In our current digital landscape, big data has played a major role in impacting business and risk management. Using nearly unlimited access to information regarding potential customers and user behavior, businesses are relying heavily on advanced analytics to improve their risk management practices now than ever before.
Why Big Data Is Important
Big data is not an emerging new technology, but it has taken quite a lot of time for businesses to recognize the value of big data. Big data doesn’t simply track consumer behavior online, it provides a cataloged history of the consumer’s behavior that big data services can analyze and extract meaningful insights from it. If the consumer uses smart devices, makes a transaction with credit cards or checks or visits places that use smart devices, they create a data trail that can be analyzed by big data consulting to discover possible trends. These trends can enable businesses to comprehend the factors that influence customers to make certain purchases over others.
Anytime you go online to browse articles, make a purchase or log on to social media, big data is present. It is there when you call someone using a smartphone, it is there when you open an app. Companies are constantly gathering information using tracking software and cookies. This collected data is then used for extrapolation or sold to businesses that require it.
4 Ways Big Data Is Evolving Risk Management
1) Identifying Emerging Trends and Risk Factors
The most distinct advantage of big data is its ability to identify emerging and existing trends among consumers. Statistical analysis allows new businesses to develop detailed business plans. Meanwhile, established businesses can notice changes in user behavior early, which allows them to mitigate the risk of moving the business strategy in a new direction. These analytics are also capable of detecting factors that play in part in customer defection, ultimately helping businesses to decrease and prevent high churn rates.
2) Evaluating Potential Business Locations
When setting up a new brick-and-mortar business, choosing a physical location is a pretty huge decision. In the past, businesses would rely on the process of trial and error, but now, using big data, businesses can utilize analytics to identify key demographics and best locales near those potential customers. When the target market is identified, selecting a business location no longer remains a guessing game but rather a highly informed decision.
3) Identifying Potential Fraud
Although it is true that the digital era has led to an increase in risk that did not exist previously, it has also led to the creation of more solutions to handle those risks. Businesses that are heavily involved in financial and or personal information can use big data to detect potential fraud by analyzing risk factors and exactly locate usual behavior and discrepancies through a highly streamlined and filtered process. This means that businesses no longer have to waste hours of manpower and bear the risk of human error in keeping customers’ information safe.
4) Assessing Financial Risk
Financial institutions require risk management, arguably more than any other business. Big data provides the statistics such organizations require to assess and mitigate financial risks such as market risk, asset liability and credit card fraud. Using predictive modeling and creating risk-free services based on analytics, financial organizations can improve customer satisfaction and sustain business continuity.
For the most part, big data has greatly evolved and advanced risk management for businesses and as more and more businesses integrate and shift to digital transformation, we can expect even more advancements in the risk management field over the next couple of years. Due to its ability to provide businesses with the opportunity of making highly informed business decisions using advanced statistical analysis, big data is paving the way for the creation of more opportunities for growth for businesses as well as consumers than what was ever possible before.