Generative AI in Compliance: The Opportunities and Challenges in Compliance

Generative AI (Gen AI) in compliance is set to improve productivity across industries, with financial services expected to take the lead in this shift. According to a global consulting firm, generative AI can increase revenues by $340 billion annually in the global banking sector primarily due to increased productivity.

Financial institutions are now handling risks and staying compliant with changing regulations by using advanced technologies to model analytics, automate tasks, and process fragmented data.

This article examines how financial institutions can address generative AI's key opportunities and challenges in compliance that can effectively enhance risk and compliance frameworks.

The Promise of Generative AI in Compliance

Generative AI offers a significant shift in how financial institutions manage risk and compliance, moving from task-based operations to a more strategic approach focused on early risk prevention. 

This technology allows organizations to adopt a "shift-left" strategy, integrating controls and compliance measures early in the customer onboarding and product development processes, rather than addressing them after issues arise.

The "shift-left" approach, commonly applied in security and compliance, encourages institutions to embed compliance protocols early in the workflow. This strategic approach identifies potential risks early and prevents them from escalating into costly issues. This streamlines processes and minimizes last-minute fixes that could disrupt workflows and lead to extra expenses.

This approach reduces the chances of costly issues arising later in the process by ensuring that potential risks are identified and mitigated early on.

Furthermore, AI-powered risk intelligence centers can make compliance systems transparent as well as efficient by simplifying options and automating regulatory reporting.

These AI-powered systems provide instant risk assessments and automate the creation of policies, reports, and procedures in response to real-time regulatory changes. Banks can use generative AI as a virtual expert to scan transactions, detect potential red flags, and deliver detailed data for decision-making processes like credit risk assessments.

Emerging Applications Opportunities of Generative AI in Compliance

Generative AI is already proving valuable across multiple risk and compliance functions. Let’s talk about the key areas where Generative AI is used for compliance :

1. Regulatory Compliance

Generative AI is becoming valuable as a virtual regulatory expert assisting banks and financial institutions in managing complicated regulations, policies, and procedures. Generative AI tools streamline the compliance process and reduce the risk of regulatory violations by automating regulatory compliance checks.A survey of 200 U.S. business executives from organizations with $1 billion in revenue revealed that generative AI remains the leading emerging technology. As of June 2023, 75% of business leaders ranked it among the top three technologies expected to shape their industry over the next 12 to 18 months.

2. Financial Crime

Generative AI is reshaping the way financial crime is detected and reported. It automates the generation of Suspicious Activity Reports (SARs) and continuously updates customer risk profiles, significantly improving transaction monitoring. 

A senior official from the European Central Bank (ECB) revealed that their organization has identified over 40 potential use cases for generative AI in banking supervision, highlighting its ability to streamline usual supervisory tasks.

Gen AI models enable compliance teams to respond quickly to emerging threats to generate and refine code to detect suspicious patterns.

3. Employee Training

Employee training and the onboarding process can be done faster and in an efficient manner with the use of Generative AI in the workplace. 

Lucinity estimates that recent growth in the generative AI  sector could save the banking industry $27 billion annually in training and staff costs.

For Tier 1 banks alone, this means annual savings of $15 million to $36 million on recruitment and training. Lucinity's trials showed that adaptive AI systems can reduce staff turnover and increase the output value by 40% by filling the gaps within the lower-level compliance teams.

4. Credit Risk Assessment

Generative AI speeds up the credit risk assessment process by summarising customer transactions and generating credit risk reports. A recent survey of senior credit risk executives from 24 financial institutions, including nine of the top ten U.S. banks, highlights the growing adoption of generative AI in credit risk management. 

According to the survey, 20% of respondents have already implemented at least one generative AI use case within their organizations while an additional 60% plan to do so within the next year.

Even the most cautious executives anticipate using generative AI in their credit risk processes within the next two years. This shows good signs of Gen AI’s potential to transform risk evaluation and decision-making across the financial sector.

5. Modelling and Data Analytics

In areas such as data modeling, generative AI automates the migration of outdated programming languages, monitors model performance, and provides automated documentation.

Institutions such as Goldman Sachs have already begun integrating generative AI through several proof-of-concepts (PoCs). This allows their developers to focus their creativity and innovation on meeting client needs while automating routine tasks for IT experts. 

One standout application is document classification and categorization. Generative AI is used to process millions of documents, summarise key information, and deliver actionable insights to the compliance teams.

6. Cybersecurity

Generative AI strengthens cybersecurity defenses by generating code for vulnerability detection and incident response. In "red teaming" scenarios, it simulates adversarial strategies, identifying potential weaknesses in security infrastructure. 

A recent report by Seeking Alpha anticipates that cybersecurity spending will remain robust compared to other IT sectors, despite broader economic challenges such as rising interest rates and macroeconomic uncertainty impacting stock valuations across the industry. 

According to the report, the analyst highlighted numerous opportunities for the cybersecurity industry to leverage generative AI and machine learning (ML) solutions.

7. Stress Testing and Scenario Analysis

Generative AI is making significant progress in improving stress testing and scenario analysis. Financial institutions are using AI to simulate extreme market conditions, regulatory changes, or sudden economic shifts.

One of the world’s largest consulting firms estimates that process automation at scale is now within reach for most players. It has the potential to reduce operational costs by up to 30% over the next five years when combined with other generative AI.

Automating the generation of complicated scenarios and analyzing outcomes, generative AI enables compliance teams to refine risk management strategies, enhance resilience, and ensure compliance with stress-testing requirements imposed by regulators.

8. Customer Behaviour Analysis for Risk Detection

Generative AI is increasingly being used to analyze customer behavior patterns to detect early warning signs of risk. AI can identify patterns that may suggest fraudulent activity by examining large datasets of customer interactions, transactions, and historical behaviors.

To enhance customer experience and reduce fraud risk, American Express has implemented a fraud detection model backed with generative AI. Processing over $1.2 trillion in transactions annually, the system makes fraud detection in milliseconds. Using Sequential Recurrent Neural Networks (RNN), it analyses transaction sequences to detect anomalies.

9. Automated Audit Trail Generation

Traditionally, tracking all transactions and decisions made within an organization requires significant manual effort. 

Generative AI can also be used to correct errors in payment messages, minimizing manual intervention and improving straight-through processing rates. 

This ensures that compliance activities are transparent, thoroughly documented, and easily accessible during audits, reducing the risk of non-compliance due to incomplete or inaccurate records.

Major Risks and Challenges of Generative AI in Compliance

This section examines the core risks associated with generative AI and how they affect organizations as they adopt this technology, especially in the context of AML compliance.

1. Generative AI Security Risks

As generative AI models become more integrated into financial services, the risk of security breaches increases. These models are prone to such security risks due to the large datasets required for training and their complicated structure.

Breaches can result in unauthorized access to sensitive data for malicious activities like phishing campaigns, DDoS attacks, or even malware creation.

Moreover, internal misuse of AI tools by employees can expose organizations to additional risks. At such times, the use of platforms such as Lucinity that use Microsoft Azure AI cloud infrastructure for secure development of its models.

2. Data and Reliability

The quality of data used to train AI models directly impacts the reliability of their output. Low-quality data or biases in training sets can lead to “hallucinations,” which means AI models start generating fabricated information that appears legitimate. 

This can lead to consequences in industries where compliance accuracy is essential. For instance, Google’s Bard AI led to a $100 billion loss in market value after a single factual error in its demo.

Furthermore, systemic bias in AI models can lead to unfair outcomes. If AI is trained on non-representative data, its decisions can be unfair to certain groups. For example, an experiment with Stable Diffusion AI revealed inherent racial and socio-economic biases in its image generation.

3. Intellectual Property (IP) Challenges

Generative AI platforms are trained on big datasets, known as data lakes, that contain billions of parameters constructed from archives of text and images. These platforms use the data to detect patterns and relationships for creating rules, making predictions, and generating content in response to prompts. 

In many cases, generative AI platforms may use unlicensed content to train their models, raising issues about whether creators' works are being used without permission. This also applies to the direct use of copyrighted or trademarked works in prompts, potentially leading to legal disputes.In the case of Andersen v. Stability AI (2022), artists have sued multiple AI platforms for allegedly using their original works without licensing. If courts rule that these AI-generated outputs are generated using unauthorized intellectual property, platforms could face significant infringement penalties.

4. Regulatory Challenges Linked to AI Adoption

With the rapid adoption of generative AI, regulatory bodies are finding it challenging to keep themselves updated. While some countries like the European Union have introduced AI-specific regulations like the  EU’s AI Act, many countries still lack comprehensive legal structures. This creates a fragmented regulatory environment, making it difficult for global companies to ensure compliance across markets.

In response, financial institutions must take strategic steps to ensure compliance with both current and future AI regulations while implementing Gen AI. This includes developing long-term AI governance strategies that focus on data security, transparency, and performance monitoring. 

Furthermore, Assigning AI oversight to senior executives such as Chief Data Officers or Chief Technology Officers can ensure that AI models align with company values and regulatory requirements. Ongoing education and training for employees on safe AI practices are also essential to maintain compliance and minimize legal exposure.

Strategies for Planning a Succesful Gen AI Implementation

For effective integration of generative AI into their compliance and risk functions, banks should take a targeted approach. Starting with three to five high-priority use cases that align with strategic objectives is important. 

Moreover, developing a roadmap, building a Gen AI ecosystem, and cultivating cross-functional teams will help scale AI applications while ensuring responsible usage. Key areas to focus on include:

• Choosing a robust, production-ready Gen AI ecosystem
• Securing hybrid-cloud environments for scalability
• Integrating with enterprise-grade models, such as Luci.
• Automating supporting tools like ML Ops
• Implementing a comprehensive governance and talent model

While generative AI is still an emerging technology, banks that integrate it into their operations stand to gain significant competitive advantages.

How Lucinity Can Help with Gen AI in Compliance

Lucinity is a leading generative AI platform for compliance. With its AI-powered solutions that improve financial crime investigations and compliance functions, banks and institutions can make their compliance accurate and systematic. 

Here’s how Lucinity’s solutions directly benefit compliance teams:

1. Lucinity Case Manager: Lucinity’s Case Manager empowers teams to work efficiently by optimizing and streamlining the compliance investigation process. Lucinity Case Manager automates key workflows which reduces manual tasks. Its AI-powered insights help prioritize high-risk cases, providing clear, actionable recommendations.

The automation of this reporting process, coupled with Luci’s case management and real-time risk insights, ensures consistent and auditable compliance operations.

2. Lucinity Customer 360: Lucinity’s Customer 360 provides a comprehensive, real-time view of each customer’s risk profile, giving compliance teams detailed insights into customer behaviors and potential risks.

Its seamless integration with existing systems ensures that compliance teams have access to relevant and latest information assisting them in better risk management and improved compliance outcomes.

3. Luci Copilot: Powered by OpenAI’s GPT-4, Luci simplifies investigations by summarizing cases, automating regulatory reports, and visualizing financial flows. Luci’s ability to reduce investigation time allows compliance teams to focus on high-value tasks of the organization.

Lucinity’s system-agnostic plugin allows Luci’s capabilities to be integrated into any web-based enterprise system, including CRM and Excel. Luci’s integration with existing systems means institutions can benefit from it immediately, without the need for lengthy deployments or IT investment.

This seamless integration helps boost compliance team productivity by up to 90%.

Wrapping Up

The rise of generative AI in compliance offers financial institutions the opportunity to transition from defensive compliance to anticipatory risk management. Institutions that will successfully integrate generative AI into their compliance frameworks will be able to streamline operations, enhance risk mitigation, and gain a significant advantage in the market. 

Let’s have a look at the key takeaways from this blog:

1. Generative AI automates tasks and improves risk management, making compliance processes more efficient.
2. AI integrates compliance controls early, reducing costly errors by addressing risks from the start.
3. AI automates fraud detection and risk profiling, improving transaction monitoring and response times.
4. AI-driven solutions cut training and staff costs by up to $27 billion annually in the banking sector.
5. Institutions must address AI-related risks like security breaches and data quality with strong governance strategies.

For more insights on how Lucinity can support your compliance with its generative AI tools, visit Lucinity's website.

FAQs

1. How does generative AI impact compliance functions?

Generative AI automates repetitive tasks, enhances decision-making, and provides real-time insights for regulatory compliance, financial crime detection, and risk management.

2. What are the risks of using generative AI in compliance?

Generative AI introduces risks such as data privacy breaches, model biases, and security vulnerabilities. Strong governance frameworks are required to mitigate these risks.

3. How does Luci Copilot help in financial crime investigations?

Luci Copilot automates case summarization, regulatory reporting, and money flow visualization, allowing compliance teams to complete investigations faster and with accuracy.

4. Can generative AI be integrated with existing compliance systems?

Yes, Lucinity’s Luci Copilot Plugin integrates seamlessly with web-based enterprise applications, boosting productivity without the need for significant system changes.