Addressing Customer Risk: What Actions Should You Take?

Every new customer will always pose potential risks to a business. It's imperative that businesses that seek to ensure compliance with anti-money laundering (AML) and counter-terrorist financing (CTF) laws and regulations enforce efficient customer risk assessment processes.

Discover the steps to effectively identify customer risks and the actions you must take to keep your business compliant.

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What is a customer risk assessment?

Customer risk assessment is the process of evaluating the potential risks associated with customers, whether new or returning, to ensure that an organization can manage these risks effectively. Financial institutions and businesses that adhere to AML laws must decipher each customer's many complexities from the start to do this. This includes understanding the purpose of their transactions, behavior, financial stability, and true source of funds.

A customer risk assessment protects the organization from financial losses, regulatory issues, reputational damage, and, ultimately, unwanted financial crimes. This means businesses identify, analyze, and mitigate various risks that customers might pose.

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Customer risk classifications

When a financial institution conducts a customer risk assessment, each individual or entity can be categorized based on their risk profiles. This helps organizations better understand the potential risks they pose if they move forward with the business relationship and decide on measures to ensure AML compliance.

Many variables come into play when identifying risk factors for every customer. Some of the most common information includes financial stability, credit history, transactional behavior, and regulatory compliance. Once the customer risk factors are considered, they can be categorized according to the following.

Low-risk customers

Low-risk customers are individuals and entities with strong financial stability, as demonstrated by bank accounts, excellent credit history, and consistent positive transaction behaviors.

Long-standing customers with a good track record and a favorable level of transparency are often categorized as low-risk.

Level of monitoring required

Minimal monitoring and lowered scrutiny are needed for low-risk customers since businesses know there is little to no chance of money laundering risk and have a good idea of transaction volume and behaviors.

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Medium risk customers

Medium-risk customers have a moderate level of financial stability and an average credit history. They may include individuals or businesses that have only transacted for a few years or have a shorter relationship with financial institutions.

They can occasionally exhibit irregular transactions that may still need time for businesses to determine their behaviors.

Level of monitoring required

Customers in the medium-risk category are subject to regular transaction monitoring and periodic reviews.

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High-Risk Customers

High-risk customers can be categorized as such for various reasons. These include poor financial stability, weak or no credit history, or a history of irregular and suspicious transactions that could suggest financial crimes.

Customers who are just beginning to build a credit history or have limited information are often listed as high-risk customers. Other individuals and entities from high-risk where money laundering risks are higher, are automatically listed in this category.

Level of monitoring required

Those listed as high-risk customers require stringent monitoring. This means enhanced due diligence processes, including frequent reviews, are launched.

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Other considerations for high-risk assessments

There are many other types of customers who may be flagged as high-risk to the businesses involved. Here are some that need effective enhanced due diligence measures in place.

• Politically Exposed Persons (PEPs)
  Customers identified as PEPs are individuals who hold or have held prominent public positions in their respective locales. This also extends to their family members and close associates. They are flagged as high-risk due to potential risk factors of being involved in corruption or bribery.
• Sanctioned entities
  Sanctions checks are an integral part of the KYC and KYB process. This helps financial institutions identify which customers are included in sanctions lists both locally and internationally. Being on the list prohibits organizations from engaging in transactions with them. It can also trigger immediate termination of business relations and reports to authorities.
• Foreign customers
  Foreign individuals and businesses who are from countries with high levels of corruption, terrorism, or weak regulatory frameworks can be flagged as medium to high-risk as well. Their assessment is highly dependent on risks associated with the country.
• High Net Worth Individuals (HNWIs)
  Individuals with a high net worth or significant wealth and complex financial activities require heightened monitoring. They may not necessarily be high-risk. However, the nature and complexity of their transactions are subject to tailored risk assessments.
• Dormant accounts
  Any other accounts with little to no activity over extended periods of time are at risk of identity theft and other financial crimes. These dormant accounts require periodic reviews for possible transaction fraud. They can also be categorized based on the frequency of account use.

Organizing customers into these categories allows organizations to tailor their risk management strategies, allocate resources more effectively, and ensure compliance with regulatory requirements.

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Customer risk factors

When building a customer risk profile, it's important to note that there are several key factors that must be considered to assess and manage potential risks effectively.

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These can fall into the following components.

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Financial stability

Financial stability can demonstrate how responsible a customer is in supporting each and every transaction they make. While this doesn't guarantee a financially stable customer will or will not be involved with money laundering, it does eliminate the chance of evading debt and other financial crimes like fraud. Under financial stability, you'll want to look at the following.

Income and employment

• The stability or frequency of income and its official sources
• An individual's employment history and current work status

Assets and liabilities

• A customer's total assets, which include real estate, investments, and savings
• Any outstanding debts and liabilities

Credit history

• Credit scores and reports sourced directly from credit bureaus
• History of loan repayments, credit card usage, and any defaults

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Transactional behavior

Transactional behaviors allow financial institutions to uncover the true intention of a customer's transactions. Knowing the true purpose of an account's creation helps determine whether or not it will be used for illicit activities. It can also set the baseline for what might be a normal transaction for this customer and when to be alarmed at a specific transaction. When looking to analyze transactional behavior, some considerations include:

Transaction patterns

• The frequency, size, and nature of a customer's regular transactions
• What the unusual or irregular transaction patterns are for this customer based on analysis

Payment behavior

• Timeliness and consistency in paying bills and loans before it's too late
• Checking for instances of late payments or bounced checks

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Customer profile

Understanding who the customer is can give you a lead in understanding what their motivations might be and what they can be capable of doing. For instance, gaining basic information can allow you to find out whether they are PEPs or mentioned in sanctions lists. Information you'll need to gather include:

Personal information

• Pulling the most basic details such as name, age, address, and contact information plays an important part in completing a customer profile
• Checking the marital status and dependents as this can impact financial stability

Geographical risk

• Knowing their country of residence and business operation can indicate the level of risk immediately
• Geographical location can inform you of associated risks, including political and economic stability

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Business and industry risk for business customers

An entity's industry can impact whether or not moving forward with the business relationship is beneficial. For example, a business in a high-risk industry like a casino can expose financial institutions to numerous money laundering cases.

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Industry risk

• All high-risk industries have their own risks due to the nature of their businesses
• Industries can also have volatile economic conditions and regulatory environments

Business size and stability

• The size of the business in terms of revenue and number of employees can impact financial institutions if they have complex structures that point to illicit activities.
• Business history and stability should be recognized and factored in, including all years in operation

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How do you assess customer risk?

Effective customer risk assessment involves a structured approach that combines data analysis, behavioral evaluation, and compliance checks. The general process of risk assessment goes through these stages.

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1. Collect customer data

Many customer data points are crucial to risk assessment, as they serve as the starting point that kicks the full process into motion. These details are used as a reference point to verify documents and spot fake information.

Three types of data gathered include the following:

• Personal information
  Basic details include the individual or entity's name, address, and contact information.
• Financial information
  Data or documented proof of income, assets, liabilities, credit history, and financial statements.
• Transactional data
  The above information allows businesses to analyze customers' past transactions and behaviors.

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2. Evaluate credit scoring

Evaluating credit scores is a valuable step when checking for customer risk. Financial institutions can pull this data from credit bureaus for insight into credit scores and reports, which speaks volumes about the customer's financial standing and trustworthiness.

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3. Behavioral analysis

Based on a customer's credit score and transaction history, risk financial institutions can use a risk assessment tool to analyze the individual or entity's financial transactions, payment history, and account activity. This can help determine whether or not the potential new customer has been involved in suspicious activity that could be money laundering and other financial crimes.

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4. Run regulatory compliance checks

Financial institutions must conduct various procedures to comply with AML and CFT laws and regulations and proper customer identity verification. This includes performing Know Your Customer (KYC) procedures for individual customers and Know Your Business (KYB) or corporate KYC for entities.

Businesses often use AML compliance and business verification tools to ensure that no detail and legal requirements are overlooked throughout the process. Good software should have access to a robust database of sanctions and PEP lists that combines machine learning and artificial intelligence to comb through all relevant data.

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5. Screen for fraud

Financial institutions have fraud detection systems in place that use algorithms to identify any potential transaction fraud instances. This is also highly important for dormant accounts prone to fraudulent activities.

This makes implementing any multi-factor authentication (MFA) and transaction monitoring tools vital to ensure the safety and compliance of financial institutions.

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6. Risk scoring and profiling

Based on all gathered data and considering any industry-related risks associated with PEPs and businesses, financial institutions can move on to risk profiling. Fortunately, many KYB verification systems have automated risk scoring and profiling.

For instance, Vespia automatically screens businesses for risks based on the data gathered throughout the onboarding process. This allows financial institutions to classify customers according to risk levels and streamline the decision-making process.

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7. Ongoing monitoring

Once customers are classified according to their risk profile, the necessary measures are set into motion. Financial institutions often establish ongoing monitoring mechanisms that check on customers as frequently as necessary. This helps detect changes in customer risk profiles and other significant changes that indicate crime.

When using automated systems, businesses receive alerts for significant changes, whether fraudulent attempts or simple changes in transaction behavior.

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8. Using advanced analytics and AI

Advanced analytics uses machine learning and artificial intelligence technology to further enhance risk assessment models for each customer it evaluates. In this way, financial institutions can benefit from predictive analytics to anticipate a customer's risk profile and potential risks and effectively employ proactive measures.

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Why use tools that employ a risk-scoring model

Using an efficient risk assessment tool that employs a risk scoring model for customer risk profiles offers several advantages that enhance risk management processes' effectiveness and consistency. Here are some key reasons financial institutions will want to use a risk-scoring model.

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1. Objectivity and consistency

A risk scoring model standardizes the entire customer risk evaluation process. This can likewise simplify the comparison process against other customers, helping businesses determine what is normal and unusual in financial transactions much faster.

This means it reduces the subjectiveness of evaluations and ensures customers are all assessed using the same criteria, resulting in a smoother, more consistent process.

2. Automated assessments

Automated assessments are made more efficient, which can significantly reduce the time and effort needed for standard and manual evaluation. This lets businesses screen a larger number of customers faster and more efficiently.

3. Advanced predictive capabilities

Businesses can proactively manage risks by identifying issues before they even happen. Machine learning and AI tools benefit businesses as they make it possible to predict risks based on historical data and trends.

4. Scalability

Considering that tools with a risk-scoring model can automate the risk assessment process, this makes it easier for financial institutions to grow their business without investing too much in additional resources. This means it's suitable for organizations that are small, medium, and even large enterprises.

5. Improved decision making

Finally, a risk-scoring model can improve a financial institution's overall decision-making process. With clear and quantifiable scores, the business can make an informed decision backed by data. This means strategic approval of customer relationships and risk mitigation becomes much easier to implement as well.

Finding the right risk assessment solution

Customer risk is one of the most important steps to ensuring compliance with AML and CFT laws and regulations. They can protect financial institutions from financial losses, regulatory issues, and reputational damage.

By taking a proactive approach to identifying customer risk, businesses can identify and mitigate risks early on with new and existing customers. This can be done with a tool that determines risk levels and automates the monitoring process.

Vespia understands the need for an efficient tool that simplifies the process and prepares your business for growth. Discover how the Risk Assessment Solution can help your business. Book a demo today.

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