Navigating the digital frontier, financial institutions are leveraging AI and machine learning to combat evolving fraud risks and ensure compliance with stringent regulations.
Digitalization has enabled users to access their accounts and move funds digitally in a fast and convenient manner. However, this has also opened up different possible ways for fraudsters to access and use the financial system remotely and transfer their illegal funds or laundered money across various accounts, and in different jurisdictions.
Financial institutions across the world are struggling to account for the fraud risks and detect fraud incidents or attempts posed by digitalization.
The evolving fraud risk indicators need to be identified by fraud risk management specialists and included in the digital fraud scenarios to assess the fraud risk’s significance and impacts on the operations of the institution.
Fraud mitigation efforts are then required to consider all possible digital fraud scenarios to develop relevant processes and internal anti-fraud controls that enable the prevention of fraud risks and compliance with anti-financial crime compliance and anti-fraud laws and regulations.
Machine learning (ML) and AI technologies may enable institutions to improve the fraud detection process and reduce the chances of occurrence of apparent and hidden fraud risks.
AI and ML technology may help in customer risk profiling based on the study of available customer datasets, including past relationship behavior, fraudulent attempts, jurisdiction, nature of business, source of income, customers’ residence, sources of income, beneficial owner, negative media, social proofs, etc. The fraud risk score is assigned to customers’ accounts based on their perceived level of fraud risks identified by using AI and ML algorithms to deeply study the hidden data patterns and transaction links.
The AI and ML technology may account for some parameters that may not vary over time, so customers, employees, vendors, suppliers, or stakeholders, may remain in the same band of fraud risk score irrespective of their current transactions and history.
Using AI and ML technologies the institutions can define fraud risk scores at the time of onboarding customers and performing due diligence and know your customer practices. The use of AI and ML identifies high-risk customers and their transactions and deeply analyses the nature, purpose, and other details to ensure that any fraudulent transaction is identified on a real-time basis and escalated for enhanced review by fraud risk specialists.
Such high-risk customers and transactions include politically exposed persons (PEPs), and transactions with high-risk jurisdictions. Further, the use of ML and AI may reduce the risks of defining irrelevant transaction scenarios, fraud risk scenarios, fraud risk indicators, etc. that reduce the generation of excessive false positives.
Navigating the Digital Frontier
The intrinsic risk is mainly captured through the customer’s transaction or his or her non-transactional attributes that are determined through customer data files, past transaction alert data, past reported financial crime-related activities and other risk characteristics.
The AML requirements put more focus on explaining the rationale behind the customer risk profiling and rating performed by financial institutions. To justify the customer risk rating or profiling, financial institutions need to adopt AI-based regulatory compliance technology to ensure a spontaneous or real-time customer risk profile or rating.
Customer due diligence (CDD) including enhanced due diligence (EDD), would be applied using AI and ML-based techniques for changing customer risk rating and making the AML monitoring process more effective, as per regulatory requirements.
The use of AI and ML enables a dynamic ongoing review of customers’ and employees’ behavior and activities and adjusts the fraud risk scores and profile risks on a real-time basis. This dynamic ongoing review and adjustment of risk profiles need real-time and current data which usually is a challenge for most of the institutions to develop and consistently maintain.
For example, the institutions may not keep regular track of their customer’s businesses and income levels, which makes the customers’ data irrelevant to be used by AI and ML algorithms for fraud detection or revision of customers, vendors, suppliers, and employees’ risk profiles.
Increasing regulatory pressure put on institutions to use more AI-based statistical compliance procedures such as in the Bank Secrecy Act (BSA), the AML compliance is pushing institutions to replace rule-based heuristic CRR approach with a well-established, and statistically based CRR model.
The feature engineering process requires making ML algorithms work. ML algorithms with smart features yield accurate outcomes, and feature engineering may bring mathematical value to subjective customer data knowledge. For example, a network of accounts with suspicious cases may be defined objectively in the feature engineering step. Many features may be created based on evolving financial crime typologies.
Artificial Neural Network (ANN) is a supervised deep-learning technique to unearth patterns in the available data. It may update the scores and weights coefficients on its own. ANN is a powerful technique to learn complex, non-linear relationships and provides accurate results.
Clustering is an unsupervised ML technique that may help discover natural groupings in data patterns. It may be used to define risk banding based on the characteristics of the cluster and distribution of the existing customer risk segment.
Statistical machine learning-based models are founded on well-established statistical methodologies and approaches that have been vetted, reviewed, and published in academic journals. Most of the statistical models that financial firms use for CRR are predictive, such as linear regression, binary or ordinal logistic regression, decision trees, and neural networks. The application and risk rating objectives determine the model that the firm selects.
In the dynamic fraud risk scoring and rating framework, the AI and ML algorithms learn over time. It increases the risk score of customers whose activities it perceives to be abnormal and minimizes the customer risk score for those who show risky activities or behavior but in a one-off transaction or scenario.
Final Thoughts
Digitalization has indisputably revolutionized the financial world, making transactions swifter and more user-centric. However, this very progress has paved the way for a new breed of sophisticated fraudsters who exploit these digital avenues, thereby challenging financial institutions globally. As institutions grapple with these evolving digital fraud risks, there’s a palpable urgency to enhance fraud risk management, necessitating updated methodologies that encompass all potential digital fraud scenarios. Enter Machine Learning (ML) and Artificial Intelligence (AI); these technologies are at the forefront of transforming fraud detection, offering intricate customer risk profiling by delving deep into vast datasets.
Yet, while AI and ML can discern hidden patterns and connections in transactions, they also come with their set of challenges. The effectiveness of these technologies is contingent upon the relevance and currency of the data they access. Moreover, as regulators intensify their focus on justifying risk profiling, adopting AI-centric compliance technology is no longer optional. Institutions must pivot from traditional rule-based systems to dynamic, data-driven models, leaning on a gamut of techniques from Artificial Neural Networks (ANN) to clustering. Only through such a holistic and adaptable approach can institutions maintain a step ahead of digital fraudsters in this ever-evolving landscape.
