Big Data and Risk Management

Big Data and Risk Management
Big Data and Risk Management

The Big Data age is finally here. Information is currently available in unbelievable proportions measured in what is globally known as zettabytes (ZB). One zettabyte represents a billion terabytes. The incredible thing is that the information proportion is still growing exceptionally.  In fact, the IDC revealed that global data would increase from 23ZB in 2017 to 174 Zettabytes by 2025.

Depending on the type of your organization or the industry you are in, you could be having massive volumes of internal and external data readily available for creating viable projections, mining, and applying predictive analytics.

Using data gives entities the power to boost the customer experience, effectively direct operations, and enhance income streams. Generally, the health of your organization improves significantly when data is assessed accurately. Furthermore, big data is an essential and powerful risk management tool.

Consider the various human interactions that generate data, including vendor transactions, financial interactions, app experiences, emails, webpage views, and social media posts. These interactions offer an excellent opportunity to obtain organizational risk insight, which facilitates the reduction and assessment of threats.

Once your business uses big data in managing risk, you will get a comprehensive overview that assists you in structuring your financial revenue streams. This means that in case you are utilizing big data in managing risk, you may not be utilizing all that information to benefit your company.

How to Boost Risk Management Using Big Data

To comprehend how you can utilize big data in organizational risk management, it is vital to assess the critical risk management principles.

Risk is virtually part of every company’s decision. Avoiding risk is difficult, particularly when a business is seeking to diversify products, achieve a new goal, or grow. Nonetheless, the decision-making process regularly involves uncertain results. ISO 31000 defines risk as to the impact that uncertainty has on objectives.

The answer to dealing with all that uncertainty lies in risk management. The main risk management elements are prioritization, evaluation, and identification of risks, and not to mention, the steps involved in reducing the negative risk aspects such as controlling and monitoring. Each of these aspects in risk management boasts a direct correlation specifically to the use of big data.

The sizeable historical data stores and real-time big data analytics deliver a considerable system for extracting useful information instantly. When integrated with robust analytics that analyzes potential risks, companies can reduce uncertain objectives while increasing their clarity in making decisions.

Big data can be applied across different industries as opposed to just the fintech industry, which for a long time has been using data systems in weighing risks and evaluating opportunities.

The application of big data in managing risk can prove useful in various industries including e-commerce, manufacturing, retail and healthcare and can be used in a wide array of corporate threats, including regulatory risk and business impacts.

Big Data Applications in Specific Risk Management

Vendor Risk Management: Third-party associations can generate regulatory problems, as well as pose a threat to your company’s operations and reputation. Vendor risk management helps you in evaluating the severity of risks, selecting vendors, and creating internal controls for mitigating risk.

Money laundering and Fraud prevention: predictive analytics give a comprehensive and precise technique of preventing and mitigating suspicious/fraudulent activity, which is necessary in a period where money laundering actors are applying sophisticated methods. Numerous significant data risk mitigation and management methods are used by governments and global lending entities, including unit price, text, unit weight analytics, web, and trade partners’ relationship profiles that are useful in identifying shell companies.

Spotting Churn: Churn is a significant organizational risk. Losing customers affects the bottom line considerably. In a white paper, Fred Reichheld claimed that customers enable a business to generate more profit every year they stick with a given company. For instance, a 5% rise in the retention of customers in the financial services industry produces a profit increment of over 25%.

Credit Management: Credit management risk can be reduced by assessing the data relating to both historical and recent expenditure, not to mention the patterns of repayment. New sources of big data, including customer interactions with banking or financial institutions, mobile airtime purchases, and social media behavior boost the ability to analyze credit risks.

Manufacturing sector-related operational risk: big data can provide various parameters that help in assessing supplier dependability and quality levels. Sensor technology data can also assist in detecting costly production defects early enough.

Real Estate: Even though location is highly vital, determining the ideal spot can be a risky process. Starbucks is among the well-known leaders in the application of big data to grow. The company utilizes a predictive tech platform that assesses various demographics, including average income, maps, and traffic patterns in the recommended location, effects on the other stores around, and identifies the profit and feasibility potential of new store openings and real estate purchases.

With that said, is your company leveraging predictive analytics and big data in managing risk as well as minimizing your uncertain organizational outcomes?  Bear in mind that the risk management applications, such as big data, are limitless and ever growing.

Author Bio

Ken Lynch is an enterprise software startup veteran, who has always been fascinated about what drives workers to work and how to make work more engaging. Ken founded Reciprocity to pursue just that. He has propelled Reciprocity’s success with this mission-based goal of engaging employees with the governance, risk, and compliance goals of their company in order to create more socially minded corporate citizens. Ken earned his BS in Computer Science and Electrical Engineering from MIT.  Learn more at

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