Across the industry, Artificial Intelligence (AI) continues to revolutionize the financial services sector and transform the way businesses operate.
From automating routine and manual processes, like qualitative risk management, to helping improve the customer experience, AI and Machine Learning (ML) are helping financial services firms become more efficient with both their time and money.
Essentially, AI helps financial services firms:
- Perform quantitative risk analysis using algorithms and machine learning, preparing them for near-term threats.
- Process and analyze large amounts of information much quicker than is possible through human efforts alone
- Improve the accuracy of their risk management processes and make them more efficient and manageable.
What is Quantitative Risk Management
Quantitative risk management is the process of assessing and quantifying risk exposure with a numerical value, helping companies identify potentially adverse outcomes that may impact the business.
By using relevant data and statistics, firms can create mathematical models from general assumptions about the market and their business and use that to determine their perceived level of risk in real-time.
Overall, quantitative risk management AI models can give firms actionable data and visibility into future threats through predictive risk analysis, which can help them make better data-driven decisions.
Common examples of quantitative risk models used in the field include sensitivity or scenario analysis. Depending on the output of such models, financial professionals can make informed decisions and act in the best interests of their firms and clients.
Mainly, quantitative risk management is utilized by asset managers and investors, as they can analyze the risk of their investments and assess how the overall portfolio is affected by changes in the values of individual securities.
Challenges to Quantitative Risk Management
While quantitative risk management can be highly insightful to portfolio managers and investors, it is not necessarily a straightforward task that is easy to conduct.
- Involved and Complex Processes: Requiring in-depth knowledge around the various calculations, the necessary inputs, and how to interpret the outputs of each model, quantitative risk management and predictive risk analysis are very involved and complex processes, which may keep financial professionals from utilizing them.
- High-Quality Data: Running a quantitative risk model requires mountains of high-quality data as inputs. This type of data is not always widely or readily available in the financial world, and running a quantitative risk model with not enough or low-quality data can compromise the results and insights gained.
- Unique Skills Required: When done manually, quantitative risk management with probabilistic or statistical modeling can be highly complex, which may not be a capability that all firms possess. So while quantitative risk management can be more objective and meaningful to firms than qualitative risk assessment, not all firms have the data analysts or in-house modeling capabilities to maintain these models consistently.
3 Benefits of AI and Machine Learning in Quantitative Risk Management
AI and ML models are transforming quantitative risk management and making it more accurate and accessible within financial services firms. Combining new and traditional models to assess risk from both structured and unstructured data, AI makes it possible for companies to have a complete picture of potential threats to their business.
By filtering through and assessing large amounts of data much more effectively than humans can, AI in risk management is helping firms to accurately model their risk and draw insights from its models around the clock.
While it is typically complex to build out mathematical models and manually alter inputs to determine all possible outcomes, financial teams can now automate many of these processes through technological innovation.
AI allows financial services firms to implement quantitative risk management into their business easily and understand their exposure to adverse events with predictive risk analysis.
Benefit 1 – Better Data Analysis With Natural Language Processing
Companies that integrate AI into their quantitative risk management can benefit from its natural language processing capabilities. Interpreting the tone of text data and giving it a quantitative value through sentiment analysis can be extremely useful in the risk management process and allows unstructured or qualitative data to be used in these mathematical models when assessing risk.
The more data points that can be input into these models, the better and more accurate picture quantitative risk management can provide for firms. With natural language processing, financial teams can conduct data analysis in a much more comprehensive manner by considering a wide range of inputs and giving concrete insights into qualitative data. Overall, AI in risk management helps firms make data-driven decisions and extract market sentiment in an actionable way.
Benefit 2 – Automating Manual Tasks
Of course, a significant benefit of using AI in any business is its ability to automate manual or repetitive tasks. AI in risk management is extremely valuable throughout the financial services industry, helping a firm become more efficient and allowing employees to turn their focus to more value-add activities.
Up until recently, data analysis has been a manual process, which was both tedious and time-intensive. With AI and machine learning, financial professionals can delegate data processing tasks to the algorithms, which are much quicker and more efficient in analyzing data and extracting meaningful insights.
For quantitative risk management, AI has been groundbreaking in automating the process and analyzing the large amounts of data required for these models. This automation frees up time for financial professionals, makes quantitative risk modeling and predictive risk analysis more accessible for all firms, and leads to better decision-making overall.
Benefit 3 – Better Fraud Detection
Investors are taking advantage of the advanced capabilities and increased efficiency of using AI in risk management throughout the industry. Specifically, AI and ML are helping to detect patterns of fraudulent stock market orders. They can identify when trades in the market are occurring to artificially inflate the price or volume, which could pose a risk to investors and their portfolios.
The implications of this capability are immense and can help investors detect actual investment opportunities over manufactured ones. Rather than staying alert and combing through individual trades to determine whether the momentum is warranted or risky, investors can rely on the algorithms to give them real-time alerts when fraudulent trading is detected.
Overall, investors can make more informed investment decisions and have a clearer view of the market risks and forces.
Altogether, AI and ML are revolutionizing the quantitative risk analysis space and are making it more accessible and accurate for financial services firms.
What was typically a tedious process to build out risk management models and perform complex mathematical equations manually, AI algorithms simplify the process and allow financial institutions to extract valuable insights on risk exposure with ease.
Using No-Code AI in Quantitative Risk Management
Machine Learning (ML) and AI in risk management are becoing increasingly common among financial services firms to stay competitive and better protect themselves from adverse events. With a fast and automated solution, such as the Accern NoCodeNLP Platform, financial professionals can have sentiment analysis at their fingertips, without technological complexities.
Watch this 3-min video to see how asset managers can leverage the Accern NoCodeNLP Platform to receive fast and accurate insights from text data to manage risk in investment decisions.
Schedule a demo to learn more about the Accern platform and how it can drive unstructured insights for your business ROI.Share this Post!