AI and Machine Learning Can Repurpose Humans, Not Replace Them

By Nina Kerkez

The financial crime prevention industry has seen an increase in regulation driven by the escalation of criminal behavior in recent years. Global money laundering schemes, complex cross-border transactions and overlapping regulations complicate financial crimes enforcement just as the realities of fraud schemes and terrorist financing call for faster responses. For banks, technology is key to enabling that acceleration.

Recent research from Accuity indicates that banks struggle with the time required for staff to perform manual repetitive compliance tasks. Labor still accounts for the largest cost of compliance functions. Management tends to view compliance functions as cost centers, rather than profit generators, while regulatory changes drive bank behavior.

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Meanwhile, emerging technologies like artificial intelligence, machine learning, blockchain, big data and behavioral analytics are making their way into different operations within banks. The science is already here, and some banks have already implemented these technologies into their processes, while others think of these developments as futuristic. Challenger banks are particularly effective in building such technologies into their compliance processes as a part of their customer onboarding function due to the lack of legacy technology requiring backward compatibility and systems integration.

AI, ML, big data and data analytics in financial crime operations

Most people today understand the power of a data-driven approach when it comes to their recent online shopping behavior: we purchase goods online and our preferred vendor will offer us previously bought goods that might complement our purchase. The more data this algorithm has, the easier it can make predictions and recommendations for users. This algorithm does not care about why the associations between recommendations exist; it only cares that they do. Industry compliance analysts will care to know why.

Banks are focusing their efforts on introducing new technologies into their processes. The regulatory compliance market is excited about them. Here are four main areas of exploration:

1. AI and data analytics enable efficiencies for banks

The first thing that comes to mind when we talk about AI is the efficiency that such technology can drive. Industry reports discuss introducing efficiencies into the process by doing the same things better or faster, rather than introducing radical changes into strategic initiatives. The World Economic Forum illustrates that opportunities for driving efficiency are abundant, from deposits and lending, to insurance, capital markets, payments and other sectors.

Banks have been able to digitize as much as 90 percent of their processes through the automation of manual work, allowing human agents to focus on key decisions. AI can help standardize time-consuming tasks and make them more efficient: banks have introduced robotic process automation, text analytics, insights and entity resolution and network analysis into compliance for some time. AI and ML have the potential to enhance the efficiency of information processing and to reduce information asymmetries.

Data analytics coupled with AI can also help banks create more efficient and smarter organizations. banks can get ahead of market changes and regulatory forces through the utilization of such technologies, which will drive business strategy and results while using automation to handle huge volumes of data.

2. AI and data analytics cuts costs and saves on labor

Numerous banks in the market are exploring the option of utilizing new technologies as a labor-saving innovation due to the extremely high costs associated with compliance staff. A report LexisNexis Risk Solutions published this year estimates that labor accounts for 57 percent of costs for banks globally. Compliance functions within banks continue to be cost centers and the costs of being compliant increase annually. The entire organization benefits when banks eliminate human-performed, menial and repetitive tasks. Employers can and should repurpose their staff for more strategic and comprehensive tasks.

3. Risk reduction through pattern detection

These technologies are a significant part of the solution to preventing money laundering, fraud and terrorist financing—and helping banks improve their risk appetite. Banks constantly discuss risk management, specifically linked to consumer behavior pattern detection.

These technologies can help spot patterns in behaviors, which will later highlight anomalies in transactions to indicate potential fraud or money laundering risks. AI can parse the masses of unstructured data to separate the signal from the noise. Machines can learn to observe patterns from the past and predict how these patterns might repeat.

Banks should be careful about letting these technologies self-correct, though. AI, data analytics and ML-based tools can miss new types of risk that arise if they are over-trained on previous events.

4. AI and data analytics add strategic value

Organizations still struggle to select the right areas to use these new technologies. We have only seen marginal improvements on existing data processing to date.

U.S. banks operate in a highly digital environment. Online and mobile transaction volumes and new account openings have increased. This leaves banks with a vast amount of data to use in connecting more strategically with their customers. New technologies can help organizations scale the data they have; use compliance data gathered on an ongoing basis to help with tailored experiences for their customers; retain their customers; and augment bank performance.

Responsibly implementing AI

AI is pulling banks in many directions, making it difficult for them to apply AI, big data and data analytics. Banks must be clear on their investments and activities and separate their marketing story from the implementation story.

Given recent changes in regulatory requirements for data privacy, data protection is coming to the forefront. New systems that utilize AI, big data and data analytics must immediately take into account whether certain data is subject to privacy rules before it banks use it to train the system for compliance purposes. It is critical to distinguish between guidance and regulated advice. There is a fine line between the two when it comes to money laundering prevention.

Banks and corporations must absorb the rising operational costs for match remediation as the volume of alerts, transactions and list entities grows. Continually adding compliance staff only treats the symptom—too many false positives—and is not a practical long-term strategy.

Too much time spent remediating matches and distinguishing false positives from relevant risk drains resources and diverts attention from your core mission. Banks can take an innovative approach to efficient compliance by dramatically reducing the number of matches that require manual review, automatically remediating a significant percentage of matches found through PEP, adverse media and sanction screening. These institutions can implement new technologies and significantly improve both processes and outcomes in the compliance department. Taking action now puts banks on the front foot.

Nina Kerkez is director of financial crime compliance at LexisNexis Risk Solutions.