Improving Risk Evaluation Through Advanced Analytics
The data we generate and copy annually doubles in size every two years, and IDC says it will reach 44 trillion gigabytes by 2020. That creates significant new opportunity for business. Indeed, as The Economist has noted, data has become more valuable than oil.
In the case of data, there’s a lot of it to wrangle from many sources. Ensuring you have the right data, that it’s fresh and that you’re using it to yield accurate results is challenging. And it’s quite complex.
Traditionally organizations have used people and manual processes to collect, review and report on data. That’s no longer a viable strategy. Manual systems simply can’t keep up with the growing volume of transactions and data processing required to make timely, informed decisions. Overwhelmed with higher demands and expectations for quality assurance, personnel and processes across industries have reached capacity.
Legacy rules-based analytical models don’t scale to meet today’s requirements for evaluating risk associated with this flood of data. Fortunately, technology innovation is fueling significant improvements in analytics. That’s enabling organizations to address the data onslaught and use it to their advantage.
Early decision models rely on hard-coded rules or very simple linear simulations with a limited number of features or variables. These models use sub-sampling and analyze cases as groups, ignoring important data sources that can impact outcomes. Restricted to making decisions at the group level, they are incapable of examining individual cases.
Legacy models also are limited by the perspective of those creating the rules, making it hard to incorporate unanticipated parameters. As new rules are added for each new case discovered, it becomes more difficult to see gaps and overlapping rules within hundreds of rulesets. This process yields too many false positives and exceptions. And over time it becomes too complex to maintain.
Automated decision-making is not new. Computers sift through data, spotting patterns and anomalies a lot faster than humans can. However, the application of artificial intelligence (AI) and machine learning is ushering in the next generation of analytics modeling using intelligent automation.
At first glance, intelligent automation approaches might seem similar to existing solutions, but technology innovation brings considerable change to the outcomes. New capabilities make more granular models possible, shifting from group to individual decisions.
Increased transaction volumes and higher data availability provide the inputs required for more accurate results. Greater processing power from cluster computing, distributed data management and cloud storage enable this data to be collected and used. Finally, advanced algorithms in AI and machine learning examine data fast and update frequently. And they learn from patterns and correlations to improve results over time.
To take full advantage of new capabilities in AI and machine learning, the function of rules must shift. Instead of designing analytical models using rules to make decisions, businesses can develop rules of learning. This approach entails using algorithms to process all cases and examine individual behaviors. This allows organizations to move beyond sampling and segment-based treatments.
