Use of Softwares in AML/CFT

 

AML software refers to an automated tool used in the financial and related sectors. It helps various companies meet compliance demands as well as prevent, detect, investigate, and report suspicious activities associated with money laundering, terrorist financing, and fraud. Globally, financial sector regulators follow the Recommendations of the Financial Action Task Force (FATF) for combatting money laundering.  

Flagged financial transactions or suspicious activity must be reported to the regulatory body for investigation. AML software helps organisations achieve AML compliance and generally has three facets:

• Client screening
• Transaction screening
• Transaction monitoring

 

AML software works by tracking transactions and analyzing suspicious activity while later if needed, generating Suspicious Activity Reports (SARs) to follow and identify parties involved. It also frequently scans and maintains a database of countries to identify clients on a regulatory body's blacklist.

AML software has two key goals, which oblige companies to:

• Follow FATF’s recommended Risk Based approach. For high-risk customers like politically exposed persons (PEPs), organizations should implement enhanced due diligence (EDD) measures for stricter monitoring as well as to detect changes in customer risk levels.
• Ongoing compliance with KYC, also known as Perpetual KYC (pKYC). This is when the AML software adopts a proactive approach by continuously monitoring customer behavior. Using the software, the company can receive automated alerts triggered based on customer behavior and replace many manual compliance tasks, such as risk assessments.

This way, AML software assists institutions in fulfilling daily AML compliance requirements and staying vigilant to emerging hazards.

Client Screening Software. 

This AML software verifies the identity of new clients at the onboarding stage. It also checks whether the new client is on any sanctions lists or other watchlists.  It assesses what level of financial crime risk your business incurs in the onboarding stage and can be utilised at any stage during the client life cycle. It is inclusive of:

Compliance with FATF’s recommended risk-based approach. When customers are assessed as high-risk (such as politically exposed persons, or PEPs), organisations might implement a heightened level of monitoring known as enhanced due diligence (EDD). This AML process assesses clients’ activity or behaviour on an ongoing basis to detect if financial crime risk levels change.

Ongoing know your customer compliance (Perpetual KYC). This AML software exemplifies the latest approach to truly understanding your customer. The proactive approach is to monitor customer behaviour continuously, with review alerts automatically triggered by their behaviour, rather than through periodic, manual risk assessments.

‍Transaction Screening Software. 

Applying new technologies, including digital ID and client screening/matching onboarding tools, can facilitate more streamlined onboarding processes adapted to the risk, context, and individual. Not only can this facilitate more effective compliance, but it can also improve customer experience.

Client screening and matching tools benefitting from NLP and advanced fuzzy matching tools allow elements of identification to be differentiated, like similar names. They can also overcome language differences and identify cross-references with adverse media information and different databases. Thus AML software integrates and interprets multiple data points in transaction messages for each customer, including the identities of both sender and receiver, and establishes if either are on sanctions lists. 

AML Transaction Monitoring Software. 

Building on client and transaction screening software, this AML software analyses the patterns of each client’s transactions, typically against a library of rules or filters. It determines whether trends of transactional behaviour fit criminal profiles, as well as whether anomalous transactions by a client may require further investigation or not. Possible red flags raised are reviewed and analysed by AML teams and appropriate responses are decided upon, including whether a suspicious transaction report (STR) is necessary.

AML can be applied with the same scope as manual transaction monitoring, covering every kind of transaction, currency exchange, wire transfer or credit-related activity that a firm might handle. The monitoring mechanism can be calibrated to detect a range of specific behaviors, including:

 

·         Transactions that exceed a certain threshold

·         Frequency of transactions over a certain time period

·         Unusual or out-of-character customer account behavior

·         Transactions involving individuals on an international sanctions list

·         Transactions involving Politically Exposed Persons(PEPs)

·         Transactions involving high-risk countries 

·         Transactions that otherwise do not match a customer’s risk profile

·         Adverse Media involving a customer that might indicate their risk profile has changed

 

Transaction monitoring software offers numerous benefits for the AML/CFT process, including:

 

·         Detection: Transaction monitoring software can detect suspicious behavior at the point at which a customer interacts with the firm’s services.

·         Efficiency: Monitoring software not only offers procedural efficiency and accuracy but also minimizes false positives.

·         Usability: Firms can install and implement transaction monitoring software quickly and easily without the need for extensive training or technical knowledge.

·         Prioritization: Monitoring software helps firms automatically manage their regulatory priorities and organize customers into risk tiers. 

·         Adaptability: Transaction monitoring software can easily be adjusted for different risk levels and regulatory environments without the need for technical support. 

·         Confidence: Firms can select tried-and-tested transaction monitoring software that offers a level of industry confidence and that has been used in previous audits and investigations.

The Financial Sector’s need for AML software

• Increased criminal threat level. Rapidly evolving financial crime typologies are exploiting technological advances and digitalised money, which means that money laundering is accelerating.
• Political pressure. Globally, entire economic regions and governments are pressuring regulatory bodies to close legislative loopholes which make the financial sector vulnerable to money laundering and other serious economic crime. Regional record fines for individual institutions were smashed the world over in 2020, and the total number of fines issued to financial institutions increased by 141% to 198 from 82 such penalties in 2019.
• Unsustainable compliance costs. The costs of labour related to anti-money laundering among middle to large-sized financial institutions is increasing worldwide. The LexisNexis 2020 Global True Cost of Compliance Report concluded that 2020 saw an increase of US$33 billion in AML costs since 2019, from US$180.9 to US$213.9 billion. This represents a global increase in AML expenditure of 18.2%.
• Struggling AML compliance methodologies. AML compliance teams in the global financial sector are struggling to keep pace with innovative money laundering techniques, owing in part to the silo effect of outdated practices. This means unwelcome scrutiny from both the regulators- which resulted in AML/CFT breaches constituting 54% of the cash amounts of fines issued from 2008 until the end of 2020- and the media, whose negative reporting on transgressions causes inevitable reputational damage.

Application Processing Interfaces 

Applying new technologies, including digital ID and client screening/matching onboarding tools, can facilitate more streamlined onboarding processes adapted to the risk, context, and individual. Not only can this facilitate more effective compliance, but it can also improve customer experience.

Client screening and matching tools benefitting from NLP and advanced fuzzy matching tools allow elements of identification to be differentiated, like similar names. They can also overcome language differences and identify cross-references with adverse media information and different databases.

AML practitioners the world over face regulators on one hand, and criminals on the other. It has never been more important for the financial sector to embrace AML software.

The FATF formally endorsed responsible innovation for AML/CFT in a public statement issued in Buenos Aires on 3 November 2017, which declared: “The FATF strongly supports responsible financial innovation that is in line with the AML/CFT requirements found in the FATF Standards, and will continue to explore the opportunities that new financial and regulatory technologies may present for improving the effective implementation of AML/CFT measures.”

The inadvertent use of the banking system for money laundering activities is a key challenge facing the financial services industry. In response, regulatory authorities have introduced anti-money laundering (AML) regulations to detect and prevent such activities. Complying with these regulations requires banks and financial institutions to implement an effective compliance system, along with appropriate tools and systems. This, in turn, requires companies to build an effective business case for the right compliance system equipped with requisite capabilities and latest technology tools. Challenges banks face:

Increased governance: Banks and financial institutions can find it difficult to manage cross-border and multi-jurisdictional AML-compliance requirements and evergrowing customer due diligence requirements. Identifying beneficial ownership and initiating remedial measures to address AML gaps uncovered by regulatory reviews also come with their own set of challenges.

Lack of skilled personnel: Getting skilled resources with in-depth knowledge of AML can be a challenge. Other issues include high on-boarding timelines and costs, and attrition. Organizations also need to invest considerable time and effort in keeping personnel abreast with changing regulatory requirements.

Complicated processes and technology: AML compliance requires banks to put in place a multiplicity of processes and technology solutions that will consolidate KYC data and systems in a single repository. They also need to create infrastructure for cross-channel detection of suspicious activities, improve data quality, and standardize data to enable centralized analysis of fraud and financial crimes. The risk level assigned during on-boarding varies according to the transactions undertaken by the customer. This means that banks have to assess the risks dynamically for each customer, and change risk levels accordingly, to prevent false positives. This necessitates continual transaction monitoring for each customer, which is a mammoth task.

Emerging Trends in the AML Space

A new paradigm is emerging wherein principle-based AML systems grounded in scientific disciplines are replacing inflexible rules-based solutions. Some emerging trends in AML compliance are:

 Focus on digital payment-related issues: Regulatory focus is currently centered on containing money-laundering risks associated with new payment methods like mobile wallets, e-payments, and e-money issuers. In addition, top priority is being accorded to combating cybercrime and curbing potential money-laundering risks associated with virtual currencies.

Use of third-party utilities: Third-party services such as the shared services utility model for KYC compliance, managed services for transaction monitoring, and browserbased delivery of commercial watch lists are being leveraged by several banks. Financial institutions are using the expertise of third-party providers for KYC verification and due diligence, and to spot new AML risks and violation methods.

Adoption of enterprise-level approaches: Enterprise-wide case management for an overall view of risks at the enterprise level, and effective centralized control is becoming the norm. Banks are also implementing AML or fraud platform convergence capable of detecting both fraud and money-laundering activities to derive operational synergies. Risk-based approaches are replacing traditional rule-based approaches.

Adoption of analytics: Banks are adopting analytics for their AML initiatives. Some areas where analytics can be used successfully include:

Fraud detection: Advanced filtering technologies and analytics for real-time fraud detection and generation of alerts based on changes in behavior patterns are gaining ground. Banks are also using high-tech linkage analysis (network visualization) to detect suspicious activities and track money trails. They are using cross-channel detection support to get an integrated view of suspicious activities across all payment types and anti-stripping technology to detect masking or manipulation of wire transfer data.

Screening: Banks are tapping social media as an additional source of information to validate customer identity, identify politically exposed persons (PEP), and obtain default information for account reviews of a customer. Banks are leveraging social media analytics to support their enhanced due diligence (EDD) process, which includes negative media screening efforts for discovery of litigations, adverse orders, and other potential risks. Banks are also using software for sanctions screening, which results in benefits such as reduced timelines for enhanced due diligence; faster and informed decision making; quicker identification of key risks associated with companies, management teams, and other affiliates; and faster processing of transactions and monitoring of alerts.

Detection of rogue activities: Banks are using analytics to detect anomalies and identify patterns indicative of laundering, and detect and prevent suspicious activities in real time. High speed streaming and computing to handle transaction data in all formats, parallel processing of transaction data and data from other channels, and high-speed alert generation and processing are other trends that are catching up.

Similarity detection: This can be used to analyze data at fixed intervals in order to find similarities in current and past financial crimes, which helps eliminate redundant investigation and create stronger cases.Trending analysis: This technique can help identify behavioral trends using cognitive computing of nonparametric statistics. With this, banks can identify behaviors and bad actors associated with moneylaundering activities. 

Anomaly detection: This can help detect and study unusual behavior of a single actor. Trusted pair identification: This technique can help banks identify and ignore trusted pairs of parties through link analyses patterns, thereby reducing false positives.

Linkage detection: Banks and financial institutions are using analytics to detect entity-level  linkages, and study the behavior of different linked accounts colluding for a laundering activity. Some of the linkages that can be tracked include:

Linked customer: To identify IDs owned by the same customer across lines of business.

Linked accounts: To track customers having multiple accounts under different names.

Linked transactions: To track transactions linked to a closed group of entities and performed with a clear intention of routing money to destination accounts

Linked access: To track transactions happening through common cyber infrastructure.

Some Technologies & their use in AML/CFT 

Digital Identity Solutions can enable non-face-to-face customer identification/verification and updating of information. They can also improve authentication of customers for more secure account access, and strengthen identification and authentication when onboarding and transactions are conducted in-person, promoting financial inclusion and combating money laundering, fraud, terrorist financing and other illicit financing activities.

Natural Language Processing can support more accurate, flexible and timely analysis of customer information and reduce inaccurate or false information and enabling more efficient matching and search for additional data. Better and more up-to-date customer profiles mean more accurate risk assessments, better decision-making, and fewer instances of unintended financial exclusion. NLP uses fuzzy logic, a logical technique that takes imprecise or approximate (fuzzy) data and processes it using multiple values to produce a useable (but imprecise) output.  

Applying NLP and fuzzy matching tools to AML compliance allows issues associated with poor data quality (such as incomplete or distorted data) to be overcome and false positives and negatives to be efficiently reduced.

Artificial intelligence (AI) and machine learning (ML) technology-based solutions applied to big data can strengthen ongoing monitoring and reporting of suspicious transactions. These solutions can automatically monitor, process and analyse suspicious transactions and other illicit activity, distinguishing it from normal activity in real time, whilst reducing the need for initial, front-line human review. AI and machine learning tools or solutions can also generate more accurate and complete assessments of ongoing customer due diligence and customer risk, which can be updated to account for new and emerging threats in real time. 

FATF recommends the use of AI-enhanced transaction monitoring as it can allow regulated entities to comply with greater speed, accuracy, and efficiency. AI and machine learning are especially useful when applied to big data to strengthen ongoing monitoring, distinguish normal from suspicious activity in real-time, and filter cases that require additional investigation.

Machine learning, which is the currently the best-known form of AI, also provides the ability to automate the process of risk analysis partially or fully by analysing a greater volume of data and identifying emerging risks. This can increase the degree of confidence when applying risk-based measures.

The Role of Machine Learning : Aided by rapid developments in data science, machine learning, with its ability to help construct algorithms for predictive data analysis, is revolutionizing the way financial ecosystems work. Machine learning holds great promise for the banking system, especially in the area of detecting hidden patterns and suspicious money-laundering activities. Machine learning helps identify money-laundering typologies, strange and suspicious transactions, behavioral transitions in customers, transactions of customers belonging to same geography, age, groups and other identities; and helps reduce false positives. It also helps analyze similar transactions for focal entities and correlate alerts that were flagged as suspicious in regulatory report

Application Programming Interface (APIs) and Distributed Ledger Technology (DLT), data standardisation, and machine readable regulations can help regulated entities report more efficiently to supervisors and other competent authorities. The technologies also allow alerts, report follow-ups, and other communications from supervisors, law enforcement, or other authorities to regulated entities and their customers, as well as communications among regulated entities, and between them and their customers. The application of more advanced analytics by regulators can also strengthen examination and supervision, including by potentially providing more accurate and immediate feedback. DLT offers:

• The ability to make identity verification easier by improving transaction traceability on a cross border and even global basis.  
• Increased monitoring possibilities because transactions could be managed via a single ledger and shared among several institutions across jurisdictions, or via interoperable ledgers.
• Improved management of customer due diligence requirements as well as greater cost effectiveness and a more accurate, quality-based data pool.

FATF on Use of New Technologies for AML/CFT

Opportunities and Challenges of New Technologies for AML/CFT report was published by FATF in July 2021

.New technologies have the potential to make anti-money laundering (AML) and counter terrorist financing measures (CFT) faster, cheaper and more effective. They can improve the implementation of FATF Standards to advance global AML/CFT efforts, ensure financial inclusion and avoid unintended consequences such as financial exclusion.

As the global AML/CFT standard setter, the FATF is strongly committed to keeping abreast of innovative technologies and business models in the financial sector and to ensuring that the global standards remain up-to-date and can enable “smart” financial sector regulation that both addresses risks and promotes responsible innovation. Accordingly, the FATF reviewed the opportunities and challenges of new technologies for AML/CFT to raise awareness of relevant progress in innovation and specific digital solutions. The FATF also looked at the persisting challenges and obstacles to their implementation and how to mitigate them. This project included the review and analysis of regulatory technology (RegTech) and supervisory technology (SupTech), both of which can improve the effectiveness of FATF Standards. 

BIS on Use of New Technologies in AML/CFT

Suptech applications for anti-money laundering

 Suptech, or the use by financial authorities of data collection or advanced data analytics tools enabled by innovative technologies, seems more advanced in the field of anti-money laundering (AML) and combating the financing of terrorism (CFT). In particular, AML/CFT authorities need suptech-enabled advanced data analytics tools to analyse large volumes of information at their disposal. AML/CFT authorities are in general pursuing similar advanced data analytics tools, such as network analysis, natural language processing, text mining and machine learning. These tools increase their ability to detect networks of related transactions, to identify unusual behaviours and, in general, to transform significant amounts of structured and unstructured data into useful information that contributes to their respective processes. Efficiency gains seem to be the number one benefit of advanced data analytics tools, which could help capacity-constrained AML/CFT authorities. However, the use of these innovative technologies gives rise to a number of challenges, including computational capacity constraints and data privacy and confidentiality issues. 

Detecting potential anti-money laundering (AML) and combating the financing of terrorism (CFT) violations is one field where data analytics tools seem more advanced. AML/CFT authorities have either supervision or financial intelligence functions, or both.

AML/CFT supervision and financial intelligence functions have different mandates. Authorities with AML supervision functions are expected to ensure compliance by financial institutions with requirements to combat money laundering (ML) and terrorist financing (TF). Authorities with financial intelligence functions, ie financial intelligence units (FIUs), meanwhile, are expected to serve as national centres for the receipt and analysis of suspicious transaction reports and other information relevant to money laundering, and to disseminate the results of that analysis. FIUs sometimes also have AML supervision functions.

Both AML/CFT supervisors and FIUs need advanced data analytics tools to analyse the large volumes of information at their disposal. AML/CFT authorities typically receive substantial amounts of transactional and non-transactional data. On top of these traditional sources of data, some AML/CFT authorities are now actively collaborating with other government agencies and private entities to expand the scope of data available to them. Some authorities are also exploring the use of non-traditional sources of information (eg newspaper articles, social media) and integrating them with traditional information to come up with richer analyses.

The difference in mandates does not seem to affect the types of advanced data analytics tools the AML/CFT authorities are pursuing. AML/CFT authorities covered in the paper are in general pursuing similar advanced data analytics tools, such as network analysis, natural language processing, text mining and machine learning. These tools increase their ability to detect networks of related transactions, to identify unusual behaviours and in general to transform significant amounts of structured and unstructured data into useful information that contributes to their respective processes.

Authorities have used different strategies to develop these tools. AML/CFT authorities that are within the central bank, or prudential or conduct authority, generally benefit from the institutional strategy to utilise innovative technology to help in supervision work and can develop these solutions inhouse. For some AML/CFT authorities, taking advantage of ready solutions in the market may be more efficient. Others are actively collaborating with the academic community and promoting research in this field. Many of the authorities use a combination of these approaches. The optimal solution for a specific authority will depend on several factors such as the profile of the authority, the characteristics of the financial system that the authority oversees, and the legal framework in which the authority operates. 

Challenges from use of New Technologies

However, the use of these innovative technologies gives rise to a number of challenges.

First, computational capacity may be an issue, since these tools deal with large volumes of data.

Second, data privacy and confidentiality requirements provide safeguards that AML/CFT authorities must consider in using certain data and external resources in developing data analytics tools.

Third, assessing the effectiveness of these tools might be challenging, in particular for FIUs given the necessary time to prove the occurrence of a money laundering activity.

Finally, tools based on supervised machine learning could lose their effectiveness over time, especially if not regularly updated with new training data, given the capacity of criminal organisations to change their behaviour in order to avoid detection.

There is scope for information-sharing among AML/CFT authorities on the data analytics tools they are developing or using in order to promote peer learning. Although the data analytics tools used by AML/CFT authorities are tweaked to reflect their mandates, the underlying methodologies of these tools are quite similar. There are therefore opportunities for peer learning through regular exchange of information and sharing of experiences on the development and use of these tools.

AML/CFT authorities that are just starting to develop their data infrastructure have a “late mover” advantage and may find it easier to integrate advanced data analytics tools. These authorities have the advantage of developing their data infrastructure from scratch without the burden of legacy systems. They can design it in a way that makes the data collection, validation and management processes seamless, while more easily enabling the integration of newly developed analytical tools.

 ML/TF risks have international reach, so development of data analytics tools that are international in scope should be considered. The tools discussed in this paper are all national in scope. Money laundering, however, is an international issue, and criminal organisations tend to exploit loopholes anywhere in the world. Therefore, a strong argument could be made for international cooperation and collaboration in terms of developing data analytics tools with an international coverage.

Happy Reading

Those who read this, also read:

1. Transaction Monitoring

2. Suspicious Transaction - AML/CFT