Copious amount of data swarms the Insurance industry today. In this blog Amrita Dhar, Senior solutions marketing manager , Saama Technologies, talks about how this industry can utilize Machine Learning for processing, streamlining and systemizing this data for better insights.
The old way of getting at insights is pretty much redundant now. Feeding in data, processing it and arriving at an insipid answer, is a thing of past. We now have a new player in town – Machine Learning which can prove to be a fundamental game-changer for the insurance industry for meeting compliance, improving cost structures and ensuring competitiveness.
Today, only a subset of all insurance-related data is utilized in predictive modeling that compromises the model accuracy due to absence of machine learning component at its core, which would utilize the information for purposes other than the original intent.
Let’s take the claims process as an example:
If industry reports are to be believed, claims account for nearly 70-80% of the operating cost and insurance claims leakage or money lost through faulty processes, including inefficient claim processing, human error or fraud—can comprise nearly 18% of insurance companies’ costs.
What if this cost could be abated by even a percent? Machine learning allows a revolutionary leap from a reactive claims leakage process to a one that is proactive.
Traditionally, companies use claims audit tools to monitor processes and to reduce leakage. However, audits have limitations as they only identify a small sample of the claims and recognize incidences that result in claims leakage. Checking every claim for leakage is not a feasible measure from an efficiency standpoint.
Here are a few key areas that insurers can use machine learning for:
- For predicting if a human intervention will accelerate the claims payment process, and based on historical claim payment information predict how long it will take for an insurance company to pay for a claim
- With machine learning, the time required for claims processing would come down from a number of months to just a matter of minutes.
- Realizing that machines over perform at routine tasks and that algorithms learn over time, insurers should re-focus on their ‘proof-of-concept’ efforts. The more data that the machine analyses and the more decisions it makes, makes it proficient at taking on more complex tasks and decisions. Reminded of the ‘Edge of Tomorrow’ movie? Yes, something like that.
- The overall claims management process can be made more efficient like in audit, leakage, predictions, severity, mitigation and subrogation on the property and casualty (P&C) side. All the simple claims can be automated using machine learning that would expedite the claims process and reduce human error
- Analytics can identify fraud by a combination of modelling, rules, text-mining and database searches. Analytics also has an important role to play in streamlining settlement.
Saama’s Edge:
Saama Fluid Analytics Engine integrates with external systems using prebuilt connectors to unstructured text, databases, flat files, APIs, Web Services, and ETL. The prebuilt machine learning algebraic and non-algebraic models come with advanced visualizations & data management. This approach eliminates the need for expensive analytics engines and allows the most valuable data science resources to focus on solving core business problems.
Conclusion:
Machine learning, if harnessed well, can significantly impact the insurer’s bottom line. It begins with identifying pain-points in the processes that pose greatest risk for claims leakage and then restructuring and systematizing procedures to incorporate and harness new capabilities.
Claims is just an example. The data with machine learning is enabling companies to customize and model their customers’ experiences to chart trends, predict and build more cost-effective products.
Click to learn more about how Saama uses Machine Learning within Insurance.