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The Asia Pacific region is at the forefront of integrating AI into financial crime compliance, with developed economies like Singapore taking significant steps to introduce initiatives to help financial institutions to test AI-driven projects.
In the last few years, AI has transformed the way organisations view operations, with the rapid pace of technological development moving AI from being experimental projects into actual operational deployments. Generative artificial intelligence (gen AI) holds immense promise for financial institutions by offering the potential to streamline their operations and provide a strategic advantage. However, gen AI also brings with it a set of inherent risks that must be considered carefully.
Singapore’s industry collaboration on AI – Project MindForge
In June 2023, Singapore announced a collaborative initiative, Project MindForge to examine the risks and opportunities of generative artificial intelligence (gen AI) technology for financial services.
The Project has two clear objectives:
1. Develop a clear and concise framework on the responsible use of gen AI within the financial industry.
2. Catalyse gen AI powered innovation to solve common industry wide challenges and enhance risk management.
The Project brought together a consortium of financial institutions, industry associations, and technology and consulting partners to explore the potential risks, impact, provide a private sector perspective for using gen AI responsibly, and to support innovation within the industry while ensuring AI is used ethically, transparently, and safely.
The Project was split into two phases:
Phase 1 [completed]: The consortium developed a comprehensive gen AI risk framework and expanded the scope to include financial institutions from the insurance and asset management industries.
Phase 2 [in progress]: Focusing on AI governance and developing an AI governance handbook for the financial industry in Phase 2, with industry consultations and feedback to be gathered to refine the handbook.
Financial institutions in financial crime compliance face a slew of challenges brought on by technological advancements and operational inefficiencies, including high false positive rates and manual processes in investigations and reporting. If risks are managed properly, gen AI could help organisations reduce manual workload, improve consistency in investigation and reporting automation, and enhance detection accuracy.
How can financial institutions future-proof
Compliance costs in Singapore can be high as there is a high regulatory burden, with heavy fines for non-compliant institutions. The Monetary Authority of Singapore (MAS) is a model regulator for collaboration and is guiding the industry to have the right controls in place to drive down money laundering. The compliance burden is necessary and proportionate considering Singapore’s thriving fintech scene and booming economy – AI will be key to driving down the associated costs.
When AI-enhanced capabilities are embedded correctly in compliance, organisations benefit from reduction in false positives, faster triaging process, earlier detection of hidden threats, faster navigation of connected risks, and stronger case files for internal and regulatory reporting. The AI governance handbook will be a key part to help financial institutions navigate the complex business environment and maximise the potential of using gen AI in their business operations.
While financial institutions may be in different phases of their AI journey, it is imperative that they do not overlook important steps in the transition towards an AI-centric organisation. For financial crime compliance teams to make informed decisions based on AI-generated recommendations, they need to understand the AI process and outputs. An explainable AI approach where every output is paired with human-readable reasoning and supporting evidence helps to transform and deliver value in financial crime compliance when it’s applied responsibly.
Read more on human oversight, biases, and where to start your AI AML journey
The cost of inaction
In today’s world of borderless transactions, financial institutions are faced with the dual challenge of facilitating seamless cross-border transactions and abiding by regulations and adhering to guidelines at the same time. The complex regulatory landscape across the Asia Pacific region further compounds the extensive compliance cost for financial institutions.
Every decision carries risks. On one hand, there is the risk of adopting AI too quickly in the organisation without fully understanding it and implementing the necessary guardrails. On the other hand, there is an equal, if not greater, risk in doing nothing while peers embark on their AI journey.
In the hyper-competitive landscape in which financial institutions operate, AI can be the co-pilot that compliance professionals need to help them better understand context and investigate more efficiently. It can help deliver higher value to customers and shareholders, manage cost responsibly, adhere to regulatory requirements, and stopping financial crimes.
As we look to the future of risk management and compliance, it is essential to continue investing in AI and fostering collaboration between industry stakeholders to stay ahead of increasingly sophisticated financial criminals.
Read more about how to harness AI for AML, in the Napier AI / AML Index
Operationalising AI through a compliance-first approach
Countries and individual organisations may have varying degrees of risk appetite when it comes to AI, but it is undeniable that AI has the capability to adapt in real time to emerging threat patterns by deriving actionable insights from unstructured data. This adaptability is beneficial for compliance teams as it allows them to stay ahead of potential risks and ensure that their organisation remains compliant with evolving regulations within and across jurisdictions.
AI only delivers value when it produces clear, compliant, and explainable results for financial crime teams. A compliance-first approach ensures that every AI capability is developed in line with regulatory expectations and tested for fairness, transparency, and auditability, and not just for AI’s sake.
However, the solution is not as simple as just overlaying a legacy solution with AI. To responsibly harness the potential of AI, firms must go beyond technical implementation and build foundations of trust – both internally and externally.
Read more on how to harness AI responsibly in financial services
Photo by Zhu Hongzhi on Unsplash
