How AI Can Assist Banks With Risk and Compliance

The advent of Artificial Intelligence (AI) is on the brink of triggering a new phase of enhanced productivity across various sectors, with the financial services industry standing prominently among them.

AI is already reshaping the landscape of banking operations by offering capabilities ranging from analytical modelling to automating manual tasks and synthesising unstructured data. This transformative technology is altering the normal methods of banking functions, including risk management and regulatory compliance.

As organisations embrace AI, it becomes imperative for risk and compliance functions to establish frameworks around its usage. Nevertheless, AI presents opportunities for these functions to enhance their efficiency and efficacy. 

How Can Banks Harness the Potential of AI?

Over the next three to five years, AI holds the promise of revolutionising risk management practices within banks. It can enable functions to shift focus from mundane tasks towards strategic collaboration with business units in preemptive risk mitigation, adopting a proactive stance in managing risks inherent in new customer interactions, a concept often termed as “shift left.”

This shift would empower risk professionals to advise on product innovation, strategic decisions, analyse emerging risk patterns, fortify resilience, and enhance risk and control processes preemptively.

These advancements may give rise to AI-powered risk intelligence centres catering to all lines of defence (LODs) within banks, business and operations, risk and compliance functions, and audit. Such centres could offer automated reporting, enhanced risk visibility, streamlined decision-making processes, and partial automation in policy drafting and updates to align with evolving regulatory mandates.

By serving as dependable repositories of information, these centres would equip risk managers with the means to make timely and well-informed decisions.

For instance, McKinsey has developed an AI virtual expert capable of furnishing tailored responses based on proprietary data and resources. Banks can emulate this approach by creating similar tools to analyse transactions, identify potential risks, monitor market trends, and assess asset valuations, thereby influencing risk management decisions. These virtual experts can also aggregate data and assess climate-related risks to address counterparty inquiries.

Moreover, AI has the potential to facilitate closer collaboration between the first and second lines of defence within organisations, while upholding governance structures across all three lines. Enhanced coordination would pave the way for improved monitoring and control mechanisms, fortifying the organisation’s risk management framework.

What Are the Applications of AI in Risk and Compliance?

Financial institutions are exploring various applications of AI across risk and compliance functions, focusing on areas such as regulatory compliance, financial crime, credit risk, data analytics, cyber risk, and climate risk. Broadly categorised, these applications fall into three archetypes:

1. Virtual Expert: AI can provide succinct answers to user queries based on extensive documents and unstructured data
2. Manual Process Automation: AI streamlines time-consuming tasks
3. Code Acceleration: AI updates or generates new code, facilitating operational efficiency

These archetypes find utility across a spectrum of risk and compliance responsibilities

• Regulatory Compliance: AI serves as a virtual expert on regulations and policies, automates compliance checks, and alerts on potential breaches
• Financial Crime: It generates suspicious-activity reports, updates risk ratings, and enhances transaction monitoring
• Credit Risk: AI accelerates credit processes, aids in credit decision-making, and assists in risk reporting
• Modeling and Data Analytics: It expedites legacy system migrations, monitors model performance, and aids in model documentation
• Cyber Risk: AI detects vulnerabilities, generates code for security measures, and supports security data analysis
• Climate Risk: It facilitates code generation for risk assessment, automates data collection, and offers insights on environmental risks

Key Considerations in AI Adoption

While the potential of  AI is vast, prudent prioritisation of use cases is essential for realising value responsibly. Risk leaders should evaluate use cases based on three critical dimensions: risk, impact, and feasibility. They must align decisions with the organisation’s vision for AI, adhere to regulatory guidelines, and address novel risks associated with this technology.

These risks span categories such as fairness, privacy, security, and compliance.

How Organisations Can Plan Their AI Journey

Organisations aspiring to leverage AI should adopt a focused, methodical approach. Initiating with a few high-priority use cases aligned with strategic objectives, they can assess business impact and subsequently scale implementation.

Establishing an AI ecosystem entails focusing on areas such as reusable services, secure tech infrastructure, integration with foundation models, automation of supporting tools, talent development, and process alignment.

In conclusion, while the potential of AI is undeniable, its effective adoption demands a holistic understanding of risks, regulatory compliance, technological requirements, and talent development. By embracing AI responsibly, organisations can unlock efficiencies, foster innovation, and bolster their competitive edge in the evolving landscape of risk management and compliance.