AML Compliance with the help of Big Data

Big Data has revolutionized the world of technology and benefits industries with valuable data and insights. Large volumes of data can be instantly accessed, analyzed, and used to make informed business decisions. Financial companies can use big data to identify money laundering cases and keep criminals at bay. This emerging technology has become an inherent part of organizations that want to be AML compliant. AML refers to Anti-Money Laundering laws and regulations created and executed by the government to detect and prevent financial crimes. The AML rules empower banks and other financial institutions to monitor transactions closely and deter criminals from depositing and withdrawing funds from illegal activities.
Money laundering cannot be traced because of the primary reasons that the source of the illegally obtained funds cannot be continuously tracked, and it proves to be a massive deterrent in catching criminals indulging in financial crimes. Gradually economies worldwide are becoming data-oriented and engulfed with enormous data. Digitization and Big Data have become synonymous with each other.

With stricter AML regulations and the need to follow them diligently, organizations are taking the help of Big Data to combat the threat of money laundering. With Big Data, businesses can streamline the AML compliance process and monitor the transactions more efficiently. They can immediately identify any fraudulent activity and prevent money laundering with a risk-based approach by automating the 

AML compliance process, making it more efficient and cost-effective. Big data plays a crucial role in monitoring financial transactions and detecting suspicious customers. Big Data extracts information from varied resources, organizes them, and studies them to arrive at conclusive results. It can be defined as large sets of data multiplying quickly. The technology’s three main components are Volume, Velocity, and Variety popularly referred to as three Vs.

Volume: Big Data deals with large volumes of data from varied sources such as social media, business transactions, videos, etc. the volume of data depends on the organization.

Velocity- refers to the speed at which the data is being received. FIs might accept the data in real-time or batches, which must be analyzed.

Variety- refers to the types of data the Big Data will deal with. It deals with different types of data- structured, semi-structured, and unstructured data including text, audio, video and needs additional pre-processing to derive meaningful insights.

Authorities often fail to detect the sources of illegally obtained funds, and therefore they cannot prevent money laundering to the extent they want to. As per a UN report, 90% of laundered money remains undetected. But, Big Data can assist in tracking the transactions by analyzing large volumes of data, which otherwise is an arduous task if the analysis is done manually. The technology collects KYC information, real-time transactions, and regulatory data. The data collected is analyzed, vectored, and evaluated for fraud checks.
Event-based data are verified with data from different locations, account information, and other systems to detect fraudulent and suspicious activities.

Data Analytics can thwart the following challenges:

CDD- Customer Due Diligence – AML solutions use this information to verify customer identities using external information sources and detect risky profiles. CDD is an essential part of the Risk scoring management program. Businesses can efficiently complete the process of risk scoring in real-time, customize the process, and prevent criminals from being unnoticed.

Customer Onboarding process- The customer onboarding and monitoring process are quickened with Big Data. The KYC – Know Your Customer process ensures that the customers are not involved in money laundering and identifies profiles based on parameters such as PEPs and sanction lists. Companies need to screen customers and identify any suspicious behaviour with continuous monitoring.
Manual data processing is time-consuming, and the onboarding and monitoring process takes much longer. AML software solutions with built-in AI help businesses to stay AML compliant effortlessly.

Transaction Monitoring System: The Transaction Monitoring system regularly monitors the transactions of financial firms. It helps identify suspicious financial transactions that scrutinize areas such as particular transaction patterns.

Behaviour model profile monitoring: AML solutions are equipped to monitor each customer profile based on behaviour models. It will immediately identify any pattern going off-track and not meeting particular behavioural criteria. This way, it flags any suspicious activity and detects any possible money laundering case.

Avoid False positives: It’s essential to reduce the cases of flag positives as that can impact the business negatively. Alerts and notifications in the AML solution and results obtained with advanced analytics closely monitor the fraud detection processes. So, the legitimate customers are not bothered, but criminals are identified who are on the verge of committing financial crimes.


If you are looking for AML Compliance services, you can rely on AML UAE -one of the most reputed and acclaimed consultancy firms that have helped thousands of businesses in the UAE. We provide comprehensive AML consultancy services. Utilize the power of Big Data, streamline and make the AML compliance process more efficient. We are familiar with the UAE AML laws, and we have the requisite experience and in-depth industry knowledge to help you keep your business AML compliant.

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About the Author

Pathik Shah


Pathik is a Chartered Accountant with more than 25 years of experience in compliance management, Anti-Money Laundering, tax consultancy, risk management, accounting, system audits, IT consultancy, and digital marketing.

He has extensive knowledge of local and international Anti-Money Laundering rules and regulations. He helps companies with end-to-end AML compliance services, from understanding the AML business-specific risk to implementing the robust AML Compliance framework.