From Hindsight to Insight to Foresight

Artificial intelligence (AI) refers to any computer system or machine capable of mimicking human intelligence. In other words, it is the ability of a computer system to emulate human-like cognitive abilities such as learning and problem-solving.

Although AI has been receiving significant attention recently because of relatively new technologies like large language models (LLMs), AI as a scientific domain is not inherently new. The foundational concepts of AI began appearing in scientific literature in the 1940s and the actual term “Artificial Intelligence” was coined in 1955 by Stanford Professor Emeritus John McCarthy. Elements of AI such as machine learning (referring to the ability of algorithms and statistical models to learn and adapt without being explicitly programmed) have been deployed for decades across a variety of use cases and industries.

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There are many forms of AI, often grouped by capabilities and functionalities.^[1]

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Today, all AI is Narrow; General and Super AI remain theoretical. Refer to the Appendix for definitions of these capabilities and examples of some of the respective functionalities.

No. Both sectors have used AI for some time. For example, AI in the financial services sector traces its origins to the 1980s, when it was primarily employed to identify fraud.^[2] Other examples of early adoption by the financial services industry include back-office automation, credit scoring/risk underwriting models, portfolio management, structured derivatives pricing and customer service chatbots. Advances in AI have continued to introduce additional functionality and complexity. E-commerce businesses have also used AI for decades to, among other things, analyse customer data and make personalised product recommendations, respond to routine customer inquiries, and predict customer demand and drive dynamic pricing decisions.

Generative AI, or Gen AI, is a subset of AI that focuses on “generating” new content such as images, audio, video, text, code or even 3D models that are original and not just a variation of existing data.

Despite its increased functionality, Gen AI is considered Narrow AI because it operates under far more limitations than even the most basic human intelligence.

Large language models (LLMs) are a type of generative AI trained on vast amounts of data with a large number of parameters that generate novel text-based responses. Today there are a number of proprietary LLMs built/developed by third parties with a conversational interface (e.g., ChatGPT developed by OpenAI), accelerating user interactivity and ease of adoption.

Financial crime broadly refers to all crimes that involve taking money or other property that belongs to someone else to obtain a financial or professional gain. The specific activities included as financial crime are called predicate crimes or offenses and are generally determined by jurisdictional law.

The extent to which a company is exposed to any of these financial crimes is a function of many variables including the nature of its products and services, its customer base, its geographic footprint, and its control environment.

Predicate crimes or offenses include but are not limited to:

By analysing vast amounts of data and identifying complex patterns, AI can significantly improve the accuracy of detecting illicit activity, resulting in fewer false positives (legitimate transactions flagged as potentially suspicious) and false negatives (suspicious transactions that are not identified).

By automating the process of capturing, documenting, and organising the alert/case narrative in a standardised and traceable format using AI, the alert review team of one bank was able to increase its productivity 5X.

Value: Efficiency, cost effectiveness

A financial institution uses AI to risk score its sanctions alerts, dispositioning those that pose little risk, and directing higher risk alerts to humans to resolve.

Value: Efficiency, regulatory compliance

Fingerprint scanners, facial recognition and voice recognition technologies can be used to offer an extra layer of security, making it more difficult for fraudsters to impersonate legitimate customers.

A payment processor uses time, location, device and GPS data to determine whether activity occurring in distant geographies may be fraudulent. The company believes that AI will eventually learn to evaluate certain behaviors, including swiping speed and gestures when assessing the likelihood of fraud.

Value: Effectiveness, reputational harm minimisation, customer protection, revenue leakage aversion

A broker-dealer uses AI to analyse large datasets from multiple sources, such as market data, transactional data, social media and news feeds, to identify deviant trading patterns or anomalies in real time. This enables firms to undertake real-time monitoring, detect and deter potential violations, and send out timely alerts for investigation across a series of use cases (e.g., rogue trading, insider information, market abuse, collusion, sales malpractice, elder abuse, etc.)

Value: Effectiveness, regulatory compliance, reputational harm minimisation

An institution uses AI which learned from historical data to analyse large data sets, flag transactions and establish links between entities that deviate from established patterns and may indicate improper payments.

Value: Effectiveness, prudent risk detection, compliance

Computer algorithms can be used to examine merchant sites electronically and can spot indications of front companies that the human eye might not be able to detect.

Value: Effectiveness, risk and loss mitigation

Measuring ROI for AI investments can be complex as many benefits are long-term and difficult to quantify precisely. Among the metrics institutions may consider are the following:

a. Improved efficiency as evidenced by better productivity and/or reduction/reallocation of staff

b. Reduction of false positives/improved detection rates (i.e., more signal/less noise)

c. Better regulatory outcomes including better examination results, fewer violations of law and penalties

d. Reduced customer friction such as faster client onboarding and quicker resolution of questions about customer transaction activity, less need to contact customers

e. Greater agility to manage new threats

Governments, regulators and standard-setting bodies are all developing guidelines and frameworks for the use of AI.

As governments and regulators across the globe consider the transformative impact that AI will have, they are developing governing frameworks and communicating their expectations for the ethical and responsible use of AI. The EU AI Act is one significant example of a government framework. Other jurisdictions and regulators are still in an information-gathering phase and have not published final guidance, although most have at least signaled through speeches and in industry for what they are thinking.

Examples of emerging standards for AI governance issued by standard-setting bodies include:

• NIST AI Risk Management Framework is designed to equip organisations and individuals with approaches that increase the trustworthiness of AI systems, and to help foster the responsible design, development, deployment and use of AI systems over time.
• ISO/IEC AI Framework provides guidance on managing risks associated with the development and use of AI. The document offers strategic guidance to organisations to assist in integrating risk management into significant activities and functions.
   

The policy paper published in August 2023 by the UK’s Office of Artificial Intelligence and Department for Science, Innovation and Technology does a great job of succinctly offering five guiding principles for the responsible development and use of AI, which are common to most of the published and emerging guidance:

• Safety, security, resilience and robustness
• Appropriate transparency, interpretability and explainability
• Ethics and fairness
• Accountability and governance
• Contestability and redress
   

As governments and regulators across the globe consider the transformative impact that AI will have, they are developing governing frameworks and communicating their expectations for the ethical and responsible use of AI.

Some companies that were early adopters of newer AI tools learned the hard way of the importance of making sure they address all questions provided in the response to Question 11.

Some early adopters overestimated the functionality and benefits of an AI tool; in some cases, this included misjudging time savings and the extent to which staff could be reduced.

A valuable lesson learned by early adopters was the benefit of starting small and scaling. This allowed them to prove their value proposition, gain necessary experience without being overwhelmed by the process, and test the technology before scaling to larger initiatives and ultimately industrialising newfound capabilities.

Depending on the nature and extent of adoption, the use of AI may allow for staff reduction, principally among the staff who perform routine tasks. This would leave compliance professionals more time to focus on what’s really important — the activities that require human judgement and experience. The use of AI will also prompt the need to add (or upskill) staff in more specialised roles, including individuals who understand how to use AI tools and effectively evaluate AI outputs, and who can evaluate the ongoing performance.

The use of AI to fight financial crime can be a game changer — achieving cost-savings while driving efficiency and improving efficacy that the industry has been unable to achieve to date. Given the continued evolution of AI capabilities, potential use cases are limited only by our imagination and institutions that don’t leverage AI will find themselves at a disadvantage.

In the battle to fight financial crime, we believe the collective efforts and lessons learned from all interested parties — both public and private sector — will drive the most progress. Both the financial services industry with its extensive experience fighting financial crime in a highly regulated environment and the e-commerce industry, which includes many tech-savvy digital natives, have much to contribute to the common goal of stopping the bad guys.

In one survey of 356 experts, half believe human-level AI will exist by 2061, and 90% said it will exist in the next 100 years. But, for now at least, it is important to remember that AI is a tool, not a replacement for humans. It allows humans to focus on what’s really important — the things that require human experience, judgement and creativity.*

* AI timelines: What do experts in artificial intelligence expect for the future?
— Our World in Data