Risk management in business is a very old problem that can benefit from some brand-new tools. Big data analysis is one of the most important of these tools. As the saying goes, knowledge is the key to power — and modern organizations have quite a bit of accumulated knowledge — data — they can use to examine historical data and even monitor real-time events.
With the right strategy and the right amount of high-quality data, companies of all kinds can better prepare themselves to avoid, mitigate and respond to risk before it becomes a costly blow to their reputation or financial solvency.
Deep Learning for the Detection of Fraud
There is a branch of certified public accountancy (CPA) that is focused squarely on the detection and amelioration of financial fraud. It’s called certified financial forensics (CFF), and its mission is to use a company’s collected financial records to detect patterns, flag anomalies for review and generally bring a degree of oversight to a company’s back-office processes.
This is a labor-intensive process. So, not surprisingly, this is one of many processes increasingly entrusted to big data systems and analytical tools. In fact, detecting financial and other types of fraud is something which more and more businesses are choosing to automate. Data visualization models help humans and machines alike find hidden patterns and discrepancies through deep learning and entity mapping, among other techniques, even in real-time.
In other words, managing the risk of fraud has become proactive rather than reactive — and through data analysis, companies can position themselves to identify problems before they, well, become problems. Bringing deep learning platforms into the mix is a great way to free up accountants to focus on other critical work — like longer-term financial planning — and to outsource the task of policing financial records for errors or worrying trends to a much more observant and impartial entity.
Artificial Intelligence for Modern Compliance Efforts
It’s popular to speak about regulations as burdensome or unnecessary, but compliance requirements exist for a very good reason: to keep people safe. The thing is, regulatory bodies are only one piece required for maintaining compliance and reducing risk at the enterprise level. The second involves building a top-down company culture with integrity as a core value. The third is knowing how to choose the right technology.
There are multiple federal agencies and sub-agencies devoted to helping companies achieve compliance, including the Department of Treasury, the Financial Crime Enforcement Network and the Financial Industry Regulatory Agency. And these are just the bodies responsible for financial services fraud and compliance. Even with all of this oversight, some 47 percent of executives indicated in a recent poll that their company had experienced financial fraud of some kind. To address the gaps in regulatory oversight and poor company culture, we need the right data and the right tools for dissecting it through the lens of relevant regulations and compliance guidelines.
Artificial intelligence has staked its claim here. Companies in the health care and pharmaceutical fields are prime examples of why mindfulness of compliance is so important — and they prove how AI and big data analytics can help. For example, preventing overbilling is of vital importance in maintaining compliance and fighting fraud in these industries. In this case, intelligent data analytics can catch patterns even human auditors might miss — including singling out providers who consistently use the highest-value reimbursement codes during their services billing process.
Algorithms for Predictive Modeling and Capturing Volatility
At the risk of getting lost in the weeds of computer science, let’s take a look at some of the algorithm types working behind the scenes in helping companies avoid fraud and manage their risk.
In the first example — linear regression — the goal isn’t to detect fraud after the fact, but instead to predict it. Linear regression is the practice of mining historical company data to find strongly related variables. It’s not the same as proving causation outright, but it does help statisticians and data scientists appraise the strength of the correlation between an event — a “red flag” — and the likelihood of the associated risk to actually occur.
As an example of how this might be helpful, consider that a certain high-profile “family company” may have had all the data it needed, years ago, to determine the future likelihood of its products causing cancer in customers.
There’s another algorithm-driven approach to risk mitigation emerging, and it arrives in three parts. In many industries, including insurance, unforeseen events occur with a high enough frequency that existing methods like “clustering” just don’t work without some extra help. To solve this problem, data scientists propose using clustering with two other components: a neural network “training module” as well as a back propagation “prediction module.”
As you can probably tell, the conversation gets complicated pretty quickly. The point here is that looking for correlations between past events and future risk isn’t that useful without some kind of intelligence working in real-time to fold new information into the mix and adjust the expected degree of risk accordingly.
Big Data Is Just One Tool for Managing Risk
As we mentioned above, there are three pillars to effective risk management in modern times: oversight for regulatory compliance, a strong company culture focused on making the right hires and training on the right principles, and the wise application of useful technologies.
One of the most important advantages of bringing data analytics into the mix versus relying on the other two pillars alone is that the company’s analytics platform gets smarter with each new data point it receives. And that system and its insights stick around no matter how much employee turnover your organization has to weather, too. In other words, your next compliance officer or accountant doesn’t have to learn your whole system and risk profile from scratch.
On the other hand, this also raises the bar for bookkeepers and risk management experts. New hires in these fields will, in the very near future, probably depend a great deal on a working knowledge of machine learning, deep learning, natural language processing and computer vision for staying ahead of the curve — and ahead of risk.
