The digital age has brought a huge increase in the volume, variety and velocity of
data. This includes unstructured data such as photos, images and videos. These new
data sources require new techniques to extract value from the data. AI uses machine
learning algorithms to analyze historical data to build a statistical model without
being explicitly programmed to do so.
The significant advantage of machine learning is that the algorithms can find hidden or
unknown trends in the data that can improve decision making or optimize efficiency. In
tandem AI has significantly advanced to incorporate natural language processing, such as
ChatGPT so risk managers can now ask conversational questions too.
Traditionally geospatial, AI and predictive analytics had been the territory of data scientists
and large carriers, TPAs or brokers. Today it is becoming essential that organizations embed
these analytical techniques into traditional risk management systems enabling risk managers
to make real-time business decisions based on hard data.
7. Process Automation
In the wake of COVID-19, many companies realized that many of their manually driven and
labor-intensive processes were not up to the challenge of a major risk disruption. The ability to
pro-actively pull in data, assess, and monitor the information, and perform real-time decision
making was just not there.
Risk managers are realizing that user-specific workflow automation, coupled with
comprehensive, high-quality data, can help streamline the tedious process of risk
identification and assessment. And the benefit of automation is not limited to just large risk
disruptions. For example, automation in risk management can help CFOs improve cash flow, as
well as boost efficiency and profitable growth.
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