Leveraging Generative AI for Enhanced Risk and Compliance in Banking.

The banking sector increasingly leverages innovative technologies to boost risk and compliance management. Generative AI (GenAI) stands out as a transformative technology with the potential to revolutionise how banks handle risk and compliance. GenAI offers a promising avenue for improving risk and compliance practices within banks by automating complex tasks, providing expert insights, and enabling advanced analytics. 

In this blog, we'll explore the critical applications of GenAI in risk and compliance in banking and highlight the role of Oracle's solutions in transforming risk and compliance operations.

The Promise of GenAI in Risk & Compliance

GenAI refers to systems that generate content, such as text, images, and even complex data models, based on the input received. This capability is particularly valuable in the banking sector, where managing risk and compliance involves handling vast amounts of data and performing intricate analyses. 

The use of GenAI in finance has been shown to significantly enhance the accuracy and consistency of financial outputs. It minimises human error by rapidly processing large volumes of data, ensuring that financial reports, risk assessments, and compliance documents are accurate and reliable. This reduces the need for corrections and rework, thus saving time and resources.

You can learn more about this metrics-driven approach to evaluating GenAI use cases in finance here.

Here are some key areas where GenAI can significantly impact risk and compliance in banking.

Efficiency and Automation

GenAI can significantly enhance the efficiency of compliance checks by automating repetitive tasks. For instance, banks typically rely on manual processes to monitor transactions for suspicious activities. This approach is not only time-consuming but also vulnerable to human error.

GenAI can automate these tasks by analysing real-time transaction data and detecting anomalies that may suggest fraudulent activities. This alleviates the workload on compliance teams, allowing them to focus on more intricate issues that require human judgment.

GenAI is highly effective in detecting fraud. Traditional fraud detection systems usually depend on predetermined rules and patterns. However, fraudsters constantly change their tactics, making it challenging to keep these systems current. GenAI can analyse extensive bank data and recognise subtle irregularities that rule-based systems might miss. This adaptability ensures that banks stay ahead in identifying and stopping fraudulent activities.

ChatBot Assistants & GenAI Agents

GenAI can act as a virtual expert or chatbot, allowing banks to generate detailed reports and receive expert advice on complex compliance issues. For example, when new regulations are introduced, banks must quickly understand their implications and adjust their operations accordingly. GenAI can analyse the new regulations, compare them with existing compliance frameworks, and identify the necessary adjustments. This feature ensures that banks can maintain compliance without requiring extensive manual analysis.

GenAI can compare policies, regulations, and operating procedures. Another critical use case for banking is acting as a code accelerator; it can identify and fix compliance misalignment and gaps within the current code. Additionally, it can automate the process of checking for regulatory compliance and generate alerts for potential breaches.

At Revvence, we’ve built a ChatBot that can provide tailored answers based on being trained on content specific to finance transformation topics, such as risk and compliance. Much of this blog has been created based on the “conversations” with our ChatBot, Revvy. [Read more about Revvy here.]

ChatBots can sift through vast amounts of data to provide actionable insights into decision-making processes. For instance, when assessing the risk of extending a loan to a new customer, GenAI can analyse the customer's financial history, current economic conditions, and other relevant factors. It can then generate a risk assessment report, helping credit officers make informed decisions quickly. It can draft the credit memo and key contract terms following a credit decision.

GenAI Agents

A GenAI Agent is an advanced artificial intelligence system designed to autonomously perform tasks and generate content based on given input data. Unlike traditional AI, which follows predefined rules, GenAI Agents can create new data, such as text, images, or models, by understanding and interpreting complex patterns within the data they are trained on. These agents can assist in various applications, from content creation to predictive analytics and decision-making processes, enhancing automation and efficiency across the risk function.

For instance, AI can analyse transaction data, customer behaviour, market trends, and economic indicators to evaluate credit, market, and operational risks more accurately. By continuously learning from new data, these AI agents can enhance their predictive models, offering banks more precise risk assessments.

GenAI Agents can be utilised for Proactive Risk Mitigation by leveraging predictive analytics. These agents can identify potential risks before they materialise, enabling banks to take proactive measures to mitigate them. For example, an AI agent can detect early signs of a customer's financial distress, prompting the bank to offer tailored financial solutions to prevent default. This proactive approach helps manage risks more effectively and maintain customer relationships.

Risk Intelligence Hubs

Creating AI-powered risk intelligence hubs can transform how banks handle risk. These hubs can consolidate data from different sources to offer a complete overview of potential risks throughout the bank. By amalgamating transaction data, market trends, and regulatory updates, a risk intelligence hub can detect emerging risks and recommend proactive measures.

A risk intelligence hub in banks is a centralised hub to aggregate, analyse, and disseminate risk-related information across the entire organisation. The primary goals of a risk intelligence hub include:

• Data Integration: Involves the consolidation of information from multiple sources, including transaction records, market data, regulatory updates, and external economic indicators. This process allows for the development of a comprehensive risk profile.
• Real-time Monitoring: Continuously monitoring various risk factors and providing real-time alerts on emerging risks.
• Proactive Risk Management: Identifying potential risks before they materialise and suggesting preemptive measures to mitigate them.
• Enhanced Decision-making: Providing decision-makers with timely and accurate risk insights to inform strategic and operational decisions.
  
  

Generative AI can play a pivotal role in the effectiveness of risk intelligence hubs by enhancing their ability to process and analyse vast amounts of data quickly and accurately. Here are several ways GenAI contributes to this strategy:

Advanced Data Analytics

GenAI can analyse extensive datasets from various sources, uncovering patterns and connections that may not be readily noticeable through conventional analysis methods. One of its applications involves examining historical transaction data, market trends, and economic indicators to forecast potential market declines or credit risks. Using machine learning algorithms, AI systems can enhance their models by continuously incorporating new data, thereby improving the precision of their predictions as time progresses.

GenAI's analytics capabilities outperform traditional bank reporting and analytics processes. These conventional processes usually involve a strategic data store, like AWS, and reporting tools like Tableau. However, this architecture often leads to slower performance, especially when business owners seek new insights or analysis.

Real-time Risk Monitoring

One of GenAI's main advantages is its real-time data processing capability, which enables immediate insights and alerts. This is especially beneficial for a risk intelligence hub, as it allows for the detection of unusual transaction patterns that may signal fraud, the identification of sudden market changes affecting the bank’s portfolio, and the recognition of emerging geopolitical risks. Real-time monitoring empowers banks to swiftly respond to potential threats, thereby minimising their impact.

Market Risk Forecasting 

Banks and financial institutions can use simulated scenarios to monitor external risks such as market crashes, inflation, and the impact of supply chain disruptions. GenAI, employing Generative Adversarial Networks (GANs), can replicate and model authentic market and economic situations, enabling the adoption of more inclusive and resilient banking practices.

Managing Model Risks

Model risk management processes are crucial to banking operations and financial decision-making. Regulatory bodies continually emphasise the importance of testing the effectiveness, accuracy, and reliability of models used by banks and financial institutions. Risk managers are tasked with addressing and minimising model risks, and one approach to this is using advanced technology like GenAI. 

GenAI can be leveraged to thoroughly analyse different aspects of models, including their context, the types of risks they pose, and how they are classified. GenAI can also be trained to detect duplicate models, provide detailed descriptions of models, and compare the inputs of various models to ensure accountability, fairness, and transparency in the decision-making process.

Counterparty Risk

GenAI can simplify the data-gathering process, which is crucial for evaluating the risk related to counterparties transitioning to new financial conditions or regulatory environments. It achieves this by automatically extracting and analysing pertinent data from different sources, making it an ideal tool.

The AI can also produce early-warning signals by detecting and responding to specific trigger events or indicators that may signify potential issues or alterations in risk levels, thus empowering proactive risk management.

Automated Compliance and Reporting

Regulatory compliance plays a crucial role in risk intelligence hubs. Automation through AI, such as GenAI, can streamline the monitoring process for compliance with different regulations. This minimises the workload on human staff and improves the precision of the monitoring process. For instance, AI systems can continuously analyse transaction data to verify its conformity with anti-money laundering (AML) regulations and other regulatory criteria. GenAI can also produce comprehensive compliance reports, promptly furnishing regulators with essential information.

In certain situations, banks can use the ability to process unstructured data to fulfil their regulatory requirements. For instance, in the UK, the FCA's Consumer Duty mandates that firms take a more proactive approach to delivering positive outcomes for retail customers. Banks need to analyse data to verify that the results for their customers align with this duty. GenAI could assist in this monitoring process.

These are just some examples of GenAI's role in establishing Risk Intelligence Hubs to transform risk and compliance in banking.

Enhanced Fraud & Cyber Capabilities

GenAI & Fraud 

Traditional fraud detection methods rely on fixed rules that can become outdated as fraudsters adapt their tactics. However, GenAI can learn from new data, allowing it to adjust to evolving patterns of fraudulent behaviour. AI systems can effectively identify and prevent fraud by analysing transaction data and spotting irregularities.

GenAI can analyse large amounts of transaction data in the financial crime detection space to recognise patterns associated with money laundering and other illegal activities. By continuously learning from new data, these AI systems can stay ahead of evolving criminal methods, ensuring their detection techniques remain effective.

GenAI's proficiency in data analysis and identifying subtle patterns allows it to uncover anomalies and create reports on suspicious activities using natural language processing. Comparing real-time transactional data with historical records can proactively alert risk monitoring operations about deviations and potential fraud patterns.

GenAI & Cyber

Banks are increasingly concerned about cybersecurity, and GenAI can analyse network traffic, detect unusual activity, and forecast potential cyber-attacks. By issuing early warnings about cyber threats, AI assists banks in fortifying their defences and responding promptly to breaches.

GenAI anticipates potential cyber threats by scrutinising historical attack data and recognising patterns that precede breaches. This predictive capacity allows banks to proactively enhance their defences, lessening the probability of successful cyber-attacks.

Banks can trial LLM-based GenAI tools to fortify their defences against malevolent cybersecurity attacks. LLMs can be trained on cybersecurity intelligence datasets encompassing attack patterns, potential risks, and vulnerabilities for scrutinising network traffic, historical patterns, system logs, and real-time events.

With adequate simulation and training, GenAI can further aid cybersecurity teams in studying malware behaviours, phishing attempts, and other risks. Biometric verification, automated security patch generation, and adaptive threat detection represent additional potential applications of GenAI in banking cybersecurity.

Climate Risk Assessment

Banks are facing increased scrutiny on how climate risk impacts their portfolios. Banks need to comprehend the potential effects of environmental factors on their assets. GenAI offers a solution by modelling the impact of climate change on various assets, which enables banks to evaluate their exposure to climate-related risks. This information is critical for banks to develop strategies to mitigate these risks and ensure compliance with regulatory requirements.

GenAI also assists banks in integrating climate risks into their credit rating and loan underwriting processes. By enabling the creation of customer-specific climate risk scores and facilitating proactive climate risk modelling and assessments at the start of banking relationships through data parsing and analysis, GenAI supports banks in managing climate-related risks effectively.

Additionally, with GenAI-driven automation, banks can streamline data collection, verification, and analysis to ensure the accuracy and consistency of climate risk reporting. GenAI's adaptive predictions help banks stay abreast of the latest regulatory frameworks and update reporting protocols for compliance.

In its role as a ChatBot or virtual expert, GenAI can automatically generate reports on environmental, social, and governance (ESG) topics and the sustainability sections of annual reports for enterprise clients or across the bank supply chain. This accelerates research and the generation of reports and analysis.

GenAI can also support relationship managers in fast-tracking climate risk assessment for their counterparties. It can automatically synthesise counterparty transition plans and compare them against actual emissions to evaluate progress toward goals.

Beyond measurement, GenAI aids climate impact analysis by automating reporting on environmental, social, and governance topics. It also supports risk management by automating climate risk drafts and facilitates growth using customer data to personalise green financial products.

Finally, GenAI's ability to model complex environmental data benefits climate risk management. By simulating various climate scenarios and their impact on financial assets, banks can better understand the risks associated with climate change and adjust their investment strategies and risk management practices accordingly.

Product Pricing & Risk

GenAI can potentially optimise the link between risk management and developing new products and pricing strategies. GenAI could speed up the internal capital adequacy assessment process and model capital adequacy by gathering the necessary data.

In the context of a bank exploring new products or pricing strategies, GenAI Agents can be utilised to hasten the assessment of Impairments and RWA calculations. Even marginal enhancements in this area can potentially release substantial capital and expedite the creation and release of new products.

Implementing Generative AI in Banking

Banks need to adopt a strategic approach to implementation to fully realise the benefits of GenAI. We have written a blog, “A Metrics-Driven Approach to Evaluating GenAI Use Cases in the Finance Function,” which outlines a framework for implementing GenAI in Banking. You can read more about it here.

Risk Platform Solutions

Oracle’s financial services risk management solutions are designed to provide comprehensive tools for managing various risk types, ensuring compliance, and improving decision-making processes across the bank.  Here is a brief rundown of each of Oracle's solution accelerators.

Climate Change Analytics

Oracle's tools enable financial institutions to accurately calculate and report greenhouse gas emissions, aligning with global sustainability goals and regulatory requirements. This feature is crucial for managing climate-related risks and demonstrating environmental responsibility.

Credit Risk Analytics

Oracle offers a holistic view of credit risk across retail, wholesale, and counterparty exposures. Advanced analytics help institutions assess and manage credit risk more effectively, enhancing the accuracy and reliability of credit risk assessments and supporting better decision-making.

Market Risk Measurement

Advanced risk models provided by Oracle allow for accurate valuation of financial instruments. Comprehensive market risk analysis, including stress testing and scenario analysis, helps institutions prepare for market volatility and economic shifts, improving the precision of market risk assessments.

Liquidity Risk Management

Oracle ensures compliance with regulatory guidelines through flexible, prebuilt rules and real-time monitoring of liquidity positions. This proactive liquidity risk management enhances the bank's ability to handle potential shortfalls and comply with stringent regulatory standards.

Comprehensive Risk Management Framework

Oracle integrates various risk management functions into a unified framework, providing advanced analytics and reporting capabilities. This integration streamlines risk management processes and facilitates better oversight and governance, improving efficiency across the risk function.

Advanced Reporting and Analytics

With robust reporting tools for regulatory compliance and internal risk management, Oracle utilises AI and machine learning to uncover insights from risk data. This enhances transparency and accountability in risk reporting and empowers risk managers with actionable insights for informed decision-making.

Implementation and Scalability

Oracle's solutions are designed to seamlessly integrate with existing systems and scale to meet the bank’s specific needs. By supporting cloud-based deployment, these solutions provide flexibility and reduce infrastructure costs, thus minimising implementation time and expenses while offering a scalable solution for enterprise deployments.

Finally, the Oracle solution seamlessly integrates with Oracle EPM products, which most banks widely deploy across the risk function. Additionally, they can be integrated with other common platforms such as Microsoft Copilot, Amazon Web Services, and Oracle's extensive AI solutions running on Oracle Cloud Infrastructure (OCI).

Several architectural approaches are possible to build out a GenAI architecture across the risk function, but we believe the "Shaper" approach, defined by McKinsey, provides significant agility and scalability. 

Conclusion

GenAI has the potential to transform risk and compliance management in banking. By automating intricate tasks, offering expert insights, and facilitating advanced analytics, GenAI can significantly enhance banks' operational efficiency and effectiveness. To fully reap these advantages, banks must adopt a strategic approach to AI implementation, prioritise critical use cases, and invest in the requisite data and technology infrastructures (the topic of an upcoming blog).

Oracle’s suite of solutions tailored for financial services equips banks with the necessary tools to harness GenAI for improved risk management and compliance. By integrating these advanced analytics and AI tools, banks can proactively address regulatory requirements, identify and prevent financial crimes, and bolster their risk management capabilities, all while driving huge efficiency gains across the risk function.

How can we help?

Revvence can help in several valuable ways:

• Review your existing core finance processes and recommend GenAI use cases.
• Create a proof-of-concept to demonstrate potential outcomes and help build your business case.
• The design and delivery of end-to-end GenAI applications in the finance function.
• Check out Revvy, our Narrow-GPT for Finance Transformation. Read all about Revvy here.