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How Can Data Science Improve Risk Management

PostedOn: 2018-04-03 13:02:42

Knowledge is power, and more data can translate into more insights. Risk management has always been a matter of measuring. The process aims to quantify the frequency of loss and multiply it by the severity of the damage. Therefore, data should be at the core of it, primarily since every transaction generates data and metadata which can be included in models. Yet, to be useful, it has to be correctly selected, filtered and analyzed. 

Implementation of data science in an organization requires a dedicated strategy to avoid sub-par results and information overload. When done correctly, it can offer a competitive advantage, insights and even new ways to tackle old problems.  In a nutshell, risk management through the lens of data science becomes the ability to transform data into action. 

How Can Data Science Change Risk Management?

Employing Data Science to evaluate risks is a cross-disciplinary effort. For a thorough analysis, it is necessary to have knowledge about the domain, excellent mathematics and statistics skills, and an innovative approach to problem-solving. 

We also live in an era witnessing an exponential growth of data. This requires both a strategy and computational power. Data is so ubiquitous that its irresponsible management can be a risk in itself. The challenge now is to gather the right data, clean it and store it in a way that it can be quickly accessed and reused. 

Evaluating Risks

Depending on the scope of the organization, different risks should be focused on. A report from The Economist lists the types of risk by industry:

  • Retail banks worry about credit risk;
  • Commercial banks focus on market risk;
  • Investment banks are most concerned about operational risk.

The same document shows that over 80% of the surveyed bank representatives already consider data science as a viable option to measure and mitigate risks. They are most scared of credit and liquidity risks. Big Data could offer enough insights through linking seemingly unconnected events to highlight a possible liquidity crisis. 

Quantitative Techniques for Risk Management

Quantitative assessments are replacing intuition and customary practices. Machine learning algorithms and data science models help financial organizations understand the nature of the risks and cope better with regulations. The advantage is that a broader set of data points offers a more accurate prediction. The only drawback is the computational difficulty which will be discussed in the next section. Here are some of the most widely used tools for risk assessment. 

Linear regression is the workhorse of numerical algorithms. This method relies on historical data to produce predictions about the future. In the case of missing values, the general practice is to use external data such as indexes.  Regression is an excellent choice for optimal portfolio allocation, goodness of fit, and specifically for computing solvency-related risks. 

Classification algorithms can help with credit risk assessment. The usual classes are the default and non-default clients. A recent study highlighted that the imbalance between the sizes of these classes can create prediction problems. These are caused by the preference for the majority class (the non-defaults) which can give overly optimistic scores. 

Clusters are created based on a single parameter, thus not considering interactions. In risk management, these are far too important to ignore. To overcome this problem, neural networks are useful due to a dynamic approach. For example, existing models in the insurance industry are static and don’t take into account the continuous changes in the environment. Using a trained algorithm also helps avoid subjective evaluations.