AI is a two-sided coin for banks: while it’s unlocking many possibilities for more efficient operations, it may possibly also pose external and internal risks.
Financial criminals are leveraging the technology to provide deepfake videos, voices and faux documents that may get past computer and human detection, or to supercharge email fraud activities. Within the US alone, generative AI is predicted to speed up fraud losses to an annual growth rate of 32%, reaching US$40 billion by 2027, in accordance with a recent report by Deloitte.
Perhaps, then, the response from banks must be to arm themselves with even higher tools, harnessing AI across financial crime prevention. Financial institutions are actually beginning to deploy AI in anti-financial crime (AFC) efforts – to observe transactions, generate suspicious activity reports, automate fraud detection and more. These have the potential to speed up processes while increasing accuracy.
The problem is when banks don’t balance the implementation of AI with human judgment. With out a human within the loop, AI adoption can affect compliance, bias, and adaptableness to recent threats.
We consider in a cautious, hybrid approach to AI adoption within the financial sector, one that can proceed to require human input.
The difference between rules-based and AI-driven AFC systems
Traditionally, AFC – and particularly anti-money laundering (AML) systems – have operated with fixed rules set by compliance teams in response to regulations. Within the case of transaction monitoring, for instance, these rules are implemented to flag transactions based on specific predefined criteria, resembling transaction amount thresholds or geographical risk aspects.
AI presents a brand new way of screening for financial crime risk. Machine learning models may be used to detect suspicious patterns based on a series of datasets which can be in constant evolution. The system analyzes transactions, historical data, customer behavior, and contextual data to observe for anything suspicious, while learning over time, offering adaptive and potentially simpler crime monitoring.
Nevertheless, while rules-based systems are predictable and simply auditable, AI-driven systems introduce a posh “black box” element attributable to opaque decision-making processes. It’s harder to trace an AI system’s reasoning for flagging certain behavior as suspicious, provided that so many elements are involved. This will see the AI reach a certain conclusion based on outdated criteria, or provide factually incorrect insights, without this being immediately detectable. It might also cause problems for a financial institution’s regulatory compliance.
Possible regulatory challenges
Financial institutions should adhere to stringent regulatory standards, resembling the EU’s AMLD and the US’s Bank Secrecy Act, which mandate clear, traceable decision-making. AI systems, especially deep learning models, may be difficult to interpret.
To make sure accountability while adopting AI, banks need careful planning, thorough testing, specialized compliance frameworks and human oversight. Humans can validate automated decisions by, for instance, interpreting the reasoning behind a flagged transaction, making it explainable and defensible to regulators.
Financial institutions are also under increasing pressure to make use of Explainable AI (XAI) tools to make AI-driven decisions comprehensible to regulators and auditors. XAI is a process that allows humans to understand the output of an AI system and its underlying decision making.
Human judgment required for holistic view
Adoption of AI can’t give option to complacency with automated systems. Human analysts bring context and judgment that AI lacks, allowing for nuanced decision-making in complex or ambiguous cases, which stays essential in AFC investigations.
Among the many risks of dependency on AI are the potential of errors (e.g. false positives, false negatives) and bias. AI may be susceptible to false positives if the models aren’t well-tuned, or are trained on biased data. While humans are also liable to bias, the added risk of AI is that it may possibly be difficult to discover bias inside the system.
Moreover, AI models run on the information that’s fed to them – they could not catch novel or rare suspicious patterns outside historical trends, or based on real world insights. A full substitute of rules-based systems with AI could leave blind spots in AFC monitoring.
In cases of bias, ambiguity or novelty, AFC needs a discerning eye that AI cannot provide. At the identical time, if we were to remove humans from the method, it could severely stunt the power of your teams to know patterns in financial crime, spot patterns, and discover emerging trends. In turn, that might make it harder to maintain any automated systems up to this point.
A hybrid approach: combining rules-based and AI-driven AFC
Financial institutions can mix a rules-based approach with AI tools to create a multi-layered system that leverages the strengths of each approaches. A hybrid system will make AI implementation more accurate in the long term, and more flexible in addressing emerging financial crime threats, without sacrificing transparency.
To do that, institutions can integrate AI models with ongoing human feedback. The models’ adaptive learning would subsequently not only grow based on data patterns, but in addition on human input that refines and rebalances it.
Not all AI systems are equal. AI models should undergo continuous testing to judge accuracy, fairness, and compliance, with regular updates based on regulatory changes and recent threat intelligence as identified by your AFC teams.
Risk and compliance experts have to be trained in AI, or an AI expert must be hired to the team, to be sure that AI development and deployment is executed inside certain guardrails. They have to also develop compliance frameworks specific to AI, establishing a pathway to regulatory adherence in an emerging sector for compliance experts.
As a part of AI adoption, it’s essential that every one elements of the organization are briefed on the capabilities of the brand new AI models they’re working with, but in addition their shortcomings (resembling potential bias), in an effort to make them more perceptive to potential errors.
Your organization must also ensure other strategic considerations in an effort to preserve security and data quality. It’s essential to speculate in high-quality, secure data infrastructure and be sure that they’re trained on accurate and diverse datasets.
AI is and can proceed to be each a threat and a defensive tool for banks. But they should handle this powerful recent technology appropriately to avoid creating problems slightly than solving them.
