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Financial transactions rely heavily on trust, as consumers increasingly seek convenient ways to engage financially and enhance inclusion for the estimated 2 billion unbanked individuals worldwide. However, the surge in digital transformation, a rising number of active internet users, and the emergence of web3 technologies have led to a significant uptick in financial transactions across various channels. Unfortunately, this growth has also attracted malicious actors looking to exploit the trust consumers place in their preferred transaction methods. According to PWC’s latest global economic crime and fraud survey, external perpetrators were responsible for 70% of disruptive fraud incidents in organizations. This alarming trend saw hackers and organized crime as the top offenders, in contrast to previous years when customers and vendors were more frequently implicated.
Financial service institutions (FSIs) have a critical responsibility to safeguard their customers’ transactions. Yet, traditional methods of combating fraud and financial crime remain largely reactive, identifying issues only after a transaction has occurred. This often necessitates manual interventions to investigate and recover funds, as compliance operations prioritize deploying artificial intelligence (AI), automation, and analytical technologies to detect incidents rather than supporting downstream investigation and reporting.
To effectively combat fraud and financial crime, FSIs must adopt a comprehensive approach that involves managing and analyzing both real-time and historical data, applying machine learning (ML) techniques, and utilizing scalable architecture and infrastructure.
In this article, we will explore the challenges FSIs face, innovative strategies being implemented to enhance fraud and financial crime protections, and the AWS services that assist organizations in their pursuit of real-time defense.
Current Challenges
FSIs grapple with several challenges, including:
- The increasing prevalence and sophistication of criminal behavior, undermining the effectiveness of rules-based defenses;
- Rising data volumes without corresponding improvements in data quality;
- Escalating costs associated with continuous compliance; and
- Heightened customer expectations for quick and secure transactions.
Addressing these multifaceted challenges requires a holistic strategy focused on minimizing the feedback loop from threat detection to response.
Rules-based systems for detecting suspicious transactions can backfire, introducing unnecessary friction into the customer experience. Consequently, FSIs are actively seeking ML solutions for behavioral analysis, threat detection, and reduction of false positives. However, these efforts face hurdles due to the growing volumes of data that must be collected, cleaned, and prepared for analysis. IDC predicts that by 2025, over 175 zettabytes (175 trillion gigabytes) of data will be generated annually, leaving FSIs struggling to harness the value of this data in their fight against fraud and financial crime.
A comprehensive profile of the customer, essential for identifying suspicious activity, necessitates growing volumes of data. Notably, PWC’s global economic crime and fraud survey indicates that 52% of fraud is committed by individuals within the organization. Therefore, it is crucial to consider not just the customer’s transaction data but also related factors such as credit or claims history, any past convictions, transactions with other financial service providers, interaction history with service agents, and the relationships between customers and FSI employees.
In 2021, FSIs spent approximately $213.9 billion on financial crime compliance amid increasing regulatory pressures aimed at combating money laundering, drug trafficking, terrorist financing, and other financial crimes. The rapid growth of mobile banking during the pandemic also led to a staggering 250% increase in attempted online banking fraud. This combination of factors has heightened enterprise risk and raised IT expenditures for FSIs in their efforts to counteract growing fraud losses and compliance costs.
As digital wallets and real-time payment systems gain traction, FSIs must adapt. McKinsey reports that over 56 countries now boast active real-time payment systems—an increase from just six countries six years ago. A batch-driven fraud detection strategy is misaligned with the demand for instant transactions, further exacerbating compliance costs for FSIs.
Solutions
Strategies Adopted by FSIs
ML Augmentation
Business rules typically focus on specific conditions or behaviors and are often straightforward to explain and validate. To address the challenge of rapidly evolving criminal behaviors, FSIs are integrating ML models into their detection systems. Supervised learning models can recognize broader patterns through numerous historical examples, enabling them to identify suspicious activities even when fraudsters make minor adjustments. These ML models not only excel at identifying risky patterns but are also more resilient than traditional rules. They can be deployed downstream to triage alerts and flag false positives, allowing organizations to allocate specialist resources more effectively towards high-priority cases.
Real-time, All the Time
The ML augmentation strategy is often hindered by batch-based components that don’t support real-time integration. FSIs are now implementing real-time detection and response systems that balance the friction introduced into customer journeys, such as onboarding and payments. By leveraging streaming data and scalable data lake architectures, FSIs can detect fraud and data errors earlier in the process, often within the interaction data stream itself. This proactive approach enables organizations to flag suspicious interactions before customers are even aware, thereby minimizing fraud losses and steering customer communications toward proactive measures, such as password changes or card replacements.
How FSIs are Implementing This on AWS
Typically, FSIs must evaluate whether to build or purchase solutions that support their fraud and financial crime mitigation strategies. With cloud technology, organizations can choose to either build using cloud services, purchase software as a service, or adopt a hybrid approach based on their unique circumstances.
In light of the challenges posed by rapidly changing criminal behavior and the need to process increasing data volumes, organizations are employing analytics and ML to develop solutions that adapt to new behaviors as data becomes available. They utilize a rich array of AWS services, such as Amazon SageMaker, which provides notebooks, built-in algorithms, and tools for augmenting or creating new solutions. This post demonstrates how to use SageMaker alongside the Deep Graph Library (DGL) to train graph neural network models for identifying malicious users or fraudulent activities.
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