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Mine Your Way to Combat Money Laundering, Part 1

Money laundering generally involves a series of multiple transactions used to disguise the source of financial assets. This enables those assets to be used without compromising the criminals who are seeking to use the funds. Through money laundering, the criminal tries to transform the monetary proceeds derived from illicit activities into funds with an apparently legal source. Worldwide value of laundered funds in a year ranges between $500 billion to $1 trillion, according to the United Nations Office on Drugs and Crime. Weak financial regulatory systems, lax enforcement, gaps in the information systems of financial institutions and corruption are key factors that make certain jurisdictions particularly attractive for laundering illicit proceeds.

In this article we present the risks of money laundering and the need for anti-money laundering (AML). Challenges in detecting occurrences of money laundering using traditional methods and the limitations of the same are outlined. We also examine how data mining can deal with the complexities of the modern money laundering operations. Finally, the advantages of data mining and its challenges are elaborated.

Process of Money Laundering

There are three phases to the complete laundering of funds, beginning with the placement of currency into a financial services institution (placement), continuing with the movement of funds from institution to institution to hide the source and ownership of the funds (layering), and concluding with the reinvestment of those funds in an ostensibly legitimate business (integration). This is done by using the services of formal financial systems such as banks, money changers, wire transfers, etc. Advances in information technologies for banking and financial services have not only increased efficiencies of routine financial transactions but have also allowed criminals to use the same services to launder money.

Money Laundering Risks - Need for AML

Money laundering poses serious threats not only to financial institutions but also to the nation. The risks faced by financial institutions are:

  • Reputation risk: The integrity of the banking and financial services marketplace depends heavily on the perception that it functions within a framework of high legal, professional and ethical standards.
  • Operational risk: It can be defined as the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events. A public perception that a bank is not able to manage its operational risk effectively can disrupt or adversely affect the business of the bank.
  • Concentration risk: This risk relates to the exposure of banking and financial services market place to a single customer or groups of related customers.
  • Legal risk: Banks may become subject to lawsuits resulting from the failure to observe mandatory KYC standards or from the failure to practice due diligence.

Society is also at risk because money laundering provides the fuel for drug dealers, terrorists, arms dealers and other criminals to operate and expand their criminal enterprises. Hence, the government regulatory bodies partnering with the financial institutions and law enforcement agencies have initiated elaborate AML programs to track and prevent financial crimes.

A Typical AML Solution

While countermeasures to all three phases of money laundering are important, laundered money is most vulnerable to detection at the placement stage.1 Hence, international regulatory and law enforcement efforts have concentrated especially on developing methods to make it difficult to place illicit funds without detection by developing measures.

AML Legal Framework

The first step in the AML initiative was setting up the legal framework which would become a major tool for fighting the money laundering activities. The different laws and acts, such as the Bank Secrecy Act and the Patriot Act, evolved focusing on this area. These laws spell out the mandatory requirements to be followed by the financial institutions (FIs). The major requirements are:

  • Retention and traceability of financial transactions and
  • Reporting of certain suspicious financial transactions.

In addition to these laws, there are several guidelines and recommendations (e.g., FATF recommendations, Wolfsberg principles) developed by international bodies that help the FIs to set up appropriate checks and balances so that it satisfies the regulations and help them fight financial crimes. The focus areas are:

  • Customer identification and customer due diligence: Measures to establish the identity of the clients and beneficial owners; collect and record the proofs establishing the identity - commonly referred to as know your customer (KYC).
  • Continuous monitoring of the customer transactions and identifying suspicious activities.
  • Reporting on suspicious activities (SAR) and policy violations.
  • Establishment of a compliance office and internal audit function; and
  • Continuous employee education and training.

Building Blocks for an AML System

In today's world, it has become mandatory for the bank to implement a robust internal AML system. There are heavy penalties for noncompliance such as heavy fines, asset forfeiture and even suspension of the charter. Trends show that money laundering is becoming more and more of a cross-border phenomenon and new crime strategies are coming up almost daily. Hence an effective AML system should have:

  1. Interfaces that integrate with the international network of financial and regulatory bodies and enhances the capability for information processing;
  2. An enterprise-wide architecture that integrates internal core financial applications and facilitates information flow and traceability across the applications within the financial institution;
  3. Transaction monitoring system having the intelligence to detect and alert suspicious activities;
  4. Case management workflow to efficiently investigate and action the alerts;
  5. Customer risk assessment model to restrict entry of unwanted entities into the financial system; and
  6. Efficient reporting system for both regulatory and internal control reporting.

Figure 1 illustrates the major components within an AML System

Figure 1: Components of an AML System

The Key AML Challenges

Though the AML systems of today are becoming cleverer with application of newer technology and techniques, the criminals always seem to be one step ahead. They come up with new strategies which only the human brain and trained eyes can detect. Hence, an effective AML program should have a synergy between the technology-driven integrated systems and human investigative skills.

The Human Factor

Many financial crimes can be recognized right at the initiation point by careful observation and interpretation of the clues. Alerting front-end personnel (like the customer service executives), supported by a properly geared-up investigative workflow can do wonders. The challenges to the financial institution are:

Continuous employee training: The money laundering trends and the respective regulations are changing so frequently that it has become a big challenge for the institutions to keep their employees up to date. An efficient AML system requires early identification of crime, prompt notification and timely follow-up. Hence continuous employee training (at all levels) is a key success factor for any AML system.

Efficient AML case workflow: An effective AML system needs an efficient and flexible workflow system with a high level of coordination between different groups of actors. An alert (generated by human or system) needs to go through a series of phases before a crime can be confirmed. These phases (as illustrated in Figure 2) require a lot of analysis and sometimes only the human worker can identify the missing links. The processes are strictly driven by service level agreements (SLAs) and compliance rules. The challenge lies in streamlining the operational processes, which will ensure compliance (noncompliance leads to heavy penalties).

Figure 2: AML Case Management Workflow

The System Factor

Though AML investigation is best performed by a human, the sheer volume of data and complexity of relationships make the process impossible without system support. New AML scenarios are discovered almost every day. The incidents involve multiple financial institutions and span across country borders. The suspects try all possible permutations to hide their identity. Hence, the challenges in this area are:

External integration: Since money laundering today is a cross-organization cross-border phenomenon, it's a challenge keeping track of the transactions and identifying the suspicious activities. This calls for building an interface that integrates with the external applications and facilitates exchange and validation of information. In this area, it is important that one learns from others' mistakes and best practices.

Data mining: The huge volume and variety of transactions makes the analyst's and investigator's job more difficult. The challenge lies in scanning through millions of data elements and identifying the links and patterns. A business intelligence layer and a comprehensive search and reporting tool (which spans across applications) are the essential components that complement the case investigator.

Case and activity trail: Regulators prescribe that all the transactions and transaction-related artifacts need to be tracked and should be easily traceable. Considering the huge volume of activities, the challenge lies in developing an efficient content management engine, where all the case-related artifacts and audit trail can be maintained and archived.

Customer risk model: The focus of any financial institution is on the growth of its customer base. On the other hand, getting unwanted customers into the system leads to heavy financial loss. The challenge is to balance between these two aspects and to define the customer acceptance criteria, which can act as a primary filter and reduce risk for the institution.

Enterprise-wide integration: Any global bank will have hundreds of disparate applications to serve its business functions. This causes serious impediment in the information flow within the institution and leads to different versions of similar data. AML systems demand a single view of the customers, which can only be achieved by creating an integrated enterprise-wide architecture.

Traditional Approach to Money Laundering Detection and its Limitations

It should be noted that each of the individual phases (placement, layering and integration) will be composed of a variety of individual activities that may vary across institutions and countries. Additionally, the number of channels through which the monetary transactions are enabled are multifarious. This duo combination greatly complicates the approach to detect money laundering.

Traditional approaches to the detection of money laundering activities followed a labor-intensive, manual approach. Traditional investigative approaches could be classified into identification of money laundering incidences, detection avoidance and surveillance of money laundering activities.2 These approaches relied to a great extent on field activities such as surveillance/discreet enquiries, interviews, search warrants, subject interviews and the like. Data sources for following up on important leads are likely to be fragmented. Such an investigative approach consumes substantial time and resources. Law enforcement officials may work on a particular case for many years before they can piece together sufficient evidence to persecute a criminal. Given that the volume of financial data and transactions have increased in a variety of ways, such techniques need to be supported by automated efforts for money laundering pattern detection.

The complexity of banking and financial services operations have also made it difficult to keep track of the various patterns of money laundering. Launderers are willing to shift their patterns of activities from physical cash to conversion to monetary instruments, reliance on wire transfers and use of non-bank money transmitters. Wire transfer transactions may be made using a variety of mechanisms, such as shell companies and front corporations, false invoicing, etc. The similarity between legitimate and illicit businesses in terms of cash turnover has also been used by launderers to obscure the trail of funds. This calls for automated methods to monitor financial transactions and to detect instances of money laundering.

Part 2 of this article continues in the October 9 DM Direct e-newsletter.

References:

  1. B. A. K. Rider. "The Weapons of War: The Use of Anti-Money Laundering Laws Against Terrorist and Criminal Enterprises - Part 1." Journal of International Banking Regulation 4, 2002.
  2. R. C. Watkins, K. M. Reynolds, R. Demara, M. Georgiopoulos, A. Gonzalez and R. Eaglin. "Tracking Dirty Proceeds: Exploring Data Mining Technologies as Tools to Investigate Money Laundering." Police Practice and Research 4, 2003.

G. S. Vidyashankar is former director, Data warehousing and Business Intelligence Practice, at Cognizant Technology Solutions. He has more than 16 years of IT and management experience in the software industry, with specific focus on data warehousing and business intelligence. He has extensive experience in developing business analytical models for banking and financial services, retail and the telecom sectors. He headed the Business Analytics Group at Cognizant Technology Solutions. He may be reached at vidyashankar.gs@gmail.com.

Rajesh Natarajan is assistant manager, Projects, Business Analytics Group at Cognizant Technology Solutions. He specializes in applying data mining techniques to real-world problem scenarios. He has published in international conferences and journals. Natarajan may be reached at rajesh.natarajan@cognizant.com.

Subhrangshu Sanyal is assistant manager, Business Development, Banking and Financial Services at Cognizant Technology Solutions. He has over 10 years of IT and rich domain experience. He may be reached at Subhrangshu.Sanyal@cognizant.com.

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