The convergence of technology and finance crime has created an intricate web of risks. Here's how data analytics can help.
Singapore has been hailed as one of the safest countries in the world for doing business with strong governance, but the recent billion-dollar money laundering case has underscored the imperative for the city-state to stay vigilant in the fight against financial crime.
Notably, a recent survey found that nearly a third of organisations based in Singapore (62%) anticipate an uptick in financial crime risks over the next 12 months, with cybersecurity and data breaches identified as primary contributing factors. Additionally, financial institutions in Singapore spent US$5.7 billion ($7.7 billion) in the past year to fight crime and meet regulatory obligations.
Singapore’s position as the financial and trade hub in the Southeast Asia region has facilitated business opportunities for companies, albeit with a heightened risk of facing new and sophisticated threats. Given the sheer volume of cross-border financial transactions, cybercriminals are presented with a lucrative opportunity to exploit weaknesses in the ecosystem.
In this current threat landscape, the demand for compliance continues to tighten, subjecting organisations to incessant streams of processes and data that humans alone cannot efficiently manage. This is where the adoption of advanced data and analytical technologies, such as artificial intelligence (AI), machine learning (ML) and cognitive automation come in useful. These tools not only alleviate tedious tasks but also reduce time and free up manpower to focus on more transformational activities.
Shifting landscape in the digital era
In today’s digital age, financial crime has taken a significant leap in complexity and scale – the paradoxical effect of digital transformation. With the proliferation of cloud-based applications and online transactions, individuals and organisations alike are exposed to a multitude of cyber threats, spanning from phishing, ransomware attacks, global identity theft to financial fraud. As consumers embrace fintech solutions for ease and convenience, cyber criminals are also lurking online to disrupt services and steal data, leading to unauthorised access and financial theft.
Within organisations, technological advancements could pose a double-edged sword, benefiting both legitimate users and insiders with malicious intent. This underscores the need for robust security measures. Additionally, globalisation and digitalisation have enabled financial criminals to up the ante by operating across jurisdictions, making it imperative to adopt a global stance to combat evolving financial threats.
Today, organisations are grappling with a myriad of data-related challenges driven by accelerated digitalisation. The sheer volume of data generated across numerous platforms and systems proves to be a significant obstacle to effective data management. At first glance, it seems that organisations can just simplify their IT infrastructure to manage their data. In reality, data is often stored in silos, sprawling across different systems, resulting in IT complexity and lack of visibility. Traditional data processing methods are inadequate in managing the massive influx of data, let alone conducting any analysis, leading to inefficiencies and the risk of non-compliance.
To keep up with the ever-growing labyrinth of regulations, it is critical to utilise flexible, scalable and intelligent data management solutions as compliance is often dependent on the ability to correlate, analyse, and manage data from disparate sources, such as validating a customer’s identity and sources of funds. In such a challenging scenario, data analytics emerges as a powerful tool to stay on the right side of the law and navigate the complexities of financial crime.
How a data-driven approach can help against financial crime
The convergence of technology and finance crime has created an intricate web of risks, as bad actors capitalise on sophisticated techniques to impersonate users, conduct phishing scams or manipulate digital transactions. This illustrates the importance of adopting a two-pronged approach that combines technology and collaboration to combat new financial crime. For instance, the Monetary Authority of Singapore (MAS) announced the launch of a new digital platform for financial institutions to share sensitive customer information in a timely manner to combat financial crime.
According to a recent Veritas research, 68% of organisations globally are looking at AI and ML to boost their security posture. The use of advanced analytics solutions such as AI and ML empowers organisations to transform raw, unstructured data into actionable insights.
These technologies can swiftly analyse vast amounts of data as well as identify patterns, trends and perhaps, more importantly, anomalies that might otherwise be undetected. By automating their compliance and governance processes, organisations can streamline fraud detection, money laundering tracking, and flag potential financial crime activities such as digital fraud or insider threat, in real-time, to ensure timely adherence to regulatory requirements.
Moreover, the integration of AI and ML capabilities into financial crime detection processes significantly enhances efficiency. These technologies reduce manual intervention, minimise errors, and enhance the accuracy of detection mechanisms. By incorporating predictive analytics into AI and ML, organisations can conduct proactive monitoring for potential risks and take preventive measures to mitigate financial crime. This insight empowers proactive and intelligent decision-making, allowing organisations to align their operations with any upcoming mandates effectively. According to the Institute of International Finance, AI and ML are critical tools to combat rising fraud in payments, especially in the wake of losses totalling US$28.58 billion in 2020 and projected to grow to US$49.32 billion by 2030.
Generative AI also holds great promise in the early detection of financial crime. By utilising data analytics to monitor and predict illicit behaviours from diverse data sets, generative AI models can flag deviations, aiding in anomaly detection crucial for fraud prevention. Additionally, generative AI can simulate fraud scenarios and generate synthetic data, enhancing training sets for fraud detection algorithms and augmenting their accuracy.
To unleash the potential of AI-driven benefits, organisations need to invest in skilled staff, including data analysts, data scientists and cybersecurity experts proficient in utilising these advanced tools. It is also critical to ensure that any data used for AI purposes is subject to the same principles and guidelines around security, privacy and governance, to meet regulatory requirements.
In a fast-evolving digital landscape, workloads and applications will continue to be data-intensive, creating the need for organisations to adopt a holistic 360 approach to mitigate against threats by unifying data security, data protection and data governance capabilities. By shoring up our defences in the fight against financial crime, we can pave the way for a more secure and resilient future.
Andy Ng is the vice president and managing director for Asia South and Pacific Region at Veritas Technologies