Financial security is the number one concern of all organizations. And while in the case of trade in goods and services, risk insurance is provided by the terms of the contract, financial technology, the instruments of transactions themselves include security measures.
Governments and regulators are pushing financial services institutions to the forefront of the fight against financial crime with increasingly stringent requirements. Here, data is used both to trigger processes, manage finances, predict risk, verify compliance, and more. In the anti-money laundering and compliance arena, data for detecting and controlling financial crime is challenging. In 2019, banks peaked at $10 billion in fines for violating anti-money laundering policies.
Working with unreliable and incomplete data makes it difficult to identify financial threats, undermining an institution's ability to effectively manage risk across the enterprise.
Big Data is structured or unstructured data sets of large volume. They are processed with special automated tools and then, using big data analytics, businesses can identify patterns and extract valuable insights from these data sets. For example, for statistics, analysis, forecasting, and decision-making.
Thanks to high-performance technologies such as grid computing or in-memory analytics-companies can use any amount of big data for analysis. Sometimes data is first structured, selecting only what is needed for analysis. Increasingly, it is being applied to tasks within advanced analytics, including artificial intelligence (AI). Typically, web scraping provides an easier and less expensive way to get data for analysis and further use.
Companies can perform real-time risk assessments at the moment a user creates an account. This allows for automated risk management and advanced reporting.
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Big data affects the compliance process. Regulators seek to evaluate every step of the data process, including collection, processing, and storage. The main reason for this in-depth assessment is to keep data safe from cyberattacks.
To achieve compliance status, a company must develop security strategies that will protect the data. The analysis must demonstrate how each risk mitigation strategy works and its level of effectiveness.
This is where big data comes in and helps provide accurate predictive reports on the likelihood of an attack. The process involves collecting all the data the company has and does not have and analyzing it using statistical algorithms to look for patterns and anomalies to detect fraud, policy violations, and other malfeasance.
Anti-money laundering (AML) is a framework for combating money laundering, terrorist financing, and weapons of mass destruction. It includes identifying, storing, and exchanging information about users, their income, and transactions between organizations and agencies. Money laundering is classified as an economic crime. AML laws require financial institutions to report any identified financial crimes to the appropriate regulators.
According to PwC, large banks spend $88 million a year on AML-related data storage. Yet the banks get no competitive advantage from this enormous expense.
Anti-money laundering is a key strategy for stopping financial crime by making it more difficult to deposit and access the proceeds of illegal activities. Financial institutions consider the following business activities as reasons for AML inspections:
AML uses different algorithms to link the KYC database and other information sources. Connection between AML and KYC must be constant and reciprocal. KYC modules can be used to tailor the AML program to the unique needs of a particular business, clarify customer risks and improve compliance.
FinCEN, FATF and OFAC are constantly improving AML laws to help the financial industry. However, not just banks must comply: any business that allows customers to divert money, including online marketplaces, cryptocurrencies, fintech and gaming platforms, must have an effective AML program or risk huge fines.
Know Your Customer or Know Your Client (KYC) is a policy of financial institutions that requires them to verify the identity of the counterparty before carrying out a financial transaction. The purpose of KYC procedures is to help financial institutions better understand their customers and monitor transaction risks. These include customer eligibility policies, customer identification procedures, transaction tracking and risk management.
In most financial institutions is a digital KYC check process that involves authenticating the identity document or further authenticating the document holder with additional biometric checks, such as facial or fingerprint verification.
KYC is only part of AML's anti-money laundering efforts. Its other elements are:
KYC software works on the principle of collecting information to compile a database. The process itself is a multi-step operation involving the collection and customers' personal information analysis. Financial institutions use web scraping, deep web analytics, and NLP as advanced collection and analysis methods.
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To verify the data, institutions send information to multiple independent third-party verifiers. Organizations compare it to official databases to verify whether the information is correct and matches up across all parameters, and they also compare the person's information to global criminal databases.
In 2021, FinCEN proposed that cryptocurrency and digital asset marketers verify customer identities. Coinbase, which works with more than 10 million users, requires them to provide personal identification data to confirm the absence of suspicious activity. In return, the exchange offers transaction security.
And the new cryptocurrency AML Bitcoin (Anti-Money Laundering Bitcoin) is designed with KYC and AML policies in mind, as well as the requirements of the American Patriot Act and the American Interbank Association. The currency is fully open to banks and governments through biometric identification of its holders.
The methods of money laundering vary in complexity. The most common is processing dirty money through other cash-based businesses. The legitimate profits from these businesses are mixed in with the criminal money, hiding their source. Anyone who has seen Breaking Bad remembers how Walter White bought the A1A car wash to launder the money he made from the drug business.
Financial crime and money laundering have devastating effects on economies, security, and societies, causing large swings in global capital flows and exchange rates. The United Nations estimates that between $800 billion and $2 trillion is laundered each year. And unfortunately, about 90 percent of this amount remains undetected today.
Money laundering can be difficult to trace, one of the reasons is connected to other crimes. Money laundering is the concealment of illegally obtained money to eliminate traces of criminal activity, which means that the criminals laundering the money first committed another crime through which they obtained the illicit funds. And it's becoming more and more difficult to trace as criminals are increasingly operating online and making greater use of the various technologies responsible for regulating money laundering.
Plus, criminals are constantly finding new ways to launder money. Billions of dollars are spent each year to determine which warnings require investigation.
Modern technology has made it much easier to detect abuse of the financial system as well as to gather information about abusers. In the U.S., all financial institutions are required to monitor the transactional behavior of customers.
The U.S. Financial Crimes Enforcement Network enforces these rules to combat both domestic and international financial crimes. All U.S. financial institutions comply with two laws: the Bank Secrecy Act (BSA) and the USA PATRIOT Act, which allow the federal government to monitor illegal activities and require financial institutions to submit reports, such as suspicious activity reports (SARs).
The European Union has anti-money laundering directives that are periodically updated to reflect current money laundering, terrorist financing, and criminal risks. The EU's Fifth Anti-Money Laundering Directive 5AMLD was published on July 9, 2018, and came into force on January 10, 2020. It focuses on the regulation of cryptocurrencies and introduces:
6AMLD was published in late 2018 and came into force in June 2021. This directive includes provisions to harmonize the definition of money laundering offenses, expand the scope of money laundering and criminal liability of individuals, and increase penalties for those convicted of money laundering.
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But unfortunately, only 30% of corporate respondents proactively inform their banks about KYC and Customer Due Diligence (CDD) changes. The reason financial institutions are reluctant to proactively update KYC and AML information is because the process itself is a headache. And anyone affected by KYC and AML issues is likely to continue to make changes only under duress.
Institutions that fail to comply with the law face serious and costly consequences. In 2017, Deutsche Bank was fined $630 million for failing to detect a $10 billion Russian money laundering scheme. And in 2019, Deutsche Bank was under criminal investigation for possible monitoring violations.
UBS and Capital One were fined $14.5 million and $100 million, respectively, in 2018 for similar crimes. The ACAMS website regularly lists sanctions against banks for regulatory noncompliance.
There is a growing need among financial institutions to use data and technology to more cost-effectively and accurately identify potential criminal behavior and prevent criminal activity.
We can see an upward trend in the market value of anti-money laundering software. In 2016 and 2017, the market was valued at $690 million and $868 million. The anti-money laundering software market is projected to reach $1.77 billion by 2023.
Advanced data collection, analytics and cognitive technologies like AI, machine learning (ML) and automation are already helping to sort out false positives and improve inefficiencies in existing investigative processes.
Big data solutions help in:
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Integrating modern data sources into a data management program enhances discovery and ongoing forensics. Non-traditional data sources include documents, news feeds, images, videos, social media, visitor data, etc. from which a lot of useful and relevant information can be gathered. Their growth is due to increased customer interactions and digitalization of business processes.
Anti-money laundering statistics show that 95% of the alerts generated by the system are false positives. Here, big data can reduce false positives when a legitimate customer is flagged as high risk or fraudulent. Machine learning uses advanced analytics to identify risk detection patterns, fraud and data quality issues that can lead to false positives. The company then adjusts the rules to catch criminals rather than legitimate customers.
Big data and scraping technologies enable businesses and financial institutions to retrieve data from various sanctions lists designated by the UN, OFAC and related financial regulatory watchdogs, extract important information and conduct AML checks on high-risk customers. If a customer frequently engages in large transactions or belongs to a high-risk country, adverse media screening, PEP compliance and other watch list checks are performed. This is how big data analytics tools help organizations connect the data points using pattern recognition models to build risk-based customer profiles.
Transaction monitoring is at the heart of most AML programs, which examine the transactions of existing customers and use matching rules to flag suspicious activity. AML transaction monitoring systems use ML to identify unusual transactions and reduce false positives.
When a new client arrives, the firm must perform due diligence to ensure that the client is not at high risk of money laundering. Today's AML programs assess risks in real time and use data and ML to make timely adjustments to rules to prevent criminals from appearing.
Solutions based on AI models allow for better tracking of customer behavior to timely disrupt suspicious and risky transactions that lead to financial crime. Pattern recognition combined with information from automated data analysis enables corporations to effectively combat financial crime. Suspicious Activity Reports (SARs) reduce false positives in background checks and identify unusual customer transactions.
As mentioned earlier, businesses and financial institutions often use data-driven strategies to learn more about their customers and avoid business risks. FIs are increasingly seeking to comply with regulatory requirements and use data and technology to more cost-effectively identify potential criminal behavior and prevent criminal activity in the first place. And addressing these challenges requires complete and accurate information, and improved data quality will have an immediate impact on the performance of existing monitoring and verification systems.
According to the report, more than half of the expert agencies reviewed are creating richer data environments and increasing their awareness of emerging threats at the sector level by implementing or experimenting with analytics techniques including web scraping, natural language processing, clustering and automatic content categorization.
The data-driven approach involves collecting all the data and then analyzing it, so all the existing web scraping and big data tools are readily available to the KYC world. FIs can make risk-based decisions about how to proceed and adapt quickly to new regulatory requirements. Most importantly, this approach makes it increasingly difficult for criminals to "circumvent" existing policies to find ways to launder money.
Using web scraping provides the latest data to create or enhance tools used to detect money laundering or other financial fraud, such as facial recognition or identity verification programs. Still, the more data there is, the more advanced AML processes will be.
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AI-based technologies help with digital identity verification, AML, and identity transaction monitoring. These solutions verify documents in just seconds to authenticate people's addresses and true identities. The technology captures a document template and runs forgery and photoshop tests to make sure the document hasn't been tampered with. And with optical recognition features, the data can be extracted from the document to verify its authenticity. Plus biometric verification becomes more intelligent with AI, 3D liveness detection can be performed to prevent facial forgery.
Natural Language Processing (NLP) has the ability to read and understand the meaning of important details from text, which will keep businesses compliant and up to date with regulatory changes, as well as keep KYC compliant and limit the activities of unscrupulous individuals.
AI has also automated the data collection process, making the analysis process easier, more cost-effective and efficient. For example, OCR's extraction feature allows users to retrieve customer data from a document rather than writing it manually. Users can upload a photo of the document with the necessary information, and it will be easily extracted using OCR.
Ending money laundering is an important step in the financial industry. However, compliance is simultaneously a burden for most financial institutions.
Big data automation and analytics are easing this burden and improving companies' organizational efficiency. Data and analytics are opening the door to uncovering ways to combat financial crime based on smart data. And advanced AI analytics and cognitive techniques, machine learning, and automation will improve the inefficiency of existing investigative processes.
Are you looking for data to use for anti-money laundering activities? If so, contact us. From KYC to Anti Fraud, we offer reliable solutions for everyone providing financial services and beyond to stay one step ahead of unfair actors.
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1. Make a request
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2. Analysis
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