Using Data Science for Better Risk Management
Risk management is the practice of identifying potential risks. Every organization faces risk, and they can come from a variety of sources, e.g. financial, legal, cyber, natural disasters, etc.
Once these risks are identified, they must be analyzed and evaluated based on the forecasted impact to the business and either accepted or mitigated.
Data science is enabling better risk management, but it only applies to those adept enough to use it well. For everyone else, technology is increasing the pace of business and compounding uncertainty.
With the global business environment moving faster than ever, there are more “unknown unknowns” arising at a faster pace. The result is a widening gap between firms capable of wielding data science tools in a modern risk management framework, and those simply hoping they can survive sets of problems they can’t even see coming.
If data science tools are going to put your firm in that first camp, what do you need to know about them?
The Data Science Basics You Need to Know
The most dangerous misconception about data science is that it simply boils down to gathering data and analyzing it. While that isn’t totally incorrect, it obscures the biggest challenges involved in good data science.
First among them is identifying the right problem you want data science to address, followed by getting good quality data (and enough of it). Fall short in any of those areas and you likely won’t get any real benefits from using data science.
One of the data science applications with the greatest potential is in risk management.
Big Data Potential for Risk Management
There are three main areas of opportunity in this area.
1. Fraud detection
When it comes to fraud detection, data science tools offer massive advantages over basic human monitoring. Ultimately, fraud detection boils down to pattern recognition, an area where machine learning tools excel. The reason harkens back to the first challenge of data science: knowing the right question to ask. Often the challenge of identifying patterns to prevent fraud comes down to figuring out which thousands of possible data points is the best predictor of fraud.
Machine learning is capable of identifying patterns which predict fraud. At times, these might be completely counterintuitive (something a human would likely never think of), but this ability to identify patterns while analyzing massive data sets faster than any human make data science tools ideal for the task.
Beware that there are some major challenges involved in this application. Using data science for fraud detection generally requires a very large data set, because fraud is hopefully a relatively rare phenomenon in your field. For example, if 1 out of 10,000 cases is fraudulent, a machine learning algorithm would need to see one million total cases to examine just 100 fraud examples.
Ideally, you should have a large historical data set which provides your machine learning model the right number of examples for effective fraud detection.
The results of good data science-backed fraud detection are remarkable, and the speed with which it can be detected and addressed translates into reduced costs and improved service.
Related resource: US State Auditor Deploys ML to Tackle Healthcare Fraud
2. Risk scenarios
A more expansive data science application is in risk analysis, i.e., determining where unseen risk might be present. Unlike fraud detection – where what you’re searching for is well-defined – the first major challenge here is defining precisely what you’re looking for. This is an area where your business management team needs to collaborate with data scientists to both define risk in business terms and translate that definition into something machine learning tools can search for. This step is critical, even if everything else goes right, the wrong definition will make it impossible for data science tools to identify it.
There’s a common saying amongst data scientists: “Garbage in, garbage out.” This applies to both the quality of the data you’re using, as well as what your machine learning model searches for with that data. Feed your model bad data – or poorly define what constitutes a risk scenario – and you won’t get solid results.
The aforementioned cooperation between business management and data scientists is paramount, and both sides need to have a greater understanding of your goals. Without that common basis of understanding, mistakes are more likely to happen. On the flip side, when both these groups work well together, machine learning models offer unparalleled capabilities to identify risk and even propose ways to best hedge against it.
3. Business model development
The problem of “unknown unknowns” is compounded when exploring or testing new business models. You need to accurately calculate metrics like customer lifetime value, optimal pricing, and the risk scenarios mentioned above. Fortunately, data science tools offer powerful capabilities for these applications, and for established businesses, applying machine learning to these challenges is relatively straightforward. You can either apply existing data sets or create experiments to gather data to analyze.
While the need for large amounts of data to test business model concepts may be a handicap for some firms, the capabilities of data science far outweigh those limitations. Assigning a machine learning algorithm for determining the optimal dynamic pricing system in order to optimize customer lifetime value, for example. Here, the capabilities of machine learning far outweigh what even the best MBAs and business analysts can do on their own.
Common Challenges of Risk Management Applications
While far from insurmountable, these obstacles are quite serious and need to be well understood and accounted for from day one.
Data may be lost.
A seemingly obvious danger in applying big data analytics to solve major problems is the increased risks associated with data loss. The more you rely on large datasets, the more valuable they become. Of course, these hazards are manageable with the application of data loss prevention software, alongside the same risk management systems you’d put in place for valuable assets.
Data may be invalid.
If your data is invalid, your results – and business decisions based on them – will be as well. It’s vital to work with data scientists to ensure that you have the right type of data, it’s organized in a way machine learning models can use it, and it’s valid. Focus intently on quality control from the bottom up.
Data may be too large to process.
While this is only a challenge for organizations dealing with truly massive data sets, it can still be an issue. If this is the case, either invest in greater processing power or work with data scientists to trim your data set into something more manageable but still capable of providing actionable insights. For most organizations, this isn’t a problem: Generally speaking, the more data you have, the more valuable risk assessment and management is.
Data may not be relevant.
Again we come back to the importance of cooperation and mutual understanding between data scientists and business management. To properly use data science for risk management, it’s essential to determine that your data is relevant. In other words, a manager may believe that a particular metric is predictive of fraud, insisting on telling a machine learning algorithm to assume as much. If that manager is wrong, however, you’ll likely feed irrelevant data into the algorithm, affecting the accuracy and validity of its results.
Why Use Data Science in Risk Management?
There are clear challenges which accompany the benefits of data science in this area, but major benefits as well. Here are some practical reasons to consider it
Powerful risk prediction models
Simply put, data science tools offer risk prediction capabilities far beyond what’s possible with human analysis alone. Dangers that you never knew existed may appear, but it also offers better understanding of those you’re already aware of. The result is better preparedness, more appropriate risk mitigation, and a general saving of time and resources across the board.
Faster response time
This is a game changer for most companies. Waiting (and hoping) that your employees notice problems in time to properly mitigate risk is…well…risky. When it comes to fraud prevention, hedging, or predicting which types of events may cause problems (e.g. a change in interest, exchange rates), time is a vital factor. An extra few weeks to prepare for any of these scenarios can mean the difference between handling a crisis or being driven out of business by it.
More extensive risk coverage
Identifying potential problems is the first step toward preparing for them. That’s why having the most advanced data science tools to identify potential areas of risk allows you to purchase additional insurance, hedge where appropriate, or simply invest more heavily in tools to better mitigate that risk. Knowing where to invest your time and money most effectively is essential, because what you think poses the greatest issues may not actually be where you need to focus your attention.
While preparing your data and analyzing it with the latest data science tools requires some initial investment, the ultimate cost savings are well worth it. From more straightforward savings (like fraud minimization) to gaining a better understanding of your own structural risk, what’s gained can be measured in both dollars and peace of mind. After all, there’s no way to quantify cost savings when you’re dealing with “unknown unknowns”. Therefore, the predictive understanding that comes from data science tools is invaluable.
How Can Big Data Help?
As discussed above, larger datasets are invaluable for detecting fraud due to its relative rarity. You don’t want to wait until a particular type of fraud has occurred before identifying and addressing it.
Understanding the uncertainty and potential issues of credit is a core business activity, but big data offers far more advanced ways to understand and mitigate the underlying risks. Whether this requires looking at your company’s optimal mixture of credit or analyzing macroeconomic risks, big data combined with data analytics is a powerful tool.
As with any type of fraud, the greatest challenge is noticing the relevant patterns before it’s too late. Big data has the unique ability to notice patterns which humans may have not even known to look for. Or, alternatively, to pick up on known patterns faster. In either case, data science tools combined with big data are optimal for tackling the risk of money laundering.
Similar to how big data can help in credit management, better risk management for loans occurs both on the micro and macro levels. That means both understanding the relevant performance measures of individual loans and analyzing macroeconomic trends that can affect them. Combining both levels of analysis translates into better loan performance.
Operational risk is often an undervalued way to analyze and understand a company. Focusing too much on external concerns – and neglecting the possibility of internal breakdowns – poses its own danger. A recent IBM study found that for many firms, operational risk is eclipsing credit risk. Fortunately, big data analysis also has the capability to analyze internal processes to better predict and prevent these kinds of system breakdowns.
Integrated risk management
Aside from using it to analyze the aforementioned individual areas of concern, big data also has the potential to perform macro analyses to understand how your entire risk portfolio interacts. That kind of top-level perspective is invaluable, particularly when you’re dealing with multiple areas of risk simultaneously and trying to understand how they interact.
Are You Ready to Take On Risk Management using Machine Learning?
Successfully applying data science to risk management requires cooperation between data science professionals and business managers. That’s why it’s essential to choose the right partner and the right tool.
Data science platforms like RapidMiner can help jumpstart your project. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. 400,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.
Interested in learning more about RapidMiner? Explore our offerings and find the solution best for your teams’ unique skillsets and preferences.
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