The Role of AI and Data Analytics in Strategic Risk Management

What is Strategic Risk Management?

Strategic risk management refers to recognising, quantifying and mitigating any risk that can affect any organisation’s business strategy and the strategic execution and objectives. Over the years, with the increase in the significant risk exposure comprising the financial and banking crises, there has been an increased focus on strategic risk management. In this regard, it has been an important task for the executive management and board to make important decisions in various areas, such as regulatory change, consumer demand and preferences dynamics, technological changes, competitive pressure, and stakeholder pressure (Bussmann et al. 2021). In addition, the other area which needs monitoring includes merger integration and senior management turnover—comprehending the risk need to look at the distribution of the potential outcomes.
With the help of two metrics, strategic risk can be measured. These are risk-adjusted returns on capital and economic capital. Economic capital refers to the amount of equity that defrays unexpected losses based on the solvency standard. On the other hand, RAROC refers to the anticipated return of after-tax. If the RAROC is more than the organisation’s cost of capital, it will add value to the initiative.

AI in Risk Management

AI and Machine Learning techniques are resulting in waves within the financial landscape. The banking industry, which mainly depends on the use of data, is adopting the techniques while they leverage its powerful capabilities. It uses AI to streamline operations, automate processes, chatbots and fraud detection. In strategic risk management, AI is used for efficient credit, business-related decisions, and investments. Besides improving efficiency, it increases productivity while reducing costs. All this has been possible with the ability of technology to handle large numbers of unstructured data at great speed and a low level of human intervention. This has enabled the banks and financial institutions to reduce operational, compliance and regulatory cost while offering the bank decision-making capability related to credit (Bussmann et al. 2021). Thus AI helps generate huge amounts of accurate and timely data, thereby allowing financial institutions to create competence around customer intelligence, which further helps in successfully implementing strategies. Strategic risk management powered by AI also acts as a risk management model with benefits such as forecasting accuracy. Machine Learning helps develop the forecast accuracy that helps capture the non-linear effects between the risk factors and the scenario variables. ML algorithms and Big Data Analytics help process huge volumes of data and help in taking out multiple variables (Leo et al. 2019). A superior segmentation is allowed by the ML algorithms, and the various other attributes of the segment data are considered. AI helps in fraud detection as banks use machine learning for credit portfolio purposes. Transaction made with the help of credit cards entails a rich source of information. In other words, the credit card payment system comprises workflow engines that help monitor card transactions, which is done to assess the likelihood of fraud. Thus AI helps distinguish certain features present in fraudulent and non-fraudulent transactions.
In addition to this, AI also helps in data classification. It helps classify available information per the earlier defined patterns and categorises them accordingly. The AI-first identifies the regulatory and reputational risk of the organisation. Based on the earlier risk assessment, AI chooses the right data sets that influence the results’ quality.

Data Analytics In Risk Management

The market is constantly evolving today, and it isn’t easy to keep up with. To meet consumer demand and expectations, it is required to glean a large amount of information and organise it so that correct insights can be found to acquire a competitive advantage. A study by Deloitte found that 55 per cent of businesses believe that data analytics helps develop competitiveness. In addition to this, data analytics gives suggestions to resolve risks. In other words, risk management in business operations. Advanced data analytics helps in processing huge volumes of unstructured data in real-time. In addition, it also helps identify and predict trends that help minimise costs without sacrificing the services. By analysing the data produced by the company, data analytics helps monitor the performance across the various business units. It helps in giving insights into where the risk can be managed better (Leo et al. 2019).
Strategic risk management is broken down into different stages. These are risk assessment, identification, risk mitigation and monitoring and reporting. The risk indicators are found in the internal and external areas. The internal risks comprise the operating costs, capital flow and business processes. On the contrary, external risk comprises regulatory requirements, political changes, and macroeconomic fluctuations. In the present day identifying the risks is a great task, and this can be done with the help of data analytics. In other words, risk analytics can create complex risk profiles which assist in creating accurate risk management (Araz et al. 2020). Addressing the risk needs data-driven insights, which are mainly oriented to action. Thus, risk analysis helps monitor the effect of the actions that are taken and helps track the progress over time. In risk management, there are mainly five approaches: avoidance, retention, sharing, transferring and loss prevention and reduction.
Data analytics helps in identifying and mitigating the product and the service-related risk. This comprises warranty coverage and safety issues. Depending on the type of business, certain legal liabilities need to be addressed, which is done with the help of data analytics. It also helps in predicting cyber attacks as there is an increase in the rise in cybersecurity threats (Araz et al. 2020). The data analysts enable the security analysts to make anticipation from the patterns. The organisation uses predictions to detect and resolve vulnerabilities before they cause any damage. Data analytics also helps in marketing intelligence. It is capable of gathering feedback from campaign insights and feedback from customers. Therefore it determines whether the market is strong and, if not, whether baby corrective actions shall be taken. In other words, integrating data analytics helps in redefining the experience of the customers. Data analytics also help in catastrophe management, which implies that it helps identify areas prone to earthquakes, floods and other disasters.

Machine Learning In Risk Management

Machine Learning helps organisations gain insights and improve the process involved in the business. There are reputational, regulatory and ethical risks involved. Machine Learning uses computer-driven algorithms to detect trends and patterns from data. The insights gathered help the business make data-driven decisions, develop performance, and form a competitive advantage. However, it becomes difficult to interpret the algorithm in the black box machine learning model. For instance, the corporate world, such as the bank, uses machine learning to assess whether a client can be a good person for a loan. In other words, the model uses the information to assess if the customer is a default on the loan, which can enable the bank to deny the credit application.

Furthermore, machine learning helps improve the loan application but can also lead to unfair and biased decisions. Machine learning is used in healthcare to develop diagnostics and various other medical practices. In the criminal justice system, this is being used to predict the recidivism rate, which can be racially biased.

Machine learning also helps manage the operational risk arising from internal and external breakdowns. In the present years, operational risks have become frequent and complex (Araz et al. 2020). In this regard, machine learning has helped estimate the risk of exposure to operational risk, identify risk mitigation strategies and look for instruments that help in trading or shifting risks. In addition to this, it also helps in monitoring individual traders by combining electronic communication records and trade data.

References

• Bussmann, N., Giudici, P., Marinelli, D. and Papenbrock, J., 2021. Explainable machine learning in credit risk management. Computational Economics, 57, pp.203-216.
• Leo, M., Sharma, S. and Maddulety, K., 2019. Machine learning in banking risk management: A literature review. Risks, 7(1), p.29.
• Araz, O.M., Choi, T.M., Olson, D.L. and Salman, F.S., 2020. Data analytics for operational risk management. Decis. Sci., 51(6), pp.1316-1319.

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