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