Analytics And Machine Learning — And How Will This Affect The Insurance Industry?

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Leaders in financial services are harnessing advanced analytics to tap into huge new troves of data and unlock billions of dollars in value. Banks are using news media data to identify credit risks, for example, rather than relying only on financial and demographic data. Hedge funds are driving trading strategies based on correlations between emotions expressed in Twitter feeds and stock market movements.

The insurance industry has always relied on data and analytics across underwriting, claims, fraud detection and so on, and two advances are unleashing significantly more value in the industry: the explosion of data sources, include “structured,” machine-readable data and “unstructured” 2 data such as video, text and social media; and leaps in computing technology and advanced analytics techniques and tools such as machine learning. Meanwhile, the shift from batch to real-time processing and visualization of data feeds is fundamentally changing core operations in claims, billing, and CRM.

Many carriers are riding this new wave of innovation and applying advanced analytics techniques across products and business functions. For example, auto insurers started incorporating behavior-based credit scores into their analyses when they realized that people who pay their bills on time tend to be safer drivers. Some insurers are using data on agent characteristics and behaviors to predict how likely each is to sell multiple products and what specific products would they be most successful at selling, leading to a 20–25% increase in sales. Others have used data and analytics to proactively manage lapse rate by using geospatial, household, and zip code level demographics data to reduce customer churn by 30%. An auto-insurer applied state-of-the-art nonlinear, machine learning modeling to identify 60% of the book was mispriced, with 35% undercharged. Many insurers are making more accurate predictions of new agent and advisor success, prioritizing submissions based on likelihood to bind, improving market segmentation and making many more advances with more data and deeper insights.

The impetus to invest in analytics has never been greater, and carriers are still trying to understand how analytics trends will impact the industry. Chief executives are facing three potential scenarios:

Given the intensity of competition and the use cases1 now emerging, quick change and disruption in the industry seem more likely than a “gradual embrace.” Players who make wise investments now to get ahead of the analytics revolution, therefore, stand to gain significant competitive advantages. But no matter the pace of change, each carrier will move through four phases on its journey to a future where big data analytics drive decision-making:

Phase 1 — Building insights. Initially, companies develop models showing early evidence that analytics can offer new insights that could yield clear value. Some may develop models at a distance from the business, however, and struggle with frontline adoption.

Phase 2 — Capturing value. As the analytics function matures, model-builders work closely with frontline staff, who become involved in the “nuts and bolts” of building the model. The focus shifts from developing models to adopting them, and they come to life. Even if the models’ insights are not fully applied, the company sees them as valuable tools that enhance decision-making.

Phase 3 — Getting to scale. The CEO is committed to using analytics to improve business practices. The company invests in dedicated resources that support a portfolio of initiatives across several businesses. Mature processes are in place to govern and prioritize this support. Analytics becomes better integrated into frontline management and IT processes.

Phase 4 — Becoming an analytics-driven organization. As analytics become the “backbone” for how business is conducted, to shifts from being an enabler to the core way of doing business, and the impact of analytics is measured as part of core business results. Analytics drives underwriting, product development, and distribution, and barriers between functional siloes dissolve. The ecosystem becomes more complex, with greater involvement of third parties alongside carriers. The talent strategy focuses on identifying, recruiting, building and retaining analytic skills.

In their efforts to capture the full potential of analytics, most P&C carriers are somewhere between phases two and three. Many have achieved pockets of success and validated the potential of analytics. We see three ways that CEOs can accelerate progress beyond this middle ground:

1) Provide a mandate. Analytics should be a relentless priority of the CEO. It is not just one of several themes but the target end-state around which the organization aligns a vision for doing business differently.

2) Make a multi-year commitment. Investments over several years are required — including some that lead to false starts and require trial and error. Advancing on the maturity curve does not happen in a year, and impact may not be obvious within the first few quarters.

3) Demand fast progress. While full impact takes several years, quick wins and success stories will help prove the concept and maintain momentum. CEOs should personally look for several use cases each year that demonstrate new and incremental impact from analytics — and celebrate these successes as examples for others.