Underwriting, the process of evaluating and pricing risk for insurance policies, is fundamental to the insurance industry. However, traditional underwriting practices struggle to keep pace with today‘s data-rich landscape. Insurance companies now have access to exponentially more customer data from sources like the Internet of Things (IoT) and social media. Yet antiquated systems leave them unable to capitalize on these insights to the fullest extent.
The solution? Automation through cutting-edge technologies.
In this comprehensive guide, we will explore how automation is transforming insurance underwriting to create a faster, more accurate, and more customer-friendly process.
The Growing Need for Speed and Efficiency
Insurance customers today expect speed and convenience across all interactions. In fact, 85% are unsatisfied with the traditional underwriting process according to Accenture.
Lengthy wait times stem primarily from manual inefficiencies. Underwriters spend over 50% of their time on repetitive data tasks like pulling credit reports or verifying driver records [1]. This sluggish velocity also limits business growth and revenue.
The costs of these delays add up. Let‘s examine the car insurance market as an example. For every day a new policy application goes unprocessed, the potential customer remains uninsured on the road. If they cause an accident during that gap, it produces a comprehensive claim loss for the insurer.
With $300 billion in premiums written last year in the US auto insurance market alone, those missed days represent massive lost opportunity costs. Automating underwriting offers a way to capture more of this market faster.
Automating underwriting drove over 15% revenue gains on average for health insurance firms. [Source: McKinsey]
Cutting Through the Data Haze with AI
Legacy underwriting systems leave insurers data rich but insight poor. The information exists yet lacks ways to systematically process it at scale.
This is where artificial intelligence and machine learning enter the picture. AI-powered solutions can ingest infinitely more data points to underwrite business accurately and instantly.
One large European insurer reduced life insurance application processing times from 14 days to just 4 seconds using an AI underwriter. By 2030, McKinsey predicts AI will shorten most underwriting to mere seconds through automating risk evaluations [2].
Let‘s walk through some common AI/ML applications modernizing underwriting:
Automated Data Collection
Today robots can log into databases or applications to pull structured data needed to quote, bind, or renew policies. This saves huge amounts of manual employee effort.
Home and auto insurers particularly benefit from expanded data from IoT enabled devices. AI systems can constantly analyze telemetry from smart home sensors or vehicle diagnostics. This delivers dynamic and personalized policy pricing based on real-time risk profiles.
Real-World Success: Progressive‘s usage-based "Snapshot" program analyzes over 1 billion miles of driving data daily from consenting customers. This enables ultra-custom premiums and offers based on actual driving performance.
Accelerated Document Processing
In regulated sectors like insurance, document analysis remains an unavoidable aspect underwriting. This includes doctors‘ notes, bank statements, police reports, etc.
Using computer vision and natural language processing (NLP), machine learning algorithms can rapidly extract unstructured data from these docs. This replaces slow and error-prone human review.
For handwritten or scanned documents, AI-powered optical character recognition (OCR) technology can digitize the text for analysis too.
Efficiency Stats: AI data extraction reduces time spent processing a single underwriting document from over 60 minutes manually to less than one minute automatically based on benchmarks.
Predictive Analytics and Risk Scoring
Here AI systems showcase their ultimate value – accurately evaluating all collected data to score risk and recommend pricing. The machine learning models detect patterns and correlations across thousands of past underwriting decisions and claims.
These predictive insights far surpass human underwriting intuition. And the algorithms constantly optimize predictions through new data, getting smarter over time.
Uplift Seen: Machine learning models can improve risk scoring accuracy between 15-40% over manual underwriting according to insurtech solutions provider ZestAI.
Rounding the Base with Robotic Process Automation
While AI delivers the brains, robotic process automation (RPA) provides the hands for modern digital underwriting.
RPA allows configuring "software robots" to automate repetitive back-office tasks screen by screen. The robots can log into multiple systems just like humans to perform data transfers or updates as directed.
Typical RPA use cases include:
- Application data entry – pulling info from scanned documents or forms to populate underwriting systems
- Validations and checks – running verification steps like checking credit or compliance databases
- Renewals processing – updating policy information for existing clients each term
Though limited in judgment capabilities, RPA excels at relieving staff of monotonous work. Combining RPA bots with AI guidance amplifies the automation power even further.
Efficiency Gains: By deploying over 500 software robots, Primerica automated 75% of its underwriting process, reducing rate quote times by over 95%.
Enabling Instant Data Sharing with APIs
Finally, application programming interfaces (APIs) tie the entire automated underwriting ecosystem together. APIs essentially act as data highways – shuttling information seamlessly between platforms.
Insurers rely on many disjointed systems and data repositories. APIs create bridges between these islands of information.
Some key API benefits include:
- IoT integration – streaming data from smart devices into underwriting platforms
- Real-time decisioning – accessing predictive models to instantly price risk upon application
- Customer self-service – allowing online quotes or policy purchases around the clock
Through APIs, insurers break free from legacy constraints to deliver next-gen experiences. Customers enjoy speed, personalization, and convenience exceeding expectations.
And insurers reduce operational costs while capturing more revenue – by closing policies within hours or minutes instead of weeks. Everyone wins.
Speed Improvements: Humana reduced underwriting time from 2 weeks to 24 hours by implementing an API-based platform integrating third-party data sources.
Hitting Hyperdrive: Early Adoption Success Stories
These transformations outpace hype. Early automation adopters demonstrate hard underwriting results:
- QBE Insurance redesigned underwriting operations with 85% straight through processing, increasing new business volumes 20%+
- Manulife accelerated underwriting with an AI-powered predictive model, improving risk assessments up to 40% over manual review
- Primerica embraced RPA and AI to double the policies its agents sell daily while maintaining 99% underwriting accuracy
The proof points mount – AI and automation allow insurers to scale intelligent underwriting exponentially.
Expanding the Possibilities: Emerging Innovations
Rapid advances around underwriting automation continue stretching previous limits. Several emerging capabilities lie just over the horizon.
Autonomous Underwriting
Today most automated underwriting still involves some level of human oversight or exception handling. AI confidence levels are not yet high enough for full uninsured autonomy.
However continual accuracy improvements along with policyholder consent laws maturing bring fully autonomous underwriting without human intervention closer to reality everyday.
Faster Predictions Through Deep Learning
Even the most advanced machine learning models today require periodic retraining to ingest new data and keep predictions optimized. But what if risk scoring happened instantly and continuously?
That is the promise of deep learning – next generation neural networks capable of perceiving new patterns and updating decisions dynamically. Deep learning could enable real-time risk quotes adjusting as customers interact with an insurer.
Quantum Computing Power
Finally over the longer-term, quantum computing holds potential to analyze exponentially more data even faster using quantum bits or "qubits". Though early stage, research by IBM and others applies quantum algorithms showing order-of-magnitude improvements for many predictive modeling use cases.
In a future state, quantum machine intelligence could completely reinvent underwriting operations and accuracy. Savvy insurers are keeping a pulse on developments today.
Architecting Automation: A Framework for Implementation
With so many enabling technologies in play optimizing underwriting, developing a holistic automation strategy becomes imperative. Applying lessons learned from early adopters, below outlines a structured approach covering key dimensions.
Process Analysis
First reimagine desired future underwriting workflows channel-by-channel, then work backwards. Identify manual touchpoints and decision gates for automation opportunities. Also factor in expanded data now available.
Output high-level process maps showcasing enhanced digitization, data usage, and customer experiences post-automation.
Solution Architecture
Next map enabling technology components needed against planned workflows – APIs, bots, AI models, etc. Model overall data flow and orchestration required to power automated underwriting.
Define interfaces for core administration systems, pricing engines, and any specialty platforms. Modern open and cloud-native insurance software centralizes connectivity.
Organizational Transformation
With machines assuming certain tasks, underwriter and administrator roles shift towards oversight versus hands-on. Still continuity stays vital during transitions. Utilize change impact analysis and skills assessments to reshape teams.
Retrain staff on technology interaction versus traditional manual work. Some insurers setup Automation COEs for enablement. Keep roles experience-based with rotational assignments to counter change fatigue.
Scaling Implementation
Given process and technology complexities, an iterative rollout methodology works best. Start with a single channel or product line piloting limited scope. Gather user feedback, monitor model accuracy, and refine.
Once perfected transition the pilot into broader business-as-usual expansion across portfolio segments. This achieves underwriting automation systematically without jeopardizing existing operations.
Overcoming Challenges on the Road Ahead
Like any transformation, obstacles remain in modernizing insurance underwriting:
Legacy Technology Constraints
Many old underwriting platforms lack APIs or flexibility needed to integrate automation technologies quickly. New core administration systems with open architecture alleviate these roadblocks.
Migrating historical data also poses challenges. Using data transformation tools, insurers can systematically map and convert legacy data sets into compatible formats.
Organizational Resistance
As underwriters or administrators see tasks shift to robots, anxiety understandably follows. However, most employees still maintain centralized oversight roles. And automation focuses their skill sets on higher judgement activities versus repetitive tasks.
Proactive change management and training helps ease transitions and highlight new career growth opportunities. Humans and machines prove more productive together than separate.
Strict Regulations
Insurance remains a highly regulated industry. As newer AI/ML technologies enter underwriting processes, ensuring models comply becomes imperative across development, validation, and monitoring.
Some insurers create specialized model risk management groups. These experts partner closely with data scientists and technologists to guarantee governance and transparency within automation.
Industry Impact: Changes Ripple Across Insurance
Beyond underwriting itself, waves from automation technologies extend enterprise-wide to alter insurer business models, workforces, and customer experiences.
Structural Cost Reductions
Process digitization and AI allow drastic cost structure optimization. Machines both operate more efficiently at lower marginal costs than human capital and reduce third-party services spend.
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Staffing realignments further contain expenses long-term as technology displaces certain manual roles. Insurers shift budgets away from overhead towards IT and technical resources.
Combined these factors permanently boost profitability. McKinsey estimates 30%+ cost reductions possible.
New Revenue and Premium Models
With fuller customer insights and lower variable costs, creative pricing strategies emerge around precision risk-based premiums or micro-insurance targeted buys.
Usage-based coverage spurs acquisition and cross-sell supported by expanded data ingestion and real-time decisioning.
Even peer-to-peer insurance networks become feasible by tapping automation to evaluate risks across affinity groups accurately.
Business mix evolves from volume plays to maximize lifetime value. Higher revenue per policy compensates for smaller market share as new entrants specialize.
Enhanced Customer Experiences
Of course customers represent the greatest beneficiaries of underwriting automation. Waiting days or weeks for policy approvals fast becomes archaic. Serverless interactions allow precision customization and self-service unheard of before.
Younger digital-native generations come to expect this pace and personalization as the new normal. Customer analytics shift from reactive claims management to preemptive nurturing and loyalty.
The nature of human support transforms as well. With AI handling high-volume inquiries, agents focus on complex advisory conversations building trusted advisor rapport.
Gearing Up for Next Generation Underwriting
The digital future fast approaches for insurance underwriting. Leaders recognize first-mover advantage awaits. Though the road has some bumps, the rewards far outweigh the risks.
With experts forecasting $400 billion in cost savings and profit liberation through insurance automation over the next decade, no player can afford to be left behind.
Now is the time to start building a strategic automation roadmap – evaluating use cases, piloting solutions, and infusing new technologies across underwriting. Early planning and testing today will pay dividends for years to come.
Are you ready to accelerate underwriting into the digital fast lane?