Applying Big Data to Risk ManagementPublished June 13, 2019 by Ben Lack • 4 min read
The era of Big Data is here. Information now exceeds fantastic proportions, globally measured in zettabytes (each zettabyte is a billion terabytes) and growing at an exponential rate that defies comprehension. According to the IDC, global data is expected to grow from 23 Zettabytes (ZB) in 2017 to 175 ZB by 2025.
And depending on your industry and specific organization, you likely have plentiful external and internal data sources readily available for mining, applying predictive analytics and creating viable projections.
Leveraging data allows companies the ability to improve income streams, more effectively direct operations and enhance the customer experience.
Overall your organizational health improves dramatically when data is accurately assessed.
But big data also is a powerful – and vital–tool for risk management.
Think about all those human interactions that produce data— including social media posts, app experiences, webpage views, emails, financial interactions and vendor transactions— as well as streaming data from the Internet of Things (IoT) that impact your company. These can provide a wealth of opportunity to gain insight into organizational risk, which allows for assessing and minimizing threats.
When your company applies big data to risk management, a detailed picture emerges that helps structure financial revenue streams and apply predicative indicators to increase organizational growth.
In short—if you aren’t using big data in risk management, you’re not optimizing all that information for the greatest good of your company.
How to Improve Risk Management with Big Data
To understand how big data can be used in managing organizational risk, it’s helpful to review essential principles of risk management.
Risk is an aspect of nearly every business decision. It’s impossible to avoid risk, especially when a company seeks growth, diversifies products or attempts to achieve a new objective. Yet, decision-making often involves uncertain outcomes—a point ISO recognized when defining risk. According to ISO 31000, risk is the “effect of uncertainty on objectives.”
What to do about all that uncertainty? The solution can be found in risk management.
The fundamental elements of risk management are the identification, evaluation, and prioritization of risks, as well as steps taken to minimize the negative aspects of risks, such as monitoring and controlling.
Each of the elements in risk management have a direct correlation to the application of big data.
The vast stores of historical data, as well as real-time big data analytics, provide a significant system to extract valuable information instantly. When coupled with robust predictive analytics that assess possible risks, organizations can decrease uncertain objectives and increase clarity in decision-making.
And big data in risk management is applicable to all industries, not just the fintech industry, which has long used data systems for evaluation of opportunities and weighing risks.
Big data risk management has a place in healthcare, retail, manufacturing, and e-commerce organizations and can be applied to a substantial variety of corporate threats, such as business impacts and regulatory risk.
Those uncertain outcomes can be greatly diminished via Big Data in your risk management toolbox.
Specific Risk Management Applications of Big Data
Vendor Risk Management (VRM): Third-party relationships can produce regulatory nightmares, as well as reputational and operational risk. VRM allows you to select vendors, assess the severity of risks, monitor and establish internal controls to mitigate the risk, such as firewalls or multifactor authorizations.
Fraud and Money Laundering Prevention: Predictive analytics supply an accurate and detailed method to prevent and minimize fraudulent or suspicious activity—vital in an era where money laundering traffickers have become more sophisticated in their techniques. An arsenal of big data risk management and mitigation techniques are applied by governments and international lending institutions, including web, text, unit price, and unit weight analytics, as well as relationship profiles of trade partners, which help identify shell companies.
Identifying Churn: A significant risk to organizations is churn. The loss of customers deeply impacts the bottom line. In the white paper, Prescription for Cutting Costs, by Bain & Company, author Fred Reichheld, states, “Customers generate increasing profits each year they stay with a company. In financial services, for example, a five percent increase in customer retention produces more than a twenty-five percent increase in profit.” Customer loyalty can be identified using big data as a risk management tool. And, based on the data, companies can expedite measures to decrease churn and prevent customer defections.
Credit Management: Risk in credit management can be lessened by analyzing data pertaining to recent and historical spending, as well as repayment patterns. Novel big data sources, such as social media behavior, mobile airtime purchases (considered a possible indicator of credit worthiness) and customer interactions with financial institutions increase the ability to assess credit risks.
Operational Risk in Manufacturing Sectors: Big data can supply metrics that assess supplier quality levels and dependability. And internally, costly defects in production can be detected early using sensor technology data analytics. Other applications, including continuous, real-time monitoring of production data has been shown to optimize integrity of manufacturing products, according to LNS Research. With an estimated 13.5 billion connected devices in production facilities by 2022, the application of big data can transform the manufacturing sector.
Real Estate: Location is key. But determining the right spot can be extremely risky. One of the leaders in applying big data to growth is Starbucks. The company uses a predictive technology platform that evaluates numerous demographics, such as traffic patterns, maps, average income in the suggested location, impacts on other stores, and determines feasibility and profit potential of new real estate purchases and store openings.
What about your company? Are you making the most of big data and predictive analytics to manage risk and decrease your uncertain organizational outcomes? The applications for risk management, like big data, are ever growing and limitless.