For the insurance sector, still toeing the line between tradition and innovation, big data heralds a transformative shift in industry practice as much as it paves the way for new and long-lasting business opportunities. As we enter a new year, policyholders expect the standard of service that comes with strategic data analytics and insurance companies are being increasingly forced to innovate to keep up. While early adopters enjoy an advantage in customer retention and acquisition, others struggle to effectively apply the data they’ve collected for a variety of reasons.
Big data no longer refers to just the information collected but also the methods used to understand the data, and they’re not always useful. The growth of encoded information today means that older data processing software is no longer equipped to effectively manage them. The amount of data that insurance companies collect is aptly named for a reason - it’s big, and growing. The International Data Corporation suggested that by 2025, the global data sphere will produce a total 163 zettabytes per annum (1 zettabyte = 1 trillion gigabytes) - a tenfold increase in data generation, with unstructured data growing 15 times
Yet we see the current industry trend to move from process-driven to data-driven practices, and as data extraction grows in importance, it forces the hand of innovation in the industry to move away from more cumbersome, difficult, and costly methods. Today, it’s no longer a question of if companies should use data; rather, the more pressing concern lies in how companies can effectively apply its insight to business practices, thereby optimizing back-end efficiency, product offering and customer interactions.
So, how can a company apply big data, and what exactly can big data unlock for insurers? Some ways a company can effectively collect and use data to innovate their offering include:
- Rely on innovative, new gathering tools: insurers are experimenting with novel ways of collecting data, such as drones flying over disaster zones, to rapidly appraise factors like the damage levels and structural integrity of rooftops, etc. This eases the bottleneck of onsite adjusters (a limited number), and enables a quicker resolution to distressed policy holders.
- Use data to incorporate more accurate risk assessment and underwriting practices: something as simple as concentrating risk pools will more accurately reflect the risk involved, such as alcohol-avoiding religious or ethnic groups virtually eliminating the likelihood of DUI.
- Customize your consumer offering: entirely new data sets from GPS-based apps can enable brand-new business models for offering coverage, like pay-per-mile insurance. Hyper-personalized options like this are extremely attractive to consumers and business owners in the age of the gig economy, P2P carpooling, and digital nomads.
- Teach AI to learn from mistakes for improved fraud detection: machine learning algorithms iteratively learn from processing discrepancies and errors and use this "education" to avoid making the same mistake in future. Thus, frequently re-training models on growing data banks fine-tunes a company's ability to recognize incoming fraudulent claims. This could be by matching the invoicing behavior of a dodgy car repair shop with that of another using similar methods. These patterns are difficult to spot with the human eye, but optimized AI thrives on them.
- Adapt prices in real-time: incorrect pricing means a lot of missed revenue. By automating a wider segment of the business model, insurers can conduct data analyses on batched products, tweaking prices in sharp reflection of market trends and customer behavior.
No longer just a trend, big data and its various applications will transform the insurance sector in 2019 by unlocking valuable insight, revenue, and efficiency. By tapping into the wealth of data available to their practice and prioritizing data insight and application in business practice going forward, insurance companies will retain their competitive edge while exceeding customer expectations.
