Home Business Insights & Advice AI and FATF guidelines for effective customer lifecycle management

AI and FATF guidelines for effective customer lifecycle management

by Sarah Dunsby
2nd Jul 24 9:28 am

Unless you are an AML or Compliance officer, you probably know Customer Lifecycle Management (CLM) as a method for mapping the consumer journey, from initial contact with a brand or product through to purchase and loyalty. It’s interesting that the same abbreviation, CLM, can be used but means something different depending on who’s talking about it. More than that, it can also be used differently within the same organisation. Let’s use a bank, for example. Typically, CLM involves stages such as customer acquisition, onboarding, engagement, retention, and reactivation, utilising tools and techniques to optimise customer interactions and enhance satisfaction.

Unlike the freedom of CLM variability in marketing or sales strategies, compliance-related CLM must align with regulatory expectations. In the context of Anti-Money Laundering (AML) and compliance, CLM takes on a more rigorous and regulatory-focused role, ensuring that risks associated with financial crimes are identified and mitigated. This article will focus specifically on FATF recommendations and how regulatory technology leaders like Complytek help businesses adhere to these standards.

FATF sentiment on AI and machine learning technology

‘’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.’’

FATF requirements and recommendations for customer lifecycle management in AML

In the context of Anti-Money Laundering (AML) frameworks, customer lifecycle management involves several critical steps to ensure compliance and mitigate financial crime risks. So what does FATF recommend for any institution dealing with money and sensitive data? Here are the five essential steps:

Customer onboarding

Collect and verify customer information to assess their risk level.

Key actions: Implement robust Know Your Customer (KYC) and Customer Due Diligence (CDD) processes.

Importance: Helps identify high-risk individuals and entities early, preventing potential financial crimes.

Ongoing monitoring

Continuous monitoring of customer transactions to detect suspicious activities.

Key actions: Use automated systems for transaction monitoring and update customer risk profiles regularly.

Importance: Detects changes in customer behavior that may indicate money laundering promptly.

Enhanced Due Diligence (EDD)

Apply additional scrutiny to high-risk customers or transactions.

Key actions: Conduct detailed background checks and transaction analyses for high-risk customers, especially politically exposed persons (PEPs) and cross-border transactions.

Importance: Provides an extra layer of security for high-risk scenarios.

Transaction review

Periodically review transactions and customer behavior for irregularities.

Key sctions: Maintain detailed audit trails and file Suspicious Activity Reports (SARs) when necessary.

Importance: Supports compliance and investigations by maintaining comprehensive records.

Offboarding

Properly terminate relationships with high-risk customers or those involved in suspicious activities.

Key actions: Close accounts of high-risk customers and report the reasons to regulatory authorities.

Importance: Ensures institutions do not facilitate ongoing illegal activities.

IMAGO/Zoonar.com/Matej Kastelic / Avalon

How does AI and machine learning help to achieve the set standards

AI technology in AML customer lifecycle management tools is used in compliance and risk management, focusing on anti money laundering checks at every stage from KYC onboarding to transaction monitoring.

Customer Due Diligence (CDD)

Data analysis: ML algorithms analyse vast amounts of data from various sources to verify customer identities accurately and efficiently.

Pattern recognition: ML identifies patterns in customer data that might indicate fraudulent activities or false information, improving the accuracy of CDD processes.

Automation: Automating the CDD process reduces the time and resources required, allowing for quicker onboarding and better compliance.

Ongoing monitoring

Real-time analysis: ML provides real-time monitoring and analysis of transactions, detecting anomalies and unusual patterns that could indicate money laundering.

Adaptive learning: ML systems adapt and learn from new data, continuously improving their ability to detect suspicious activities.

Reduced false positives: By accurately distinguishing between normal and suspicious activities, ML reduces false positives, allowing compliance teams to focus on genuine threats.

Enhanced due diligence (EDD)

Risk assessment: ML assesses and scores the risk level of customers and transactions more accurately, helping to identify high-risk entities.

Detailed analysis: ML conducts in-depth analyses of high-risk customers, including their transaction histories and behavioral patterns, providing a comprehensive risk profile.

Efficiency: By automating EDD processes, ML allows institutions to handle a higher volume of high-risk assessments efficiently.

Suspicious Activity Reporting (SAR)

Anomaly detection: ML detects anomalies in transaction data that human analysts might miss, prompting timely SARs.

Automated reporting: ML systems can automatically generate SARs based on detected anomalies, ensuring timely and accurate reporting to authorities.

Comprehensive audits: ML maintains detailed audit trails and records, facilitating thorough reviews and investigations.

Offboarding

Risk identification: ML helps identify customers that pose high risks through continuous monitoring and risk assessment.

Decision support: By providing detailed risk profiles and historical data, ML supports informed decision-making regarding account closures.

Regulatory compliance: ML ensures that the offboarding process is compliant with regulatory requirements by maintaining detailed records and reporting reasons for account closures to authorities.

The right CLM platform for you

The right Customer Lifecycle Management (CLM) platform needs to provide all the necessary functionalities to ensure regulatory compliance and mitigate financial crime risks. Complytek offers a full scope of services designed to meet these needs, from robust onboarding and ongoing monitoring to enhanced due diligence, transaction review, and offboarding. By integrating automated workflows, real-time alerts, and customisable risk parameters, Complytek helps institutions (such as Insurance Company Hollard, Liquidity and Trading Solutions provider Advanced Markets, Banking as a Service (BaaS) platform MyMonty and many more) to focus on high-risk areas, reducing operational costs and freeing up resources to handle critical tasks efficiently.

Responsible use of AI for optimal decision-making

It is important to not get carried away by every new AI or machine learning trend and choose the best solutions on the market that have withstood rigorous testing and proven to be accurate. Organisations can ensure robust compliance and risk management by using tools and methods that work. Contrary to the AI hype, not all AI solutions are perfected to meet regulatory standards just yet, and when choosing to use ML & AI-assisted products, it is important to consider where and how they will help you and not hinder you. Complytek uses advanced AI & ML technology that works practically and has been perfected to be instrumental in scaling and growing businesses, offering precise, reliable, and efficient tools to manage the complexities of AML and compliance.

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