financial technology

Translating Smart Investments to Improved Governance and Customer Relationships

Anti-money laundering regulations (AML), including Know Your Customer (KYC) requirements, are hardly a new concept in the world of financial services. Over the years, financial institutions have largely mastered the mechanics of compliance. There is, however, significant opportunity for improvement, especially when it comes to boosting accuracy, efficiency and productivity. Banks that embrace innovation around their AML strategies stand to leapfrog the competition when it comes to stronger performance, growth and customer relationships.

Here are the trends shaping today’s AML reality.

#1: False positives continue to grow ‒ escalating the need for skilled resources and eroding confidence in data accuracy and screening processes.

Industry statistics peg the false-positive rate for suspicious activity alerts between 75 percent and 90 percent. The 2016 Dow Jones and Association of Certified Anti-Money Laundering Specialists (ACAMS) Global Anti-Money Laundering Survey found that 55 percent of alerts take more than 5 minutes to close, 30 percent take more than 30 minutes, and 10 percent take more than an hour. Consider the impact when managing thousands of alerts each month. Just as important, high volumes of false positives can erode organizational confidence in the data and client screening processes and actually make it more likely that a bank will miss a valid alert in the deluge of false positives. There are tremendous opportunities and upsides to working smarter and faster when it comes to AML processes ‒ including lower costs and better outcomes. It all starts with tackling the false positive obstacle.

#2: “De-risking” gains momentum as a strategy for avoiding the penalties and prosecution associated with AML noncompliance.

This approach appears to simplify the customer due diligence process by denying banking services to entire business segments. In reality, it results in lost revenue from legitimate businesses in a specific segment. Seeking to discourage this practice, the Financial Action Task Force (FATF) on Money Laundering advocates a risk-based approach that encompasses a case-by-case assessment and robust initial customer due diligence (CDD) measures that include identity verification using reliable and independent data and documentation, along with screening against various watch lists. Data analysis and reporting transparency are vital, as well, to document end-to-end payment flows.

#3: The regulatory landscape continues to evolve and expand.

Change is the only constant when it comes to the regulatory environment, and AML is no exception. We see the introduction of more prescriptive requirements that call for expanded ongoing monitoring and analysis.

#4: Current AML processes impair productivity and customer relationships.

A 2016 Thomson Reuters study found that 89 percent of corporate banking customers have not had a good KYC experience, and 13 percent had switched banks as a result ‒ a hit to the bottom line. Financial institutions that take aim at accelerating AML processes through additional automation and next-generation analysis stand to boost productivity and strengthen relationships through faster on-boarding.

#5: Legacy AML solutions present an obstacle to efficiency and growth.

AML point solutions continue to proliferate in today’s financial institutions. Firms are realizing that, while their legacy systems enable them to meet regulatory requirements, these same systems are holding them back, including discouraging expansion into new geographic areas. This patchwork of point solutions is difficult and expensive to manage and maintain, cannot accommodate all the data formats in today’s modern institutions and does not enable vital enterprisewide visibility. Multiple solutions also add to the complexity of entering a new geographic market.

#6: Time is of the essence – and machines need to help with AML.

Now more than ever, financial institutions are light on time and process heavy. The advent of machine learning has presented a novel approach to identifying and evaluating AML violations while freeing time up for human labor to deal with more complex tasks. The algorithms behind machine learning develop an understanding of which flagged transactions have a proclivity toward being true positive AML violations. These machine learning neural nets will save companies money and better allocate resources for years to come.

Opportunity Awaits

Financial institutions are beginning to take a careful look at their legacy AML systems with an eye toward expanding automation, improving performance, standardizing processes, increasing transparency and addressing false positives.

Considerations should include:

  • A unified platform for enterprisewide risk-based monitoring, investigations and reporting for suspicious activities.
  • A focus on data quality, leveraging a layered approach to ensure that a firm can truly identify a single customer across all lines of business and integrate both internal and external data ‒ structured and unstructured and in Latin and non-Latin character formats ‒ into the system.
  • An open data model to facilitate integration of a wide range of data from third-party systems, including criminal records, white lists and black lists, not only identifying customers on those lists but also determining when a customer might be transacting with an individual on a watch list.
  • A comprehensive, transparent behavior detection library and robust case management capabilities to streamline analysis, resolution and regulatory reporting in a single, unified platform.
  • Automated analytics to improve alert accuracy and reduce compliance costs. With regulators giving more scrutiny to the methodologies behind AML compliance programs, firms must also be equipped to defend the models they use to automate analysis. That being the case, firms seek solutions with models and algorithms that have proven themselves in the market.
  • Ability to manage global, multinational regulations, guidelines and best practices from a single solution.

Firms that invest wisely can create a compliance environment that meets immediate and future AML requirements and serves as a platform for improving overall governance and risk management – while fostering stronger customer relationships and improved performance.

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Ambreesh Khanna

Ambreesh Khanna is Group Vice President and General Manager of Oracle Financial Services Analytical Applications (OFSAA). Prior to this, he was Vice President, OFSAA Product Management, where he managed a global team of senior product managers responsible for the suite of applications. Prior to the Oracle acquisition of Sun Microsystems, Ambreesh managed Sun’s North America Financial Services Sales and was the Global Director of the Industry, Partners and Architecture team.

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