The amount of online data has grown exponentially as the number of mobile users has skyrocketed, and e-commerce is the norm. A report by EMC and IDC predicts that by 2020 the data we create and copy annually will reach 44 zettabytes, or 44 trillion gigabytes.
But data doesn’t mean much if it can’t be analysed. As more complex and intuitive algorithms are developed, the same EMC/IDC report shows that in 2013, only 22% of data was tagged and analysed, compared to an estimate of 37% in 2020.
The convergence of data proliferation and intelligent data analysis is revolutionising and disrupting traditional business models, including the insurance sector.
A large quantity of available data comes in an unstructured format, and many companies are able to capture and analyse only a fraction of the data and extract valuable insights.
Machine learning algorithms and Artificial Intelligence (AI) are pushing the boundaries of analytics even further with their applications spreading across all aspects of an organisation and its offerings. For example, deep learning enabled chatbots have replaced traditional customer service representatives.
Insurance is a data-intensive industry generating massive amounts of information and despite advances in digitising the practices, many manual processes persist.
In today’s competitive landscape, insurers need real-time information on the performance levels of distribution channels, cross-sell opportunities, which lines are underperforming and also to have the ability to spot patterns and trends.
The increasing use of predictive analytics amongst commercial insurers has improved efficiency and reduced the underwriting cycle, but a perfect storm of ongoing soft market conditions, low-interest rates, increased operating costs and emerging risks (to name a few), is making it harder than ever before for insurers to acquire new customers and retain profitable ones.
Expectations: Where does analytics fit in?
Implementing the latest analytics technology and ticking off the ‘digitising processes’ box won’t necessarily pay off. Aligning data and analytics with the strategic vision of the organisation is the first step. Just adding a layer of technology over existing processes and systems will prove to be a waste of effort and resources.
The implementation of analytic tools presents its own set of challenges related to legacy core IT systems, quality and granularity of data. To implement analytics and automation, companies need to bring in different competencies within.
Optimised Claims Management
Using analytics and automation can help reduce operating expenses and increase the efficiency of the claims processing cycle.
Predictive analytics and AI enable insurers to identify low-value claims with high accuracy. Integrating this with Robotic Process Automation technology can help write custom rules in the workflow and settle low-value claims automatically.
Clustering claims by their size can reduce claims leakage. The algorithms can then check for variance in that group up to a certain limit, increasing which of those claims can be flagged and handled manually.
Improve Fraud Detection
Fraudulent claims contribute to increasing a firm’s loss ratio. Significant resources and efforts are spent on investigating and recovering fraudulent claims. A predictive model to identify fake claims with greater accuracy can help companies reduce losses and proactively identify potentially fraudulent claims. This can also reduce the premiums to the policyholder as the cover of cost or fraud reduces.
Another way brokers can help insurers use analytics is in tracking injuries on the job. Companies can learn if there’s been a specific type of claim that could potentially be prevented in the future, like slips and falls in a particular unit or area. If the company has had some claims in the past, and then uses these insights to create a safer work environment, the broker can take those stats and reports to the insurer to negotiate lower rates.
In addition to using historical data and looking at loss history, carriers now use real-time information to collect data to underwrite more precisely. For example, in a large bus fleet or trucking company, certain data analytic tools can be used to show the driving patterns of drivers during their routes. It can show if they are driving the proper speed limits, accelerating and braking too quickly, and using cameras to get an internal view of the route. This helps create a safer work environment for drivers, and insurance carriers gain a better understanding of the safeguards companies have in place or are willing to put in place. This may then help the carriers to reduce and/or increase premiums.
The explosion in available customer data (both personal and commercial), the growth in analytical techniques and declining cost of computing power and data storage are prompting companies to invest in data analytics as a means to innovation.
At the core, the basis of adoption of new technologies is always about optimising existing processes and reducing costs. The advancement of analytics will have a major impact on companies - it might reinvent roles, make others obsolete, but will also introduce new ones. Analytical capabilities will enable companies to be well prepared and calculate risks more accurately.
Introducing new technology constantly challenges the existing infrastructure and processes. The focus should remain on completing the cycle from ideation to implementation of analytics to reap optimum benefits.
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