The insurance industry has faced profitability challenges in recent years. Effective risk management is key for insurers to sustain positive economic returns in an increasingly complex global landscape. Thankfully, insurtech innovations offer new solutions to mitigate exposures. This comprehensive guide examines pragmatic strategies and cutting-edge technologies insurers can leverage today to minimize risks.
The Importance of Risk Management for Insurers
Insurance companies assume risk from policyholders in exchange for premium payments. However, poor assessment and management of those risks can quickly spiral out of control. For example, inaccurate underwriting models that underestimate client risk profiles can saddle insurers with major liabilities once claims arise. Furthermore, concentrations of similar exposures across an insurer‘s portfolio compounds dangers. Events like natural disasters may trigger a flood of correlated claims that outstrip loss reserves.
Ultimately, the majority of insurers fail to generate positive economic profit. Figure 1 shows that only 20% operate profitably once risk costs and capital charges are accounted for.
Figure 1: Only the top quintile insurance firms earn sustainable returns once risk is priced in. Source: McKinsey
Insurers clearly need advanced strategies to quantify, diversify, and provision for risk in today‘s intricately interconnected world.
Key Pillars of Insurance Risk Management
Managing threats in the insurance business rests on three main pillars:
- Precise underwriting
- Diversified portfolio construction
- Proactive loss reserving policies
Mastering these foundational capabilities provides a starting point. However, optimizing processes like claims and enhancing detection of fraudulent claims can further bolster profit margins. Collaborating with cutting-edge insurtechs also arms incumbents with innovative solutions.
The remainder of this guide explores specific tactics in each area:
Accurate Underwriting with Advanced Analytics
Insurance policies constitute a transfer of risk from policyholders to carriers. Flawed underwriting that underestimates customer risk profiles sets insurers up for later losses once claims arise.
Yet pricing policies too high also backfires, as customers simply switch to cheaper competitors. This underwriting precision vs market share tradeoff partially explains the industry‘s overall profitability challenges.
Fortunately, advanced analytics can provide insurers with greater underwriting acumen to walk this tightrope. The latest AI and machine learning models identify subtle patterns between client risk parameters and outcomes. Drawing insights from vast datasets, they accurately calibrate premiums to risk levels.
Figure 2: AI and ML models enable more accurate insurance underwriting and pricing. Source: CognitiveScale
Supplementary data from Internet of Things (IoT) devices further informs policies. Connected vehicles and property sensors, for instance, capture driving habits and home attributes to precisely gauge accident or fire risk.
Ultimately, augmented underwriting allows insurers to minimize risk while preserving competitive premiums to maximize market share.
Portfolio Diversification Using Mean-Variance Analysis
Insurers concentrate exposures when they disproportionately underwrite related risks. Hurricane risks for coastal homeowners or injury claims at a specific manufacturer, for example, constitute correlated exposures. Events triggering one policy claim likely trigger many, overwhelming reserves.
Portfolio theory from modern finance provides mathematical techniques to minimize overall portfolio risks. By underwriting a diverse pool of largely-uncorrelated risks, random claims in one segment offset those in others. This smoothes overall claims volatility to stabilize returns.
Figure 3: Constructing diversified insurance portfolios minimizes overall risks. Source: AM Best
Mean-variance optimization methods explicitly model relationships between risks and expected returns to create optimal portfolios. Machine learning simulation tools enable insurers to dynamically stress test prospective policies’ impacts on portfolio risk metrics like Value-at-Risk (VaR).
Adding reinsurance contracts to pass along some risk to third-party carriers provides another lever for optimizing portfolio exposures. Sophisticated insurers actively manage such transfers to fine-tune total balances across primary insurance and reinsurance programs.
Rigorous Claims Reserve Policies
Insurers remain liable for policyholder claims that have been incurred but not yet reported (IBNR) or reported but not yet settled. Statutory accounting rules govern baseline reserve levels to cover near-term claims payments.
However, unusual events like natural catastrophes may overwhelm these basic provisions. More generally, relying solely on lagging indicators of claims creates liquidity risk.
Insurers can mitigate this exposure through rigorous reserving frameworks that project future claims. Actuarial analytics uncover claims patterns across prior periods to forecast IBNR and other obligations. Stress testing with severe but plausible scenarios also informs appropriate buffer levels.
Firms should set their own management reserves well above regulatory minimums if exposures warrant. This proactive stance ensures adequate capital to fund obligations as they come due.
Improving Core Insurance Operations
Beyond foundational risk pillars, optimizing core processes also indirectly enhances risk management. Efficient underwriting, pricing, and claim administration minimize operating costs. Lower expenses directly pad margins to absorb fluctuations in claims.
Here we explore two prime targets for operational improvement: claims processing and fraud detection.
Claims Processing Optimization
Lengthy, opaque claims processes frustrate policyholders and create extra administrative costs. In fact, 90% of customers indicate claims handling experience impacts their loyalty.
Advanced technologies like AI and process automation streamline interactions to improve customer satisfaction while cutting internal costs. Chatbots ease initial loss reporting while machine learning systems utilize past patterns to accelerate adjudication.
| Stage of Claim Process | Enabling Technologies |
|---|---|
| Notice of Loss | Chatbots, Telematics Alerts |
| Information Gathering | Drones, Wearables Data, IoT Sensors |
| Claims Assessment | AI/ML Predictive Analytics |
| Claims Settlement | Blockchain Smart Contracts |
Figure 4: A range of technologies optimize specific aspects of the claims value chain
Figure 4 summarizes some of the hottest insurance technology use cases through the claims process life cycle. Together, these tools speed cycle times, reduce expenses, and bolster client loyalty.
Fraud Detection
Fraudulent claims also erode insurer profit margins. But early detection curbs unnecessary payouts. Here too, digital analytics generate alerts to identify suspicious patterns. Statistical outliers in factors like timing, location, or past history flag questionable claims for further scrutiny.
Similarly, new data streams aid verification. Timely telematics and photo evidence from IoT devices and drones deter fraudsters. The richer insight not only prevents fraud but also improves overall risk management data.
Access Innovations Through InsurTech Partnerships
Today‘s technological landscape evolves rapidly. Cutting-edge insurtechs target slivers of insurance verticals with advanced capabilities. Partnering with these innovators allows incumbents to benefit from their latest offerings.
Rather than build expertise across every emerging domain, carriers can tap solutions “as-a-service.” Hyper specialization also lets insurtechs maximize value in chosen areas. They optimize and iterate rapidly.
Cloud platforms, for instance, now offer enterprise-grade AI underwriting engines for per-policy pricing. Small teams specialize in perfecting algorithms while cloud infrastructure provides easy scalability. Other firms furnish turnkey fraud analysis or claims automation software.
Such partnerships provide low friction access to leading-edge tools that would prove costly and slow for incumbents to replicate internally. Importing modular components also avoids large-scale technology overhauls.
This guide demonstrates how modern capabilities help insurers quantify and mitigate risks to operate profitably. Follow the recipe of accurate underwriting, diversified portfolio construction, proactive reserving, operational excellence, and insurtech partnerships.
Implementing these foundational capabilities positions carriers to generate economic profit. Firms can then progressively advance maturity in each dimension. For instance, continuously refine underwriting models with more data and computing power or automation levels.
While insurance inherently constitutes risk-taking, astute application of analytics minimizes dangerous threats. Technology finally provides methods and tools for companies to elevate risk management. Companies putting these into practice will separate from the industry pack by sustainably growing profits.
