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Indian Journal of Finance and Banking; Vol. 4, No. 2; 2020 
                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

1 

 

A Framework to Score the Risk Associated with Suspicious Money 
Laundering Activity and Social Media Profile 

 
 

Dillip Kumar Parida 
Research Scholar 

K. L. U. Business School, K. L. University 
Greenfields, Vaddeswaram 

Guntur District, Andhra Pradesh, India 
E-mail:dillip_parida@hotmaill.com 

 
 

D. Prasanna Kumar PhD 
Associate Professor 

K. L. U. Business School, K. L. University 
Greenfields, Vaddeswaram 

Guntur District, Andhra Pradesh, India 
E-mail:dr.prasanna@kluniversity.in 

 
 
 
Received: May 27, 2020       Accepted: June 30, 2020        Online Published: July 15, 2020 
 
doi: 10.46281/ijfb.v4i2.662        URL: https://doi.org/10.46281/ijfb.v4i2.662 

 
 
Abstract 
Money laundering has immense entailments. The criminal who possesses black money and wants to mask it as legitimate 
must fabricate the source to look genuine. It makes the crime organized and more systematic to break the financial system. 
The existing AML (Anti Money Laundering) solutions and its design based on the creation of a transaction profile. Most 
of the leading AML software focuses on financial transactions and rarely focuses on linked suspicious individual‟s social 
media profiles. Social networking is one of the most popular platforms to interact with others and millions of users use 
these platforms to communicate with each other from around the world. At the same time, the web has plenty of social and 
demographic information to create an accurate profile that aims to construct a legitimate profile. This paper consolidates 
the fragmented discussion from several articles and provides a detailed view of fraud profile identification.  Practical insights 
are identified from various AML solutions and summarized from an extensive literature review. The risk scoring framework 
and definitions of filters can be widened to include more parameters for effective alert generation. In this paper, we propose 
an approach and risk scoring framework to assess customer profiles that drive the suspicious profile or transactions based on 
social media attributes. 
 
Keywords: Anti Money Laundering, Social Media profile, Financial Fraud, Fraud Detection, Money Laundering 
Detection, Risk profile scoring, Anomaly Detection. 
 
1. Introduction 
Money laundering is the process of criminal proceeds for the secrecy of their illicit source of money. Money laundering 
helps to enjoy the profit without the stake of their illegal resource. Money laundering is described as „„the process by which 
the proceeds of crime and the true ownership of those proceeds are concealed or made opaque so that the proceeds appear 
to come from a legitimate source” (Johnston & Abbott, 2005).The widespread technology and wired transfers help the 
money laundering activity by layering the transactions. The organized criminals seek to make investments in the 'legitimate' 
economy, by investing in the legal business and real estate (Kruisbergen, Kleemans, & Kouwenberg, 2015).With the 
advancement of technologies and integration of financial systems, it provides multiple ways for the drug traffickers, terrorist 
groups, and smugglers to expand their operation and launder the money. The Identification of the money laundering 
activity is too difficult and complex due to the large volume of transactions globally. To identify and prevevfnt this activity 
many financial institutions installed software to track the illegal wired transfer. Now robust anti-money laundering solutions 
are developed to combat financial crime. The technology helps to combat the money launder based on risk profile and alert 
system. But the major Anti Money Laundering software focuses on financial transactions and limited to banking 
transactions. Money launders constantly devising new and innovative ways to launder money. It has become a major concern 
to combat money launderers with effective new measurements. Hence prevention of Money Laundering, detection of back 

mailto:dr.prasanna@kluniversity.in
https://doi.org/10.46281/ijfb.v4i2.662


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money placement in the economy, and control of these illicit activities are crucial for a stable economy and safeguard 
financial institutions.  
   Social Media sites such as Facebook, Linkedin, and Tweeter can be utilized as an investigative tool to identify and 
vet individuals and businesses, determine connections between counterparties and discover criminal involvement (Glass, 
2018).To combat this situation, the Anti-money laundering framework needs to be more robust and a combination of 
transaction profiles with a social media profile may provide additional capability to detect money launderers. This new 
methodology can identify the patterns in social media activities that could indicate a suspicious profile. If the data is not 
consistent across social media platforms then it triggers a suspicious alert about the credibility of the user for further 
assessment.  
   We find that a better view of risk profiles can be created with the help of social media attributes. Scoring of these 
parameters may help to detect the credibility of a user based on social media entities and attributes present in the network. 
The relationships can be built with the social network entities and degree of centrality to find the association. A lot of 
research has been done in this area but not much regarding anti-money laundering. The objective of the proposed solution is 
to identify the suspicious customer and create a complete 360-degree view of a customer profile that provides evidence to 
categorize the risk category based on social media events.  
   This research paper explores the possibilities for Anti Money laundering application to include suspicious social 
media profile detection and its scoring framework to categorize risk categories. We analyzed real-world AML systems and 
social media platforms that can be used as an investigative tool by utilizing Facebook, LinkedIn, and Tweeter to identify 
and appraise individuals and businesses. The major contributions of this research paper are (a.) A framework to include 
social media events as one of the criteria and (b.) Risk scoring model to provide a risk score based on predefining criteria.  
 
2. AML Risk views for Financial and Social Media Profile 
Every day we have millions of transactions in the financial industry. Transactions can be identified associated with risk and 
analyzed to remove the potential threat. Risk views give a data profile with the possibility of money launder activity. Social 
media contains a plethora of information and evidence of keywords that may be associated with money laundering. The 
social media data can be mined to get meaningful information regarding suspicious transactions. Social media could be rich 
sources of data that leads to a suspicious profile or event which is more prone to money laundering. Using text analytics on 
social media data for profiling may detect a suspicious user in social media concerning money laundering. 
 

     Table 1.  Money Laundering Events 
 

Financial and Transactional Events Social Media Profile Network Events 

 Cash Transactions (high frequency and value) 

 Sudden high-value transactions in the dormant 
account  

 Accounts with High Turnover 
 Instruments with High Volatility 

 Transactions without descriptions 
 Sending and receiving high volume transactions out 

of the country 
 Multiple transfers in multiple accounts 

 Purchasing of remittance in cash just below 
threshold limits 

 Multiple accounts with same name and addresses 
 

 The profile has nothing in common 
such as friends or even a professional 
interest 

 Profile Network connections are 
random 

 Offensive or sensitive content on the 
profile 

 Lack of complete information in 
profiles 

 Data mismatching in different platform 
and profiles 

 

  Even though the researchers presented a few strategies and approaches for identifying Fake profiles, 
however, it is as yet a hard challenge. For instance, some AI calculations are proposed for identifying suspicious 
financial transactions, however, it does not give the complete proof to recognize all counterfeit transactions. 
Utilizing accurate Social profile identification process can be an additional parameter to the financial transaction 
to understand the complete view of the user profile and its associated transactions. 
 
3. Profile and Characteristics 
Understanding the risk profile enables the financial institutions to apply appropriate risk management processes and 
solutions to the AML compliance solution to mitigate risk. 
 
 Characteristic: A distinguishable behavior related to the financial transaction of the account holder. (e.g. unusual cash 

transaction pattern) 



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 Profile: Profile created with a standard set of mostly defined by the characteristic of the transaction (e.g. online 
transaction) and social media profile. The solution will also have a standard set of behavioral rules about customer 
segments and their typical behavior. 

 A profile is based upon the transactions and it may contain no of occurrences or value of the transaction. 
 A profile is based upon the consistency of data across social media platforms. 
 A Social media profile that linked to any other suspicious profile. 

 Any of the social media profile attributes has a noise keyword that relates to illegal activity, Money laundering, or 
crime. 

 When the transaction (financial and social) imported and profile created, the system compared the profile with its 
historical records/profile. 

 The combination of the rules and the customer Segments to identify instances or patterns that are abnormal or 
suspicious behavior. 

 The defined rules or behavior combine to assign a risk to the profile and the alert generated with a risk score. 
 The risk score is derived from an algorithm using the sum and weightings of the associated risks. 

 

Figure1. Risk Filter process and stages 

 
4. Risk scoring model on Filter View 
The combination of financial transaction view and social media profiles created a pool of information that combines 
structured and unstructured data. If any data that looks suspicious in financial transactions can be profiled in the social 
media category. That will give a more detailed view of other aspects to get an insight into data concerning suspicious 
transactions.  
 
4.1 Risk Scoring Model Framework 
Risk rating involves the categorization of individual profile based on social and financial parameters, into a series of 
graduating categories based on types of risk. A primary function of a risk rating model is to assist in the creation of the 
KYC profile. As well, risk ratings assist management in predicting changes in the social and financial portfolio quality and 
its impact on the current KYC Framework of the financial institution. The risk rating can lead to a proactive response to 
the potential threat and a wider choice of reactive action to track money laundering. Risk ratings should be determined 
based on individual institution's policy and government regulations. Continuous monitoring of exiting profiles required as a 
part of the profile review process. 

      The following sample risk rating model can be enhanced and developed concerning organization policy. The model 
may be modified as appropriate to meet the specific needs of individual institutions. 

      Each transaction is evaluated under five risk filter components. Behavior, Aggregated, list matching, Key AML noise 
phrase, and suspicious profile detection. Risk Scores used for ratings are based on an evaluation of individual transactions 
and aggregated also. The maximum individual risk component score and the overall score are mentioned below. 
 
4.2 Risk Filter Components 
Risk filter components (Risk Assessment Filter) refer to the screening of transactions, events, information related to the 
customer. This screening can be classified into five different categories based on the nature of information and source 
systems. The classified filters are associated with risk percentage (Weight) to prioritize features for alert generation. The 
weight can be changed based on the priority and organization‟s money laundering policy. 

     Risk Assessment Score is calculated for a particular Risk Assessment Filter and to be anywhere between 0 to 100. 
Every filter can be divided into subsections or sub filters where the score can be calculated based on the type of violations. If 



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the Risk Assessment Filter has n number of subsections or criteria‟s then the maximum score of any one of the matching 
filters can be considered here. 

 
Table 2. Risk filter components 
 

Filter  
Number 

 Risk 
Assessment 
Filters 

Description Weight Risk Assessment 
Score ( Max) 

1  Behavior Behavior views provide the behavioral aspect 
of data. The pattern shows if any negative 
behavior associated with financial data or 
social media profile data. ( e.g. Money  
transfer to illegal entities or user‟s social 
media profile linked to drug dealer profile) 

15% 100 
 
Refer to Table 
3 

2  Aggregated Aggregated view. a combined, weighted view 
on multiple risk views. Identify if an activity 
is suspicious faster, based on previously-
recognized patterns and aggregated view of 
the entities. (e.g. If a legal threshold for cash 
deposit in the bank account is 5000 USD 
and a user deposits multiple cash deposits 
less than 5000 for a while, then the system 
will create an aggregated view of customer 
transaction profile) 
 

20% 100 
 
 
Refer to Table 
4 

3  List Matching List matching. customers, accounts, and 
transactions can be matched against 
blacklists (provided by Office of the 
Foreign Asset Control) or defaulters lists 
(provided by the central or federal bank), 
and the results can be shown in risk 
views. If any of the social media profile 
directly or indirectly linked to any of the 
blacklisted people. (e.g. If John Dave is a 
criminal and blacklisted by OFAC 
(Office of the Foreign Asset Control) for 
terrorist activities then List Matching 
filter will sync with OFAC list on real-
time and any transaction made by John 
Dave will be on hold till the alert is 
examined) 
 

25% 100 
 
Refer to Table 
5 

4  Key AML 
noise phrases 
(Suspicious 
and Money 
laundering 
keywords) 

Text mining is the measurement of the 
various qualitative and quantitative 
attributes of textual (unstructured data) 
related to financial crimes. Collection of 
online data from social media and other 
online platforms in the form of 
unstructured text, web harvesting, and 
web data extraction (Batrinca & 
Treleaven, 2014). Text mining can be 
done on every word related to customer 
and words those are more related to 
money laundering can be clustered to 
mine meaningful insight. (e.g. if user‟s 
social media conversation consists of 
appreciation to terrorist activity and the 
user is linked to Non-profitable 
Organization then the system creates risk 
score and creates alert) 

20% 100 
 
 
Refer to Table 
6 



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5  Suspicious 
profile 
detection 

Any financial transaction related to 
blacklisted Customer, Account, 
Instrument, PEP (Politically Exposed 
Person). The profile can be identified as a 
suspicious profile. (If any customer is 
linked to political exposed person in 
social media and unusual high-value 
transaction identified for that customer, 
then it creates a high score and generates 
alert)(Choo, 2008). 

20% 100 
 
 
Refer to Table 
7 

  Total  100%  

 
Table 3. Risk Assessment Score. Behavior 
 

F
il
te

r 
1

  
. B

eh
av

io
r 

Behavior Subcomponents Scenario Assessment Score 

Unusual Activity in comparison to previous 

data. Data are mostly derived out by 

comparing unusual transaction records with 

normal behavior norms(Gao & Ye, 2007). 

Frequent transactions in 

comparison to previous 

transactions 

Score out of 100 

Money transfer to the illegal entity or 

outside of industry entity 

Money transfer to the blacklisted 

country 

Score out of 100 

Unusual social profile network increase or 

decrease 

Increase of irrelevant social profile 

count  

Score out of 100 

Unusual conversation with comparison to 

previous data 

The inclination of user 

conversation towards illegal social 

media posts 

Score out of 100 

Unusual change of recency and frequency 

of activities 

Multiple account creation from 

the same user 

Score out of 100 

Note. Please refer to Table 2(Risk filter components) and Filter number 1 for definition  
 

Table 4. Risk Assessment Score. Aggregate View 
 

F
il
te

r 
2

. A
gg

re
ga

te
 v

ie
w

 

Aggregate View Subcomponents Scenario Assessment Score 

Aggregate view of cash deposit Aggregated deposit of cash exceeds the 
previous record 

Score out of 100 

Aggregate view of wire transfer Aggregated wire transfer exceeds the 
previous record 

Score out of 100 

Aggregate view of Cash 
Withdrawal 

Aggregated cash withdrawal exceeds the 
previous record 

Score out of 100 

Aggregate view of suspicious words 
on the social platform 

An aggregated view of negative sentiments 
on the social media platform 

Score out of 100 

Aggregate view of linked 
suspicious profiles 

A complete view of linked profiles related 
to the customer 

Score out of 100 

Note. Please refer to Table 2(Risk filter components) and Filter number 2 for definition  
 
Table 5. Risk Assessment Score. List Matching 
 

F
il
te

r 
3

. L
is

t 
M

at
ch

in
g 

List Matching 

Subcomponents 

Scenario Assessment 

Score 

List from OFAC OFAC issues list of blacklisted people. The business rule matches the 

name and based on matching it provides the score. If the name matches 

exactly then score comes 100 and its name matches partially then score 

comes 80. 

Exact 

Matching=100 

Partial 

Matching=80 

List from United 

Nations 

As above As Above 



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List from European 

Union 

As above As Above 

List from World 

Bank 

As above As Above 

Other official watch 

lists 

As above As Above 

Country-specific 

sanction list 

As above As Above 

Region-specific 

sanction list 

As above As Above 

Note. Please refer to Table 2(Risk filter components) and Filter number 3 for definition  
 
Table 6. Risk Assessment Score. Money Laundering Keywords 
 

F
il
te

r 
4

  
. M

on
ey

 L
au

nd
er

in
g 

K
ey

w
o
rd

s 

Money Laundering Keywords 

Subcomponents 

Scenario Assessment Score 

Terms related to the negative source 

of wealth 

Source of money from illegal activities Score out of 100 

Negative News User attended a gathering which has negative news Score out of 100 

Negative Key Words  User conversation or linked information consists of 

a negative keyword like “Drugs” or “Import of the 

illegal article” 

Score out of 100 

Negative sentiments like support to 

terrorist activities 

Sharing and support of news inclined towards 

illegal activities 

Score out of 100 

Negative Industry The profile is linked to industries like leather or 

ammunition. 

Score out of 100 

Association with the bankrupt entity 

or illegal industry words 

The profile is linked to a bankrupt company or 

customer. 

Score out of 100 

Note. Please refer to Table 2(Risk filter components) and Filter number 4 for definition 
 
Table 7. Risk Assessment Score. Suspicious Profile 
 

F
il
te

r 
5

. S
us

p
ic

io
us

 P
ro

fi
le

 

Suspicious profile Subcomponents Scenario Assessment Score 

Geographic Patterns - Proximity 

relationships between apparently unrelated 

countries and location 

Transfer from a user in the 

current country to another 

high-risk country and the user 

has no personal and 

professional relationship with 

risk profile countries. 

Score out of 100 

Conversation Patterns - fictitious invoice 

numbers, fictitiously-generated transaction 

amounts 

Profile with false information 

related to financial 

transactions. 

Score out of 100 

Profile network Patterns – link to suspicious 

profile 

Link to profile those marked 

as suspicious  

Score out of 100 

Profile network Patterns – link to the 

political exposed person 

The profile is linked to 

political parties and profile 

Score out of 100 

Profile network Patterns – link to negative 

words e.g. Gambling and Drugs etc. 

The profile is linked to the 

gambling profession 

Score out of 100 

Note. Please refer to Table 2(Risk filter components) and Filter number 5 for definition  



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4.3 Usage of Risk Scoring Model Framework 
 

 
 

Figure 2.  Money Laundering Filter and It‟s Sub Components 

The weight scoring formula is to calculate the potential risk of the customer. The value of the weight is used as a multiplier 
for the risk value. When risk values are used to compute the risk, weights are used to calculate the importance of 
a risk assessment score. 

 

Figure 3. Potential risk 

Where                   Potential Risk = Total risk score of customer 
Wi = Risk weightage for each filter i = 1,..., n 
Ri = Risk assessment score for each filter i = 1,..., n 
n = Number of risk assessment filter 

 
Note that for each customer, the max score is taken for the Risk Assessment in the respective category. Under each risk 

filter component, the number of possible scores can be divided into smaller sections or areas. 
Note. The parameters can be introduced or modified based on financial institutions' policies and regulations. For each 

filter sub-component, there will be a maximum score out of the list of parameters. If one profile matches more than 2 
parameters then the Maximum score will be considered for that filter subcomponent and will be calculated for the final 
score. 

The following table provides the risk score range to identify the risk category and it helps to promote the case to further 
investigation. 



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Table 8. Risk Category 
 
This risk category table is a reference to the final potential score to decide whether a suspicious profile requires further 
investigation or not. 
 

Minimum Risk Score Max Risk Score Risk Category Further Action 

0 30 Low No 

31 60 Medium No 

61 100 High Yes 

 
The risk category helps to filter the cases for further investigation. The cases with low risk will come to filter again with the 
aggregated view. It will compile all the views and comes to alter when it becomes significant. The event with the low score 
but frequent occurrence may accumulate to a high score for an alert. 
 
5. Calculation of risk for Customer or Business Account with the help of Risk Assessment score and Weight  
This can be better understood with the help of an example. John Smith is a Bank customer and he has an account with the 
Bank. The Bank needs to run the AML filter on John Smith‟s information to calculate the risk score based on his financial 
transactions and social media profile details. Below are the scenarios and risk core for John Smith to calculate the Potential 
risk score. 
 
Table 9. Risk Calculation for customer profile 
 

Risk Assessment 
Filters 

Weight Risk Assessment 
Score 

                            Remark 

Behavior 15% 55  Money transfer to an illegal entity or outside 
of industry entity (John smith regularly 
transfers money to an account of the leather 
industry. But, John Smith works in Pharma 
Company). Based on the score matrix this 
event scores 55 out of 100. 

 
 Unusual social profile network increase or 

decrease (John Smith added many new friends 
from a geo risk country and an unusual 
increase of profiles in the friend list.). Based 
on the score matrix this event scores 45 out of 
100. 

Note. Score matrix can be repaired based on 
individual organization requirements. 
 
John Smith has two marked behaviors events and 
the max score (55, 45) is 55. So, the score of the 
Behavior risk assessment filter is 55. Reference 
form Table 3. 

Aggregated 20% 45 Similar to the above. The score for Aggregated Filter 
View is 45. 
Reference from Table 4. 

List Matching 25% 60 Similar to the above. The score for List Matching Filter 
View is 60. 
Reference from Table 5. 

Key AML noise 
phrases  
(Suspicious and 
Money laundering 
keywords) 

20% 90 Similar to the above. The score for Key AML Noise 
Phrase Filter View is 90. 
Reference from Table 6. 

Suspicious profile 
detection 

20% 100 Similar to the above. The score for the Suspicious 
Profile Filter View is 100. 
Reference from Table 7. 



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Refer to the expression in Equation (1) 

Potential Risk Score= (.15*55) + (.2*45) + (.25*60) + (.2*90) + (.2*100)  

           =70.25 (The risk score is rounded off to 70.) 

Risk Category. High (Reference to Table 8.) 
This customer can be created as a case for further investigation and analysis by AML experts. The case 
management system must have a feedback system where the feedback should incorporate the changes in the scoring 
framework. If the score or alert is not accurate for any of the customer then the scoring framewo rk must be 
updated to generate a valid score. 
 
6. Insight 
Based on the literature review of relevant research papers and personal experience in this area, this paper focuses on 
several key insights for the new generation AML solutions to handle money launders more effectively. This will 
help to mitigate financial security risks with the use of customer‟s social media data. Users of these insights can 
generate more discussion and methods to address money laundering scenarios. To determine filters or par ameters 
for addressing different risk areas, organizations need to analyze each potential area to ensure that money 
laundering risks are being sufficiently considered and the organization‟s AML policies need to be restructured in 
the context of social media regulations. Although many organizations have considered social media profile check as 
one of the KYC (Know Your Customer) check and customer due diligence process, consideration of this data in 
the daily monitoring process is not widely implemented. Thus, organizations need to consider new areas in AML 
measurement policies to ensure that the solution is more effective to generate alerts (Reserve Bank of India - Reports, 
2009). A standardized framework for all financial institutions and social media platform is required. This strategy 
will help to feed enough information to the monitoring system so that an accurate result can be derived from the 
alert generating engines of AML software. 
 
7. Conclusion and Future Work 
The money laundering policy plays an important role in banks due to its necessity. The measure on customer social media 
profile is rarely part of money laundering regulations. However, few banks consider social media profile checks as part of 
know your customer and customer due diligence process. In this paper, we provide a basic framework for identifying money 
laundering activity by creating a transactional profile and social media profile. We show firstly the whole process of AML 
activity and then the framework through which the solution can be extended considering the social profile network. We also 
discuss an extension of current AML solutions to have a better watch view. An important knowledge retrieved from our 
experience is that by selecting appropriate parameters, the solution can be customized and can be applied to detect Money 
Laundering cases from social networks as well. We used parameters that are widely used in the banking and financial 
industry. The study finds out that we have more research papers on financial transaction monitoring but there is very little 
research work done on suspicious social media profile identification. The monitoring solution can be more robust if we add 
more data from the social media platform. More data will give better insight into the customer and more parameters can be 
identified for the filter to have better decision points. The subcomponents are the pillars of primary filters and these 
subfilters can be modified based on organization policy. The business rules and boundary criteria for decisions can be 
enhanced based on industry and geography. Besides, the combination of subcomponents for parent filter helps to improve 
the accuracy in the detection of money laundering cases. The paper adds to the existing literature on improving the anti-
money laundering framework and the inclusion of more parameters for each area. 
 
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