Savvy CFOs are Using Data Analytics to Mitigate Risks




Data analytics has shifted from being “just a fad” to a business necessity. Once considered the playground of marketing, data analytics has entered the mainstream stream business. Companies are no longer investing in data and analytics with the sole purpose of aiding marketers and drive revenues.

Rather, they are also exploring the opportunities of data analytics application in risk management.

The risk landscape is changing fast and this is driven mostly by increased volatility, heightened economic and political uncertainty, intense regulatory complexity, high-profile data breaches, rising employee fraud, shifting consumer habits and preferences, and increased competition.

As a result of these fundamental changes the strategic conversation around risk is changing too. Thus, business leaders should embrace risk as a tool used to create value and achieve higher performance. It is no longer something to only fear, minimize and avoid.

Applying data and analytics to an organization’s risk efforts plays an important role in strengthening internal controls. Implementing stronger controls is essential for avoiding and reducing substantial financial and reputational losses.

Companies that have previously placed little value or emphasis on strengthening internal controls have learned the hard way, and for many, the wake-up call came too late.

High-profile Data Breaches

The number of cyber attacks and ensuing data breaches is at alarming rate. Hackers are targeting companies across all industries and stealing treasure troves of data for criminal proceeds. Recently, a global cyber attack “WannaCry” halted service delivery and brought businesses and countries to their knees, locking people out of their data and demanding they pay a ransom or lose everything.

In the wake of these massive data attacks, companies are waking up to the realization that they need to strengthen their cyber resilience programs.

Investing in data analytics is one way of achieving this, and CFOs are uniquely positioned within the organization to drive the analytics efforts. Although data is the oil of the new digital economy, finance executives must look at data in two ways – as a source of risk and as a means to manage the risk.

Real-time Monitoring of Data

This is essential for reducing the potential of data breaches and better protect strategic data of the company. The CFO can help monitor the company’s data by performing real-time data-flow analysis and outlier analysis.

The former involves tracking the location of data at different times during a business process. Internet of Things (IoT) has brought about new ways of collecting and storing large quantities of data sets.

For instance, sensors are being installed in machines, clothing items, delivery vehicles, wearable devices, company products etc and these minute devices are capable of transmitting the data to an internal server for further analysis and insight generation.

Majority of the data hacking incidents happen at night when business have shut down for the day. It is this period that companies are more prone to cyber breaches.

By regularly conducting data-flow analysis, personnel responsible for data security will be able to detect any unusual data queries being made on the company’s database during a certain period, and compare that number with trends over the last month, quarter, year or longer.

If a trend is identified, this should act as a starting point for asking specific questions around data security and trigger responses.

Outlier analysis, mostly used by credit and debit card companies and other financial institutions, helps identify anomalies in the customer’s transaction history. Based on the historical transactions of the credit or debit card holder or customer over a period of time, the company is able to develop a profile for each and every customer.

Suppose one of your clients resides in Location A where he or she mostly transacts from, one day you notice that soon after recording a transaction in Location A another large sum transaction is recorded in Location B within a short period of time and the commuting distance between A and B is long making it impossible for your customer to be in one place at one time, this transaction must immediately be flagged up as an outlier and tell you that something is unusual.

Thus, as the purchasing history data of your customers increase, more focus should be placed on real-time outlier analysis. Thanks to technological innovation, today’s computers have massive computing power to store and perform this critical analysis on very large datasets.

Make Use of Both Structured and Unstructured Data

Structured data is easy to analyze because it is highly organized and predictable. Unstructured data is essentially the opposite, it takes more effort and time to compile.

However, much of the company’s data is unstructured, and this where CFOs can uncover perils and act almost immediately to avert hazards.

Thus, as social media networks continue to grow in use, finance executives need to find meaningful ways of combining data from multiple sources, regardless of location or format, for analysis.

It is through this combination and analysis of disparate datasets that finance is able to make informed analysis and provide improved decision support.

Many brands have suffered mishaps because of poor or misaligned social media strategies. For instance, a negative tweet was allowed to go viral before the company could hardly respond leading to damaged reputations.

Thus, having a coherent and well executed social media plan will help you detect any external threats to the company’s reputation. One negative tweet has the massive potential to make you lose your key customers and shut door.

In high performing companies, CFOs are taking advantage of new technologies and keeping an ear on the ground in order to hear what is being said about their companies on social media platforms.

This new software has the capabilities to gather and combine data from various social media platforms concerning the company’s products, services, competitors etc.

They have also deployed teams to provide round-the-clock monitoring of social media activities.

When this data is analyzed and insights gleaned, the company can reach out to the message source, tell its side of the story and resolve any differences. Better more, the company is also able to trigger a response ahead of any negative story.

Retail companies are making use of image-recognition software to detect product issues while they sit on market shelves and ensure these errors are corrected well in advance. Using their smartphones, sales reps can snap photos of the company’s products. The software then makes an instant visual analysis of the photos leading to corrective measures being taken.

Email Risk and Fraud Prevention

As employee fraud continue to skyrocket, email use, in its unstructured form, is getting special attention. If fraud perpetrators are not detected well in advance and their plans allowed to flourish, the organization stands to lose hundreds if not millions of dollars.

It is therefore imperative that companies invest more time and resources analyzing the email patterns of their employees. For example, real-time monitoring of patterns in the metadata of employees’ email communications can help you reveal risks before they take centre stage.

You need to look at clues such as – Who is the email being sent to? What is the subject and the nature of the content? Is the email high priority or low priority? Who is copied and blind copied? What time of the day is the email sent?

Investigating such information can help you monitor incoming and outgoing email traffic of specific groups or individuals, locate high risk areas that you need to look into and also establish if restricted company information is being released to the public either accidentally or on purpose.

The success and power of data analytics in achieving value-adding risk management depends heavily on the quality and preciseness of the questions asked, the organization’s ability to gather data that addresses these questions, the integrity of the data gathered and the ability of users to draw insights from the data in an objective manner.

Before investing in data analytics software, first identify the challenges your business is currently facing and ask the critical key performance questions that you want answers for.

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