Loss prevention is a critical issue for insurance companies seeking to reduce risks and claims. But the traditional approach of relying on human assessments has limitations. Enter "scientific loss prevention" – leveraging data, analytics and technology to systematically identify, measure and mitigate loss risks.
The Evolution of Loss Prevention
Insurance policies have protected individuals and businesses from the financial impact of unforeseen losses for centuries. However, early insurance practices focused more on coverage than proactive loss prevention.
Over time, insurers recognized the value in minimizing policyholder risks before disasters occurred. Initial loss prevention approaches depended heavily on physical inspections by in-house risk experts and recommendations based on inspector intuition rather than hard data analysis. But with the advent of big data and analytics, we now have the capability to put loss prevention programs on a much more empirical footing.
The need for an analytical approach
Traditional loss prevention methods simply could not keep pace with the scale and complexity of risks facing many policyholders today. Manual approaches also struggled with inconsistency – two inspectors visiting the same factory could easily arrive at very different conclusions about hazards. Such variability and uncertainty leave insurers vulnerable to surprises.
In contrast, statistical and machine learning techniques can ingest much wider datasets, recognize patterns human evaluators would miss, and yield more consistent, accurate insights using the scientific method. This is the founding idea behind migrating to a “scientific loss prevention” paradigm.
Principles of Scientific Loss Prevention
Scientific loss prevention follows six key principles:
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Determining risk elements: Specialized risk engineers gather granular data on potential loss factors through on-site/remote evaluations of insured assets and operations.
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Modeling loss scenarios: Insurers analyze probable maximum loss, maximum foreseeable loss and expected normal loss using advanced simulation models.
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Calculating risk scores: Standardized algorithms objectively quantify and compare risks across the insured portfolio, instead of subjective human estimates.
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Consulting for risk reduction: Insurers provide specific, data-driven loss prevention recommendations to clients.
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Monitoring risk mitigation: Continuously tracking key risk metrics provides insight into the effectiveness of loss prevention efforts.
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Adjusting premiums: Premiums are adjusted based on policyholders‘ demonstrated loss prevention success, providing financial incentives.
Transforming loss prevention into a collaborative data science
These principles essentially transform loss prevention from a speculative art to an evidence-based, collaborative science. Insurers obtain a singular multidimensional view of policyholder risks based on advanced analytics, instead of fragmented perspectives from various inspectors. Logic inherent in AI systems also introduces healthy skepticism into risk evaluations – challenging both human cognitive biases and blind spots physical inspection approaches may have today.
And shifting from sample-based analysis to continuous monitoring powered by IoT sensors brings both volume and velocity. This orders-of-magnitude improvement in risk visibility and agility arms insurers to have more meaningful dialogues with clients on real-world policies and protocols that mitigate the most pressing threats to their operations.
Why Scientific Loss Prevention Matters Today
- Reduces unpredictable losses, enhancing an insurer‘s stability.
- Lowers claim amounts and premiums for policyholders with strong safety records.
- Preserves brand reputation and customer trust by preventing avoidable incidents.
- Promotes resource efficiency and sustainability – preventing incidents is ecologically and economically superior to rebuilding/remediating damage.
Unlocking innovation for insurers
Scientific loss prevention also opens doors to innovation for insurers seeking competitive differentiation, including:
- Superior risk selection and pricing: Granular data insights enable more calibrated underwriting and bespoke policies aligned to policyholder risk profiles.
- New value-added services: Loss prevention analytics/recommendations and risk monitoring dashboards become platform for launching new digital loss prevention partnerships.
- Total policyholder risk visibility: Holistic monitoring of physical, digital, market risks provides unmatched vantage point into client operations.
Thought leaders also foresee scientific loss prevention playing a pivotal role in insurers adopting parametric coverage – where payout factors directly link to measurable risk events like wind speeds breaching set thresholds. Here, quality data really becomes the product behind the policy.
Scientific Loss Prevention in Action
Let‘s examine a few real-world implementations of data-driven scientific loss prevention and their business impact:
1. Using IoT sensors for risk monitoring
- Specialty insurer Hartford Steam Boiler equipped International Paper’s fluid piping with IoT sensors.
- Continuous remote monitoring enabled early detection of equipment overheating or vibration issues indicative of potential breakdowns/leaks.
- Major losses were prevented by addressing problematic anomalies early.
2. Leveraging telematics data for auto insurance
- Auto insurers like Progressive and Allstate now offer usage-based “telematics” policies.
- Premiums are calculated from policyholder driving data like mileage, speeding instances and braking habits captured via connected car platforms and smartphones.
- Safe drivers demonstrated in telemetry are rewarded with policy discounts of 10-15% on average.
3. Predictive analytics to determine facility ignition risks
- Factory mutual developed machine learning models predicting conditions that elevation facility ignition risks
- One chemical manufacturer client used these insights to adjust its pressure relief valve inspection protocol.
- This early intervention averted a highly likely dust explosion scenario that could have caused $25 million in estimated losses.
The business case for data-driven loss prevention
A survey of nearly 300 insurers globally revealed that effectively leveraging analytics and technology to improve risk selection, pricing and loss prevention is expected to drive:
- 9% boost in loss ratio performance
- 8% improvement in combined ratios
- 11% higher return on equity
Convinced of the compelling benefits, most respondents indicated plans to increase spends on advanced analytics by over 15% CAGR over the next five years.
Now that we’ve discussed the potential of scientific loss prevention, what steps can insurance companies take to execute on this vision?
5 Best Practices for Insurers
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Standardize data collection using digital forms, IoT sensors, drones and other techniques for sourcing risk data consistently.
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Invest in analytical talent like data scientists, quantitative modelers who can accurately quantify risks using machine learning.
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Enhance risk models by incorporating new structured/unstructured data feeds, spatial analytics and simulations for greater sophistication.
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Recommend via AI assistants using chatbots that provide personalized 24/7 loss prevention guidance tailored to policyholders’ unique contexts.
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Incentivize risk reduction by making premiums dynamic based on policyholders‘ loss prevention metrics rather than fixed long-term rates.
Challenges and the Road Ahead
Despite compelling evidence of advantages, insurers still face barriers in enabling scientific loss prevention at scale:
- Outdated legacy IT systems make data integration and analytics implementation tedious.
- Significant upfront investments needed in sensors, data infrastructure, analytical tools.
- Organizational inertia to move from intuition-based to data-driven decision making culture.
However, the long-term downside of inaction is significant. Leaders warn that carriers who ignore digitization face shrinking market share to new parametric and data-native competitors.
Thankfully, the path forward is becoming easier – insurtech ecosystem innovations, cloud platforms and analytics-as-a-service solutions are democratizing access to cutting-edge capabilities.
As 5G, ambient IoT and edge computing mature in the next decade, virtually every risk vector will become quantifiable in near real-time. Companies not prepared to ingest and intelligently leverage the looming data deluge risk making decisions in the dark. Now is thus the time to take steps, big or small, towards modernizing loss prevention.
The future of insurance underwriting is data-driven
Industry analysts predict that within this decade, underwriters will rely more on algorithms versus intuition. Scientific loss prevention provides the foundational data fabric that feed these next-generation automated decision systems of the future.
Insurers who embrace this transformation stand to gain happier customers, expand market share and boost profitability through lower loss costs. Policyholders receive pricing commensurate to risk, incentives encouraging safety investments and robust disaster preparation – creating a mutually beneficial ecosystem.
Rather than dreading incidents, insurers have an unprecedented chance to help consumers and businesses neutralize danger before it strikes. The reach of insurance protection then extends from financial recovery to proactive threat elimination.
Conclusion
Loss prevention is rapidly evolving from a reactive practice to data-driven science. By fully embracing the modern arsenal of sensors, analytics and AI, insurers can usher in an era of safety and sustainability benefitting all stakeholders. The winners will be carriers who overcome inertia to overhaul legacy human-driven approaches with automated intelligence. Scientific loss prevention promises to fundamentally redefine the role of insurers – from financial indemnifiers after crises to trusted partners that help avert disasters before they ever unfold.
