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The Role of Artificial Intelligence in Financial Compliance and Fraud Detection

Source: Finance Derivative

By Dr. Jochen Papenbrock, Head of Financial Technology, EMEA, NVIDIA, and Prabhu Ramamoorthy, Partner Developer Relationship Manager, Financial Services, NVIDIA

Financial institutions globally, including banks, are subject to the Financial Action Task Force (FATF) for combating financial crime, terrorist financing, and preventing money laundering. Artificial intelligence (AI) is increasingly being used to fight financial crime  and offers new opportunities to help detect and prevent fraud. AI and associated machine learning (ML) or deep learning (DL) models provide bank case managers, regulators and compliance officers with powerful new capabilities.

AI is the capability of a computer program, referred to as a machine, to think and learn and take actions without being explicitly encoded with commands. AI can be thought of as the development of computer systems that can perform tasks autonomously, ingesting and analyzing enormous volumes of data, then recognizing patterns in that data. ML, a subset of AI, is the practice of using algorithms to parse data, learn from it, and then determine  or predict next steps. Improving on the traditional rules-based approach or the hand-coding of software routines with a specific set of instructions to accomplish a particular task,  AI algorithms are trained using large amounts of data, enabling them to learn a task. DL is a subset of machine learning, with the difference that DL algorithms can automatically learn representations from data such as images, video, or text, without introducing human domain knowledge. The word “deep” in deep learning represents the many layers of algorithms, or neural networks, that are used to recognize patterns in data. DL’s highly flexible architectures can learn directly from raw data, similar to the way the human brain operates, and can increase their predictive accuracy when provided with more data

The easiest way to think of their relationship is to visualize them as subsets within overlapping AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.

Why is AI an Effective Tool?

AI models and algorithms can consume and synthesize massive volumes of data. Furthermore, AI can ingest the data and act on it in near-real time, enabling authorities to stay in step with the movements of bad actors rather than remaining days or weeks behind.

AI models are designed to detect anomalies in the patterns of data they are ingesting by scoring those behaviors relative to expected benchmarks, so that banking compliance officers are alerted when potentially nefarious interactions occur. The investigations tied to these alerts are often led by compliance personnel within banks, and are therefore time-consuming and costly.

Rules- vs. Model-based Approaches to Combating Money Laundering

Money laundering is a process that criminals use to hide the illegal source of their funds. By passing money through multiple, sometimes complex, transfers and transactions, the money is “cleaned” of its illegitimate origin and made to appear as legitimate business profits.

Technological advances in digital banking, online account opening, open banking and cryptocurrency have made tracking the source of funds and uncovering suspect patterns and behaviors far more resource-intensive for financial institutions and their regulators. Traditional methods of automation are unable to keep up with the increasingly sophisticated ways the financial system is abused, so the FATF has encouraged the digital transformation of Anti-Money Laundering (AML) and Counter-Terrorist Financing (CTF) efforts.

Legacy rules-based AML systems have a false positive rate > 90%, meaning investigators’ valuable time is wasted on the wrong transactions. These approaches rely on databases of human-engineered rules that are used to spot patterns indicative of fraud. Figure 2 illustrates a rules-based approach to identifying suspicious financial transactions. Here, a large set of rules is defined and then applied to all financial transactions. If a transaction matches any of the rules, an alert is triggered.If the alert is incorrectly triggered (false positive), it incurs a cost. If no alert was triggered, but one should have been (false negative), a new rule is designed. But fraudsters are becoming savvy at avoiding patterns that rules-based systems can easily recognize.

Many financial institutions are therefore transitioning to model-based systems that reduce false positives by more than 60% and increase anomaly detection by 200%. Using historical actual or suspected financial crime data, the machine learns what is considered normal and suspicious behavior and predicts the risk of money laundering at a more accurate rate, reducing detection from hours to milliseconds and stopping crime in its tracks. Rather than looking for patterns that exactly match pre-defined rules, model-based systems can learn to generalize and identify new fraud schemes that might be new interpretations of old ones. This makes it harder for criminals to avoid detection. They are no longer able to make small adjustments to get around a relatively static set of rules.

In addition to this, graph neural networks (GNNs) can be used by investigators to evaluate relationships between any number of parties to flag potential money laundering behavior. The concept is to construct a heterogeneous graph from tabular data and train a model to detect suspicious transactions and complex laundering activities, as criminals work collaboratively in groups to hide their abnormal features but leave some traces of relationships.

Generative AI and Large Language Models in Fraud Detection and Prevention

ChatGPT was the iPhone moment for AI, and brought Generative AI to the forefront of public discourse. In the financial services industry, Generative AI will play a key role in fraud detection.

Large Language Models (LLMs) can be useful in fraud detection because they can retrieve and analyze information from large unstructured data sources in a short timeframe. Unstructured data can be news, social media or internal organization-generated data in the form of contracts, onboarding documents, audio calls, satellite imagery, trade documents, and invoices/payments. The models can identify and connect different legal entities (companies, individuals, etc.) with associated actions, understand that context, and incorporate this complex information into an information retrieval algorithm for identifying suspicious activities.

Another fraud detection application of LLMs is to monitor conversations, chats and trade activities. Deep Neural Network (DNN) models can analyze the data, draw conclusions and trigger alerts to compliance officers.

Software and Hardware Needs for AI

The implementation of Generative AI requires dedicated hardware and software. On the hardware side, an AI acceleration platform – GPU accelerated compute, accelerated software libraries, data, and storage – is needed for unstructured and semi-structured data models. On the software side, AI frameworks and data scientist resources to code and deploy solutions at scale are needed.

Accelerated platforms make it possible to process several tasks simultaneously in a significantly shorter time. Before accelerated computing, unstructured and semi-structured language models took weeks or months to train, with results also taking time to be returned. Today, Generative AI LLMs and GNNs can be trained in hours or days, and their results returned in milliseconds. As the amount of data, including transactions and related contracts to those transactions, grows exponentially, more and more advanced models are trained. It is not only the models but also the data processing needed for the models that need to be accelerated to process large volumes of unstructured, tabular data to be useful for real time fraud applications. This is why, in turn, state-of-the-art computational accelerators such as NVIDIA are needed with integrated hardware and software solutions for end-end analytics and ML/AI pipelines.

AI is Here to Stay

Financial regulators, bank executives, and risk and compliance officers are prioritizing an investment in AI for financial compliance and fraud detection. AI technologies can help by analyzing large amounts of data and using advanced algorithms to identify suspicious activity. AI can adapt to changes without human intervention catching fraudsters in the act of committing financial crimes. For a successful implementation, financial institutions typically rely on a combination of models for use cases in different stages – for example, Generative AI and LLMs for unstructured data analysis, and ML and Graph Neural Networks for analyzing and visualizing transactions.

AI is here to stay and will continue to unlock opportunities for the financial services industry. Beyond its use in preventing  financial crime, firms  are leveraging AI to create more relevant customer experiences,  improve risk management, drive operational efficiency, and deliver more value to customers.

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Business

What can the West learn from the Arabian Gulf’s payments revolution?

Hassan Zebdeh, Financial Crime Advisor at Eastnets

A decade ago, paying for coffee at a small café in Riyadh meant fumbling with cash – or, at best, handing over a plastic card. Today, locals casually wave smartphones over terminals, instantly settling the bill, splitting it among friends, and even transferring money abroad before their drink cools.

This seemingly trivial scene illustrates a profound truth: while the West debates incremental upgrades to ageing payment systems, the Arabian Gulf has leapfrogged straight into the future. As of late 2024, Saudi Arabia achieved a remarkable 98% adoption rate for contactless payments in face-to-face transactions, a significant leap from just 4% in 2017.

Align financial transformation with a bold national vision

One milestone that exemplifies the Gulf’s approach is Saudi Arabia’s launch of its first Swift Service Bureau. While not the first SSB worldwide, its presence in the Kingdom underscores a broader theme: rather than rely on piecemeal upgrades to older infrastructure, Saudi Arabia chose a proven yet modern route, aligned to Vision 2030, to unify international payment standards, enhance security, and reduce operational overhead.

And it matters, because in a region heavily reliant on expatriate workers whose steady stream of remittances powers whole economies. The stakes for frictionless cross-border transactions are unusually high. Rather than tinkering around the edges of an ageing system, Saudi Arabia opted for a bold and coherent solution, deliberately aligning national pride and purpose with practical financial innovation. It’s a reminder that infrastructure, at its best, doesn’t merely enable transactions; it reshapes how people imagine the future.

Make regulation a launchpad, not a bottleneck

Regulation often carries the reputation of an overprotective parent – necessary, perhaps, but tiresome, cautious to a fault, and prone to slowing progress rather than enabling it. It’s the bureaucratic equivalent of wrapping every new idea in bubble wrap and paperwork. Yet Bahrain has managed something rare: flipping the narrative entirely. Instead of acting solely as gatekeepers, Bahraini regulators decided to become collaborators. Their fintech sandbox isn’t merely a regulatory innovation; it’s psychological brilliance, transforming a potentially adversarial relationship into a partnership

Within this curated environment, fintech firms have launched practical experiments with striking results. Take Tarabut Gateway, which pioneered open banking APIs, reshaping how banks and customers interact. Rain, a cryptocurrency exchange, tested compliance frameworks safely, quickly becoming one of the Gulf’s trusted crypto players. Elsewhere, startups trialled AI-driven identity verification and seamless cross-border payments, all under the watchful yet adaptive guidance of Bahraini regulators. Successes were rapidly scaled; failures offered immediate lessons, free from damaging legal fallout. Bahrain proves regulation, thoughtfully applied, can genuinely empower innovation rather than restrict it.

Prioritise cross-border interoperability and unified standards

Cross-border payments have long been a maddening puzzle – expensive, sluggish, and unpredictably complicated. Most Western banks seem resigned to this reality, treating the spaghetti-like mess of correspondent banking relationships as a necessary evil. Yet Gulf states looked at this same complexity and saw not just inconvenience, but opportunity. Instead of battling against the tide, they cleverly redirected it, embracing standards like ISO 20022, which neatly streamline data exchange and slash friction from global transactions.

Examples abound: Saudi Arabia’s adoption of ISO 20022 through its Swift Service Bureau will notably accelerated cross-border transactions and improve transparency. The UAE and Saudi Arabia also jointly piloted Project Aber, a digital currency initiative that significantly reduced settlement times for interbank payments. Similarly, Bahrain’s collaboration with fintechs has simplified previously burdensome remittance processes, reducing both cost and complexity.

Target digital ecosystems for financial inclusion

One of the most intriguing elements of the Gulf’s payments transformation is the speed and enthusiasm with which consumers embraced new technologies. In Bahrain, mobile wallet payments surged by 196% in 2021, contributing to a nearly 50% year-over-year increase in digital payment volumes. Similarly, Saudi Arabia experienced a near tripling of mobile payment volumes in the same year, with mobile transactions accounting for 35% of all payments. 

The West, by contrast, still struggles with financial inclusion. In the U.S., millions remain unbanked or underbanked, held back by distrust, geographic isolation, and high fees. Digital solutions exist, but widespread adoption has lagged, partly because major institutions view inclusion as a long-term aspiration rather than an immediate priority. The Gulf shows that when digital tools are made integral to daily life, rather than optional extras, the barriers to financial inclusion quickly dissolve.

The road ahead

As the Gulf region continues to refine its payment systems experimenting with digital currencies, advanced data protection laws, and AI-driven compliance the ripple effects will be felt far beyond the GCC. Western players can treat these developments as an external threat or as a chance to rejuvenate their own approaches.

Ultimately, if you want a glimpse of where financial services may be headed towards integrated platforms, real-time international transactions, and widespread digital inclusion – the Gulf experience is a prime example of what’s possible. The question is whether other markets will step up, follow suit, and even surpass these achievements. With global financial landscapes evolving at record speed, hesitation carries its own risks. The Arabian Gulf has shown that bold bets can pay off; perhaps that’s the most enduring lesson for the West.

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Business

Unlocking business growth with efficient finance operations

Rob Israch, President at Tipalti

The UK economy has faced a turbulent couple of years, meaning now more than ever, businesses need to stay agile. With Reeves’s national insurance hikes now fully in play and global trade tensions casting a shadow over the landscape, the coming months will present a crucial opportunity for businesses to decide how to best move forward. 

That said, it’s not all doom and gloom. The latest official figures show that the UK’s economy unexpectedly grew at a rate of 0.5% in February – a welcome sign of resilience. But turning this momentum into sustainable growth will hinge on effective financial management – essential for long term success.

Although many are currently prioritising stability, sustainable growth is still within reach with the right approach. By making use of data and insights from the finance team, companies can pinpoint efficient paths to expansion. However, this relies on having real-time information at their fingertips to support agile, well-timed decisions.

While achieving growth may be tough to come by this year, businesses can stay on track by adopting a few essential strategies. 

Improving efficiency by eliminating finance bottlenecks

Growth is the ultimate goal for any business, but it must be managed carefully to ensure long-term sustainability. Uncertain times present an opportunity to eliminate inefficiencies and build a strong foundation for future success.

A significant bottleneck for many businesses is the finance function’s reliance on manual processes for invoice processing, reporting and reconciliation. These tasks are not only time-consuming but also introduce errors, delays and inefficiencies. As a result, finance teams become stretched thin. Our recent survey found that, on average, over half (51%) of accounts payable time is spent on manual tasks – severely limiting finance leaders’ ability to drive strategic growth.

Repetitive tasks such as data entry, reconciliation, and approvals require considerable time and effort, slowing down decision-making and increasing the risk of inaccuracies. Given the critical role that finance plays in guiding business strategy, these inefficiencies and errors create significant roadblocks to growth.  

The pressure on finance leaders is therefore immense and while 71% of UK business leaders believe CFOs should take a central role in corporate growth initiatives, they are simply lost in a sea of manual processes and number crunching. In fact, 82% of finance leaders admit that excessive manual finance processes are hindering their organisation’s growth plans for the year ahead. To remedy this, businesses must embrace automation.

Achieving sustainable growth with automation

By replacing manual spreadsheets with automated solutions, finance teams can eliminate administrative burdens and focus on strategic initiatives. Automation simplifies critical finance tasks like bank feeds, coding bookkeeping transactions and invoice matching. Beyond this, it can also help alleviate the strain of more complex and time-intensive responsibilities, including tax filings, invoices and payroll.

The benefits of automation extend far beyond time saving, to accuracy, improving business visibility and enabling real-time financial insights. With fewer errors and faster-data processing, finance leaders can shift their focus to high-value tasks like driving strategy, identifying risks and opportunities and determining the optimal timing for growth investments.

Attracting investors with operational efficiency 

Once businesses have minimised time spent on administrative tasks, they can focus on the bigger picture: growth and securing investment. With access to cheap capital becoming increasingly difficult, businesses must position themselves wisely to attract funding.  

Investors favour lean, efficient companies, so demonstrating that a business can achieve more with fewer resources signals a commitment to financial prudence and sustainability. By embracing automation, companies can showcase their ability to manage operations efficiently, instilling confidence that any new investment will be spent and used wisely.

Economic uncertainty provides an opportunity to reassess business foundations and create more agile operations. Refining workflows and eliminating bottlenecks not only improves performance but also strengthens investor confidence by demonstrating a long-term commitment to financial health.

Additionally, strong financial reporting and effective cash flow management are crucial to standing out to investors. Clear, real-time insights into financial health demonstrate resilience and highlight a business’ resilience and readiness for growth.

The growth journey ahead

Though the landscape remains tough for UK businesses, sustainable growth is still achievable with a clear and focused strategy. By empowering finance leaders to step into more strategic and high-level decision making roles, organisations can stay resilient and agile amid ongoing economic headwinds.

UK businesses have fought to stay afloat, so now is the time to rebuild strength. By embracing more strategic financial management to build resilience, they can set the stage for long-term, sustainable growth, whatever the economic climate brings.

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Business

The Consortium Conundrum: Debunking Modern Fraud Prevention Myths

By Husnain Bajwa, SVP of Product, Risk Solutions, SEON


As digital threats escalate, businesses are desperately seeking comprehensive solutions to counteract the growing complexity and sophistication of evolving fraud vectors. The latest industry trend – consortium data sharing – promises a revolutionary approach to fraud prevention, where organisations combine their data to strengthen fraud defences.

It’s understandable how the consortium data model presents an appealing narrative of collective intelligence: by pooling fraud insights across multiple organisations, businesses hope to create an omniscient network capable of instantaneously detecting and preventing fraudulent activities.

And this approach seems intuitive – more data should translate to better protection. However, the reality of data sharing is far more complex and fundamentally flawed. Overlooked hurdles reveal significant structural limitations that undermine the effectiveness of consortium strategies, preventing this approach from fulfilling its potential to safeguard against fraud. Here are several key misconceptions about how consortium approaches fail to deliver promised benefits.


Fallacy of Scale Without Quality


One of the most persistent myths in fraud prevention mirrors the trope of enhancing a low-resolution image to reveal more explicit details. There’s a pervasive belief that massive volumes of consortium data can reveal insights not present in any of the original signals. However, this represents a fundamental misunderstanding of information theory and data analysis.

To protect participant privacy, consortium approaches strip away critical information elements relevant to fraud detection. This includes precise identifiers, nuanced temporal sequences and essential contextual metadata. Through the loss of granular signal fidelity required to anonymise information to make data sharing viable, said processes skew data while eroding its quality and reliability. The result is a sanitised dataset that bears little resemblance to the rich, complex information needed for effective fraud prevention. Further, embedded reporting biases from different entities can likewise exacerbate quality issues. Knowing where data comes from is imperative, and consortium data frequently lacks freshness and provenance.

Competitive Distortion is a Problem


Competitive dynamics can impact the efficacy of shared data strategies. Businesses today operate in competitive environments marked by inherent conflicts, where companies have strategic reasons to restrict their information sharing. The selective reporting of fraud cases, intentional delays in sharing emerging fraud patterns and strategic obfuscation of crucial insights can lead to a “tragedy of the commons” situation, where individual organisational interests systematically degrade the potential of consortium information sharing for the collective benefit.

Moreover, when direct competitors share data, organisations often limit their contributions to non-sensitive fraud cases or withhold high-value signals that reduce the effectiveness of the consortium dynamics.

Anonymisation’s Hidden Costs


Consortiums are compelled to aggressively anonymise data to sidestep the legal and ethical concerns of operating akin to de facto credit reporting agencies. This anonymisation process encompasses removing precise identifiers, truncating temporal sequences, coarsening behavioural patterns, eliminating cross-entity relationships and reducing contextual signals. Such extensive modifications limit the data’s utility for fraud detection by obscuring the details necessary for identifying and analysing nuanced fraudulent activities.

These anonymisation efforts, needed to preserve privacy, also mean that vital contextual information is lost, significantly hampering the ability to detect fraud trends over time and diluting the effectiveness of such data. This overall reduction in data utility illustrates the profound trade-offs required to balance privacy concerns with effective fraud detection.

The Problem of Lost Provenance


In the critical frameworks of DIKA (Data, Information, Knowledge, Action) and OODA (Observe, Orient, Decide, Act), data provenance is essential for validating information quality, understanding contextual relevance, assessing temporal applicability, determining confidence levels and guiding action selection. However, once data provenance is lost through consortium sharing, it is irrecoverable, leading to a permanent degradation in decision quality.

This loss of provenance becomes even more critical at the moment of decision-making. Without the ability to verify the freshness of data, assess the reliability of its sources or understand the context in which it was collected, decision-makers are left with limited visibility into preprocessing steps and a reduced confidence in their signal interpretation. These constraints hinder the effectiveness of fraud detection efforts, as the underlying data lacks the necessary clarity for precise and timely decision-making.

The Realities of Fraud Detection Techniques


Modern fraud prevention hinges on well-established analytical techniques such as rule-based pattern matching, supervised classification, anomaly detection, network analysis and temporal sequence modelling. These methods underscore a critical principle in fraud detection: the signal quality far outweighs the data volume. High-quality, context-rich data enhances the effectiveness of these techniques, enabling more accurate and dynamic responses to potential fraud.

Despite the rapid advancements in machine learning (ML) and data science, the fundamental constraints of fraud detection remain unchanged. The effectiveness of advanced ML models is still heavily dependent on the quality of data, the intricacy of feature engineering, the interpretability of models and adherence to regulatory compliance and operational constraints. No degree of algorithmic sophistication can compensate for fundamental data limitations.

As a result, the core of effective fraud detection continues to rely more on the precision and context of data rather than sheer quantity. This reality shapes the strategic focus of fraud prevention efforts, prioritising data integrity and actionable insights over expansive but less actionable data sets.

Evolving Into Trust & Safety: The Imperative for High-Quality Data


As the scope of fraud prevention broadens into the more encompassing field of trust and safety, the requirements for effective management become more complex. New demands, such as end-to-end activity tracking, cross-domain risk assessment, behavioural pattern analysis, intent determination and impact evaluation, all rely heavily on the quality and provenance of data.

In trust and safety operations, maintaining clear audit trails, ensuring source verification, preserving data context, assessing actions’ impact, and justifying decisions become paramount.

However, the nature of consortium data, which is anonymised and decontextualised to protect privacy and meet regulatory standards, cannot fundamentally support clear audit trails, ensure source verification, preserve data context, and readily assess the impact of actions to justify decisions. These limitations showcase the critical need for organisations to develop their own rich, contextually detailed datasets that retain provenance and can be directly applied to operational needs to ensure that trust and safety measures are comprehensive, effectively targeted, and relevant.

Rethinking Data Strategies


While consortium data sharing offers a compelling vision, its execution is fraught with challenges that diminish its practical utility. Fundamental limitations such as data quality concerns, competitive dynamics, privacy requirements and the critical need for provenance preservation undermine the effectiveness of such collaborative efforts. Instead of relying on massive, shared datasets of uncertain quality, organisations should pivot toward cultivating their own high-quality internal datasets.

The future of effective fraud prevention lies not in the quantity of shared data but in the quality of proprietary, context-rich data with clear provenance and direct operational relevance. By building and maintaining high-quality datasets, organisations can create a more resilient and effective fraud prevention framework tailored to their specific operational needs and challenges.

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