COMPLIANCE
Using data analytics to identify AML risk
Data is the lifeblood of financial institutions and other organizations . It is used to run processes , manage financials , predict risk , prove compliance , target customers and influence decisions . In anti-money laundering ( AML ) and compliance , the data required to identify and combat financial crime is complex . It is also difficult to gather because data is often stored across a patchwork of legacy systems , new systems and siloed business-specific applications . Data quality can vary greatly . Working with unreliable , incomplete or inconsistent data makes it difficult to identify bad actors who pose a financial and reputational threat , which undermines an institution ’ s ability to efficiently manage risk across the enterprise .
The more complex and geographically diverse a financial institution is , the greater the threat . Institutions with very large customer databases and transaction volumes that span numerous distribution channels and counterparties face the greatest number of challenges and the most risk .
The slinging arrows of risk
Traditional AML defenses that rely primarily on static rules to identify questionable individuals and activities are coming up short . Despite continued investment in AML technology and processes , institutions seem unable to keep pace with external threats from drug cartels , corrupt public officials , terrorist organizations and other bad actors that have developed increasingly sophisticated tactics to avoid detection . These savvy criminals know how to play the game . They will often cloak their malicious activities by keeping within the defined set of rules . One example of how criminals fly under the radar is through smurfing . By limiting transactions to under $ 10,000 , they avoid triggering a currency transaction report .
Internal threats — whether unintentional human error or intentional fraud — must also be considered when managing enterprise risk . In addition to internal and external threats , emerging payment technologies and the digitalization of banking have introduced yet another set of risks to the AML landscape .
Internal threats — whether unintentional human error or intentional fraud — must also be considered when managing enterprise risk
Cyber risk , social media monitoring and data management are all crucial considerations that have caught the attention of regulators , who have come to recognize that traditional , rules-based methodologies may not be optimal for certain typologies . Check-the-box compliance is not enough . Regulators expect banks to have defensive processes and systems in place to proactively seek out and catch perpetrators , whether external players or internal employees .
Big data , big challenges
Know your customer ( KYC ) regulatory requirements have compelled institutions to collect increasing amounts of data on customers and their transactions . Static , rules-based systems are not designed to handle huge stores of unstructured , internet-scale data . As a result , they produce an enormous volume of false positive alerts . More data only produces more false positives when screening for sanctioned entities or money laundering .
Managing the deluge of false positive alerts is a major pain point for many institutions . Not only is the process inefficient and operationally expensive , but it complicates an institution ’ s ability to quickly and accurately identify risk . The knee-jerk reaction of “ throwing more bodies ” at the problem is not the answer . Adding resources just drives up the cost of compliance and increases the risk of human error .
Driving change
The big data phenomena brought a proliferation of technology that can help meet the analytic and architecture challenges of AML , KYC and counter-terrorist financing . Data science , data analytics and other advanced technologies like artificial intelligence ( AI ) and machine learning offer a dynamic approach that is better suited to complex internet-scale data than static models . According to a report published by Celent , “ AI-enabled solutions can not only
48 ACAMS TODAY | SEPTEMBER – NOVEMBER 2017 | ACAMS . ORG | ACAMSTODAY . ORG