Is Artificial Intelligence (AI) & Machine Learning (ML) a Game Changer in Risk Management?

Artificial Intelligence (AI) and Machine Learning (ML) techniques are creating waves within the financial services landscape. The banking industry which relies heavily on the use of data is increasingly beginning to adopt these techniques and has started to leverage their powerful capabilities.

From chatbots to fraud detection, the banking sector is now using AI and ML not only to automate processes and streamline operations for both the front and back offices but also to enhance the overall customer experience. AI and ML tools with their advanced prediction techniques and capabilities to utilize large volumes of data are increasingly being used in Risk Management for quicker and more efficient credit, investment and business-related decision making.?

Banks and financial technology (fintech) companies are implementing risk management systems with AI solutions to facilitate decision-making processes, reduce credit risks, and provide financial services tailored to their users through automation and machine learning algorithms. AI’s ability to analyze large amounts of information substantially improves the identification of data relevant for?cybersecurity risk management, risk assessment, and accurate business decision-making.

AI solutions are therefore able to generate large amounts of timely, accurate data, allowing financial institutions to build competence around customer intelligence, enabling the successful implementation of strategies and lowering potential losses.

AI and Machine Learning powered risk management solutions can also be used for model risk management (back-testing and model validation) and stress testing as required by global prudential regulators and may have the following key benefits:

-??????Superior forecasting Accuracy:

Traditional regression models do not adequately capture non-linear relationships between the macro economy and the financials of a company, especially in the event of a stressed scenario. Machine learning offers improved forecasting accuracy due to models’ ability to capture nonlinear effects between scenario variables and risk factors.

-??????Optimized variable selection process

Feature/variable extraction processes take up a significant amount of time for risk models used for internal decision-making purposes. ML algorithms augmented with Big Data analytics platforms are able to process huge volumes of data and extract multiple variables. A rich feature set with a wide coverage of risk factors can lead to robust, data-driven risk models for stress testing.

-??????Richer data segmentation

Appropriate granularity and segmentation have been shown to be critical to deal with changing portfolio composition. Machine learning (ML) algorithms are enabling superior segmentation and consider many attributes of segment data. By using unsupervised ML algorithms, combining both distance and density-based approaches for clustering becomes a possibility, resulting in higher modelling accuracy and explanatory power.

Some specific use cases that have benefited from AI integrated with?risk management?systems include:

-??????Threats Analysis and?Management

Machine learning engines are now able to analyze large amounts of data from various sources. This information generates real-time prediction models that allow risk managers and security teams to address risks quickly. The models are fundamental to develop early warning systems that assure the uninterrupted operation of the organization and the protection of its stakeholders.

-??????Risk?Reduction

AI also has the ability to evaluate unstructured data about risky behaviors or activities in an organization’s operations. AI algorithms can identify patterns of behavior related to past incidents and transpose them as risk predictors.

-??????Fraud?Detection

Fraud detection traditionally requires intense analysis processes for financial institutions and insurers. AI systems can substantially decrease the workload of these processes and reduce fraud threats by using machine learning models that focus on text mining, social media analysis, and database searches.

-??????Data?Classification

AI tools can also process and classify all available information according to previously defined patterns and categories and monitor access to these data sets.

The following procedure can be used to implement AI models within organizations both to reduce ‘AI risk’ and to take advantage of the benefits that these tools can bring to an organization:

-??????Ideation

The first step to implementing a risk management system supported by AI is to identify an organization’s regulatory and reputational risks. For this, a risk assessment needs to be conducted based on current frameworks and an organization’s values. This will be used to determine the data needed to collect and to determine how information is to be processed.

-??????Data?Sourcing

Based on previous risk assessments, it is possible to define which data sets are suitable for AI model processing and which ones are not. Careful consideration should be given about what data is to be used and where it is to be sourced from. Even at the operational level, choosing the right data sets influences the quality of the results, so data sourcing becomes a crucial step for the implementation of the ecosystem.

-??????Model?Development

Once useful data has become available, a useful model can be built. Here, the level of transparency is to be considered that is desirable in AI operations since some AI tools are not suitable for high-risk activities. In this regard, any regulatory limits should also be reviewed on how AI can be used for certain business processes and how AI will further business objectives.

-??????Monitoring

Like other risk management tools, the use of AI must be constantly evaluated and adjusted. It’s critical to consider the changing needs of the organization and the possible drawbacks that this technology may present.

-??????AI & Risk Assessments

AI technologies are particularly useful in risk assessment due to their ability to quickly detect, analyze and respond to threats. AI-powered tools such as user and event behavior analytics (UEBA) can detect, analyze, and respond to any anomalies that may indicate an unknown compromise. This reduces the number of false positives generated by traditional vulnerability detection tools.

When vulnerabilities are prioritized and contextualized, risk scoring is more accurate. For example, a legacy asset may indicate a potential risk but is overlooked. In contrast to traditional risk rating systems, AI can measure exposures and countermeasures independently. By analyzing them comparatively and weighing them in comparison with each other, risk scoring is derived with greater accuracy. This aggregation of information is not possible without AI.

Auditors use AI to analyze complete groups of data and transactions rather than sampling. This leads to a more complete audit and helps auditors identify anomalies that can be flagged for additional scrutiny. It also ensures that smaller transactions get a level of scrutiny where they previously would have been overlooked because of materiality constraints. Microsoft's latest security development incorporates large language models (LLMs). An LLM is a type of AI algorithm that uses deep learning techniques and large data sets to understand, summarize and predict new content. With the upcoming Microsoft Security Copilot, analysts will be able to quickly respond to threats, process signals and assess risk exposure in minutes. This is done using OpenAI's GPT-4.

Natural language questions can be asked to Security Copilot and actionable responses can be received. By identifying ongoing attacks, assessing their scale and receiving instructions for remediation, Security Copilot can prevent future attacks. This is based on proven tactics from real-world security incidents. Security Copilot can also be used for threat hunting. For example, a query such as “Have any suspicious log ins happened in the last 10 days?” can be made and answers are received instantly. If any security incident has occurred, Security Copilot can summarize any event, incident, or threat in minutes. It can also prepare a ready-to-share, customizable report and even prepare a PowerPoint slide on a security incident.

This new AI capability can also reduce the time spent drafting risk treatment plans after the risk assessment phase.?By leveraging AI, organizations can gain much more accurate results and proactively identify potential future threats and vulnerabilities. This enables organizations to put measures in place to prevent potential security threats from occurring and more effectively remediate existing risk gaps.

-??????The use of AI as a Dynamic Risk Assessment Tool

As risk assessments are conducted, a common consideration is whether controls are adequate and relevant. However, it has always been a challenge for the risk assessor to forecast if a needed control has been overlooked until there is an audit finding. With AI, it is possible to incorporate automated measurements in AI-based systems to improve the accuracy of predicting expected outcomes and to instantaneously verify that the actual values match the predictions. This approach creates an innovative form of control verification that is proactive in nature.

It is highly likely that risk managers and auditors will no longer need to limit themselves to the evidence provided. Algorithms such as deep learning will be able to extract meaningful and contextual information from a stream of distinct sources such as contracts, conference calls and emails. This information can serve as supporting evidence. When updated data arrives, an AI system has the potential to immediately analyze it and turn it into actionable information. With deep learning algorithms, the continuous control monitoring system can reconfigure itself based on the feedback from the previous set of results. This approach can help ensure that controls are designed, configured and implemented optimally with minimal human intervention.

However, in order to implement AI technologies effectively, organizations must consider the risk they want to assess and manage, the data they want to collect, and the associated challenges such as data protection.

The first step in incorporating AI into a risk assessment strategy is to identify regulatory, financial and reputational risk. It is also crucial to identify what data should be collected and how it should be processed based on the current risk framework and organizational values. The AI model of processing data sets can be defined based on previous risk assessments. The type of data to use and the sources are critical considerations. Data sourcing is crucial for the implementation of an ecosystem, even at the operational level, as it influences the quality of the results. As with other risk management tools, AI must be continually evaluated and adjusted.

Conclusion

Over time, AI has the potential to be transformative. By automating and using machine learning algorithms, many financial institutions and organizations can facilitate decision-making processes and provide services tailored to their users. Because AI analyzes large amounts of information, it significantly improves the ability to identify risk-relevant information and, therefore, it is expected that risk management will become more dynamic with AI’s continued adoption.