Combatting money laundering has been a vital challenge globally and in India. It is an enormous task and comes with sizeable costs and risks, including but not limited to reputational, regulatory, and financial crime risks. In today’s digital world, we struggle to avoid new financial crime risks owing to old technologies. Managing these risks depends on the guardians of the financial institutions and system. Besides, criminals continue to evolve their financial crime techniques, finding and exploiting vulnerabilities such as loopholes in the financial system to move money.
The magnitude of money laundering and other financial crimes is snowballing, and as mentioned earlier, the techniques to evade detection are more sophisticated than ever. This has evoked a robust response from banks that jointly are investing billions each year to enhance their defenses against financial crime. According to the LexisNexis 2020 report, the banking institutions spent as much as $214 billion, up from $180 billion in 2019, on financial-crime compliance. The resulting regulatory fines related to compliance are increasing each year as regulators impose stern penalties. But the bank’s old-school approaches to financial crimes always seem a step behind the criminals.
Now, banks have the opportunity to get out in front. Recent enhancements in artificial intelligence (AI) and data analytics are helping financial institutions boost their anti-money laundering (AML) programs remarkably, including the transaction monitoring factor of these programs. Now is the time to explore the competence of AI and analytics, enabling a change in AML capability and providing a medium to scale and adapt to modern money laundering threats.
AI in Money Laundering
According to Deloitte’s “How AI is transforming the financial ecosystem 2018” report, the continued growth of artificial intelligence will drastically transform the overall office operations of financial institutions. The report stated that the AI expansion would need adjustments to long-standing regulations and extensive changes to the current scenario of global financial markets.
Banks and technology companies are actively designing and incorporating artificial intelligence solutions and tools to identify suspicious funds transfers or potentially problematic funds movement, to better evaluate high-risk jurisdictions, and to filter the screening of Politically Exposed Persons (PEP) and sanctioned organizations or individuals.
According to Sanction Scanner, an anti-money laundering solutions provider, the compliance teams estimated that between 1% and 2% of AML alerts become the Declaration of Suspicion (DS). AI will be the most transformative, aiding in identifying and deactivating 98% of the false positives cases.
Analytics in Money Laundering
Financial institutions have banked heavily on manual work, humans putting pen to paper in the regulatory reporting process. Unfortunately, this remains a common trend, especially in the case of management workflow. Case investigators physically review details and write disposition reports before reporting compliance obligations and suspicious activity to regulators.
But, with the extensive amounts of data coming in and out of banking institutions, it is not feasible for humans to keep up with demand. Risk alert backlogs are often proliferating more rapidly than operations teams can control.
Listed below are three examples of opportunities for banking institutions to use advanced data and analytic techniques and technologies to enhance customer experience, improve regulatory compliance and lower the operational risk management cost.
Transaction Monitoring (TM)
In AML, machine learning models can improve transaction monitoring alerts and elevate Suspicious Matter Report (SMR) conversion rates, predicting anti-money-laundering scenarios before they occur.
Know Your Customer (KYC)
Organizations must collect, verify, validate, and manage customer data to be KYC compliant, perform the requisite due diligence and allow for relevant investigations or customer risk assessments. By increasing human activity with machine learning techniques, it is possible to have a complete view of the customer and improve the data used to provide a contextual basis for discovering customer risk and detecting suspicious activity.
Analytics can also facilitate customer profiling and segmentation for different businesses, such as marketing and compliance. Using data-driven insights, fighting financial crimes can be more accurate, much faster, and cost less.
As regulatory demand changes rapidly, screening engines cannot match the risk detection capabilities of existing systems because of the pressure on their performance and effectiveness. Along with ensuring the screening engine is operating at top-notch performance with precise data, emerging AI and analytical methods can also manage operational efficiency issues related to a case investigation.
Advanced analytics and cognitive techniques like machine learning, automation, and AI can help segment out false positives and refine existing investigative processes. Data and analytics can boost efficiencies, lower operational costs, and recognize intelligence-led and data-backed ways to tackle financial crime.
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