.074 0.052

C8 0.273 0.191

C9 0.248 0.173

B2 0.698

C10 0.068 0.047

K. JIA ET AL.

Reviewers or assessors can mark a financial institution on

each factor, multiply the marks by the weights of factors in

Table 1 and then add the products up to obtain the final

weighted MRL mark of the institution. By comparing the

weighted MLR mark of one institution with others, ranking of

institutions could be obtained.

Conclusion

In this study, MLR was decomposed into a hierarchy of ele-

ments whose weights are computed by AHP. The MLR struc-

ture established and the weights of the elements obtained en-

able reviewers to calculate the MLR level of a financial institu-

tion and guide financial institutions to manage their MLR to

some extent. The MLR in financial sector, however, constantly

changes as a result of the development in society and economy,

which requires a dynamic MLR assessment model. Adjustment

on the MLR structure and the weights of risk elements should

be on an ongoing basis.

Acknowledgements

Ling Zhang thanks the financial support from The Ministry

of education of Humanities and Social Science Research Fund

Plan/Youth Fund/Self-financing project (11YJC870036) as well

as Open Fund IT2012006 of the ISTIC-Thomson Reuters Joint

Lab for Scientometrics Research.

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