Big Data & 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 ReciprocityLabs.com.
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