Risk management is all about solving complex issues for businesses. Problems in business or life could generally be defined as a recognizable pattern that results in a negative outcome for someone or something. That simple statement (data with a pattern) is all that is required to make a machine learning program.
Artificial intelligence is now baked into every major software platform businesses and people use today, primarily via machine learning algorithms. ML is simply a subset of AI where software can learn from data automatically without needing programming or a human touch. For example, anytime Youtube surveys you or Grammarly asks if it made a correct recommendation, you feed a data stream information to identify a pattern and improve a machine learning model.
There are five parts of risk management, and this article will cover how AI and ML are used in each respective sector.
Risk identification is the process of determining potential risks for a company and documenting the actual risks it faces. Every time a problem is identified and categorized, you create a dataset that can be used to train a machine learning model. Currently, ML in identification is used in “Clustering.”
Clustering is the process of using large datasets to find outliers in a set. It is the opposite of pattern identification, filtering out everything that is not within a margin of error for the pattern, thus finding anomalies.
In accounting, risk management clustering is used to identify costs that are out of the ordinary or investments that seem out of place. It is also used in some CPA programs to identify locations of cash flows to find anomalies in financial statements.
ML identification is also used in employee surveillance software to identify behavioral patterns of troublesome individuals who create possible OSHA compliance issues. This has recently been growing in popularity for remote workers to confirm they are indeed working and not just using FB on company computers.
Analysis of risks is the second step in risk management and is the most data-driven. Once a risk is identified, the next step is valuing the potential damages that may be inflicted by said risk and determining the level of preparedness a business needs to have related to the issue.
Typically, AI-based ML is used in analysis based on investment risk and loss, usually related to already owned assets. Share predictive software for short and long-term investments will use machine learning models trained on prior market experiences and news posts made that day.
Although AI analysis has come a long way in predictive asset valuing, it is only as good as the data it has been given. It is impossible to collect information from the future, so if a new situation occurs in which the ML has never been trained, it will result in poor valuing of the potential situation.
We saw this occur with COVID-19 and the cryptocurrency market crash and rebound. Stock picking software did not foretell the impending impacts of the virus and thus failed in short and long-term growth picks. That situation was out of the dataset, and possible past examples were too old to be considered similar due to various other factors. As long as an analysis situation is a repeat of a documented problem, there is a good chance the ML will result in a good value to the situation.
“What are we going to do?” is the main question of planning. This is where the true beauty of machine learning shines. Creative ML most famously exists in Google’s Imagen software which makes art, but there are even more extraordinary cases in which programs have solved odd problems creatively.
One situation in which machine learning can be used in business planning is predicting customer churn and solutions to reduce it. There are open source APIs that you can integrate into your software platforms for that use specifically.
ML can also be used in the planning stages to test plans for certain situations. Perhaps you want to test whether a cloud server or a local personal server is the safest way to store data. Well, you can have an ML program run system stress testing if you plan on either rapidly growing a customer database or expecting cyberattacks. You will also find planning in business analytics software that offers solutions to reduce costs.
AI in mitigation has yet to reach the heights of the other risk management categories; however, it has found use cases in reducing employee exposure to hazards.
There are a few startup companies that will map your business’s physical layout and determine which locations have the highest probability of physical harm to employees and which cause the most in insurance claims. This is done by using datasets from the greatest danger and comparing them to the physical layout of various workplaces.
This technology is also used in construction to place materials more efficiently, reduce time, and put large equipment in safer locations. It is also found in workplaces with hazardous gasses and materials such as large-scale wineries that can cause CO poisoning. Although there are few examples of mitigation software that use ML, they may be the most practical of the five categories to reduce insurance, reduce employee danger, and increase productivity.
As business problems continue to change, risk managers need to keep an eye on the situation. Wells Fargo Bank has been open about where they use machine learning in their company. They have been quoted by Agus Sudjianto, EVP, Head of Corporate Model Risk, as using it for financial crime or conducting surveillance with miss detection for compliance risk.
As stated earlier in the article, ML is great at identifying patterns and outliers from that expected pattern. Similar to the examples above, there are a variety of ML surveillance systems for virtual workers, physical building security, digital cyber threats, and many others.
As our world continues to be more data-driven and reliant on AI, you will start hearing about software solutions that could make most risks un-risky. With risk mitigation being one of the most data-driven jobs in the world, continue to collect and log risk experiences and results to perhaps use as a base for your AI-based ML program. All you need is a pattern.