Why Is Risk Analytics Important?

The rise of big data, computing power, and advanced analytics enables companies to gain valuable insights from data. Artificial intelligence, machine learning, the Internet of Things, and drones are just a few innovative tools now available to help companies gain a more complete view of their businesses and make better decisions. For risk managers, using big data and risk analytics provides an unprecedented ability to identify, measure, and mitigate risk.  

Effective risk analytics requires a lot of data

To get an accurate picture of risk, your risk analytics solution must aggregate and analyze both internal and external data. Relying on internal information alone ignores all of the factors that impact a business beyond its four walls. Take, for example, farming. The value of a crop is determined by internal factors – seeds, water, fertilizers, pesticides, transportation costs, etc.; however, external factors are also important – weather, competitive pricing, geopolitics, market shifts. Farmers need to analyze all this data to completely understand the risks associated with their crops.  

Machine learning is essential

Machine learning algorithms are the only way to gain any actionable insight from the massive amount of data generated each day. The volume, velocity, and variety of data produced each day is beyond the capacity of manual analyses, spreadsheets, and programmed algorithms. Machine learning algorithms generate reports in seconds, enabling risk management teams to evaluate risk in real time and avoid costly delays.

Benefits from risk analytics

Tracking and analyzing risk factors in real time provides huge benefits because you know immediately when anomalies occur and can quickly react to make changes to alleviate risk.

Using advanced analytics, you can:

1. Create alerts to monitor for anomalies and outliers in real time and know instantly when problems arise. The faster you know where problems are, the sooner you can fix them.    
2. Utilize real-time portfolio monitoring and evaluate performance across key parameters. Analyzing performance in real time enables you to quickly adjust the portfolio to improve performance when necessary.
3. Use machine learning algorithms to identify high risk customers and reduce charge-off losses by screening for risky deals.
4. Simulate portfolios and evaluate the potential impacts of possible trades, disruptions, and events and create the portfolio that meets goals for profits and risk.
5. Track credit breaches in near real time and analyze risk limit breaches by trader, profit center, and trading desk level.

Big data is changing the way businesses are run, enabling businesses to analyze massive amounts of information about operations in near real time. Risk analytics enables you to take that data to understand and reduce risk.