Applying Big Data to Risk Management

The era of Big Data is here. Information now exceeds fantastic proportions, globally measured in zettabytes (a zettabyte is 1 billion terabytes), and growing at an exponential rate that defies human comprehension. 

According to research firm 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 for better insight about operations allows a company to improve income streams, direct operations more effectively, and enhance the customer experience. Overall, your organizational health improves dramatically when data is accurately assessed.

But big data is also a powerful, and vital, tool for risk management, too. Before we get into big data’s role in financial risk management, let’s briefly define what it is.

Defining Big Data

The term “big data” refers to the structured and unstructured datasets that an organization collects as part of its day-to-day operations. These different datasets can include information gathered indirectly from a public source or directly from the organization’s customers. 

Big data can be classified by its volume (the amount of data incoming), the rate at which it’s accumulated, or the kind of data it is (structured, unstructured, or semi-structured).

Due to big data’s complexity and size, it requires specialized tools that incorporate machine learning and artificial intelligence to analyze and extract insight. 

With the ability to analyze big data properly, businesses can significantly improve strategic decision-making within the organization.

But where does all this data come from? To understand that, we need to understand big data’s relationship to the Internet of Things (IoT).

Big Data & the Internet of Things (IoT)

The term “IoT” was initially coined by Kevin Ashton in 1999 while working at Procter & Gamble in supply chain optimization. 

Today, however, “IoT” refers to the enormous amount of interconnected computing devices, sensors, and other “things” that also connect to the internet. All told, they represent about 7 billion devices worldwide. Together, these devices are what feed the massive amounts of information we now refer to as big data.

Think about all those human interactions that produce data: social media posts, app experiences, web page views, emails, financial interactions, and vendor transactions, as well as streaming data from the Internet of Things. All of these affect your company.

The tools created for big data and analytics are useful to corral and govern the data streaming in from IoT devices. IoT-focused developers are creating platforms, software, and applications that enterprises and organizations can use to manage their IoT devices and the data generated. 

Now that we’ve covered what big data is and how IoT creates it, let’s talk about the specific effect that big data can have on the banking system and financial market risk.

Using Big Data in Financial Risk Management

As it relates to financial risks, big data helps to identify and forecast risks that can harm your business. With the proliferation of cybercrime, big data analysis can help to detect patterns that indicate a potential cybersecurity threat to your business.

Using data science technology that incorporates predictive algorithms to analyze big data in conjunction with risk assessment, financial institutions can obtain real-time insight into their risks and use that to drive their risk management strategy.

By leveraging the different sources of big data, organizations derive a wealth of 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 predictive 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. To demonstrate this point, let’s take a look at the variety of applications for big data in financial risk.

Specific Risk Management Applications of Big Data

Here are several risk management applications of big data.

Vendor Risk Management (VRM)

Third-party relationships can produce regulatory, reputational, and operational risk nightmares. VRM allows you to select vendors, assess the severity of risks, establish internal controls to mitigate the risk, such as firewalls or multifactor authorizations, and then monitor the vendors’ ongoing activity.

Fraud and Money Laundering Prevention

Predictive analytics supply an accurate and detailed method to prevent and minimize fraudulent or suspicious activity. That’s vital in an era when money laundering traffickers have become more sophisticated in their techniques. 

An arsenal of big data risk management and mitigation techniques is applied by governments and international lending institutions. This includes web, text, unit price, and unit weight analytics, as well as relationship profiles of trade partners. This data can help identify shell companies.

Identifying Churn

A significant risk to organizations is churn. The loss of customers deeply affects the bottom line. In the white paper Prescription for Cutting Costs, by Bain & Co., author Fred Reichheld, states: “Customers generate increasing profits each year they stay with a company. In financial services, for example, a 5 percent increase in customer retention produces more than a 25 percent increase in profit.” 

Customer loyalty can be identified using big data as a risk management tool. Based on the data, companies can expedite measures to decrease churn and prevent customer defections.  

Credit Management

Risk in credit management can be mitigated 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 creditworthiness) 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. Internally, costly defects in production can be detected early using sensor technology data analytics. 

How to Improve Risk Management With Big Data

To understand how big data can be used in managing financial 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 answer is a robust risk management solution. The fundamental elements of risk management are:

• Identification;
• Evaluation;
• Prioritization of risks; and
• Steps taken to manage risk.

Each of the elements in risk management correlates 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. When coupled with robust predictive analytics that assesses 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 financial industry, which has long used data systems for evaluation of opportunities and weighing risks.

In addition to financial markets, 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 regulatory risk.

ZenGRC Is Your Risk Management Solution

ZenGRC is a governance, risk management, and compliance tool that can help you to implement a risk management plan. 

It can help to automate and facilitate the documentation and workflows involved in risk assessment, mitigation, and documentation of cybersecurity incident response efforts. ZenGRC can also trace your compliance stance across multiple frameworks such as GDPR, PCI DSS, HIPAA, FedRAMP, and more. 

That view is provided in real-time, showing you where your gaps are and what’s needed to fill them, improving your overall security stance in the process.

Not only does this provide one of the most effective methods for risk management; it also makes organizations more efficient at the ongoing tasks of cybersecurity and risk management, and can improve customer satisfaction when clients are confident that their data is protected.

To see how ZenGRC can improve your risk management strategies, schedule a demo today.