Top 10 Use Cases of Hyperautomation in Insurance (2023)

Hi there, welcome to this deep dive on how leading insurance companies are leveraging hyperautomation technologies to drive digital transformation. If you‘re an insurer looking to stay competitive in today‘s landscape, buckle up – the insights and use cases we‘ll be covering are likely to excite you.

Specifically, we‘re going to examine 10 key areas where hyperautomation delivers tremendous value:

  1. Underwriting
  2. Policy Management
  3. Claims Management
  4. Customer Service
  5. Regulatory Compliance
  6. Data Management
  7. Predictive Analytics
  8. Customer Analytics
  9. Process Mining
  10. Task Mining

For each area, I‘ll explain the challenges insurers face, the relevant technologies involved, real-world examples, and quantify the benefits realized – from reduced costs to improved efficiency and customer experiences. My goal is to provide practical guidance and inspiration as you explore automation on your transformation journey. Let‘s dive in!

Revolutionizing Underwriting with Hyperautomation

Accurate risk assessment through underwriting protects profitability, but the largely manual process often creates bottlenecks. Policyholder applications flood in, but your team can only handle so much. Documents must be reviewed, data entered, calculations run – it‘s slow and error-prone.

Hyperautomation brings speed and precision:

Submission Processing

Intelligent automation streamlines data collection and extraction:

  • Chatbots interact with applicants, guiding them through questions while recording responses automatically. No more manual note-taking.
  • OCR technology extracts written information from scanned documents or handwritten notes with up to 99% accuracy.
  • NLP analyzes emails, texts and even call transcripts to structuring unstructured conversations into actionable data.
  • RPA bots take this extracted info and rapidly populate your systems – quick, consistent, and without human error.

For example, Canada‘s Wawanesa Insurance automated data entry from forms using OCR and cut processing time from 11 minutes to just 30 seconds per document.

Risk Assessment and Pricing

With automation, underwriters gain sophisticated analytical power:

  • Predictive modeling combines thousands of internal data points with alternative sources to uncover complex correlations between seemingly disparate risk factors.
  • These AI underwriting models achieve 95% accuracy in total loss predictions according to Deloitte, outperforming humans.
  • By integrating predictive scores into automated pricing engines, insurers gain the ability to price policies tailored to the individual – fairer for the customer and more profitable for the business.

Leading insurers utilizing AI underwriting include Progressive, which offers real-time quotes based on driving data from IoT devices, and Lemonade, which relies solely on AI bots to pay claims instantly.

The impact? McKinsey estimates automation can:

  • Reduce loss ratios 15-20%
  • Cut costs by 60-70%
  • Shorten cycle times by 90%

In summary, hyperautomation delivers the next frontier of underwriting. Superior analytics minimize risk, while intelligent automation provides the speed today‘s digital policyholders expect. Are you ready to transform underwriting?

Streamlining Policy Management with Automation

Managing policies from issue to renewal to potential cancellation creates loads of repetitive tasks for your team. Let‘s explore how hyperautomation can help:

  • When issuing new policies, RPA bots can instantly generate policy documents from templates and pre-fill customer data without human involvement.
  • For policy changes, NLP reads customer communications and automatically modifies information in your systems to reflect requested adjustments in real-time.
  • When policies come up for renewal, the entire process can be triggered and completed automatically based on policy terms and customer preferences already on file.
  • To cancel a policy, automated workflows ensure all required steps are taken – contacting the customer, adjusting billing, documenting reasons, and more.

According to SMA, intelligent automation can eliminate 30-50% of repetitive human tasks involved in routine policy administration. This allows your team to focus on higher-value functions that engage their expertise.

For example, USAA automated 50% of its policy servicing through technologies like AI, RPA and NLP, enabling staff to handle 20% more customer inquiries. You could gain similar efficiencies.

In summary, hyperautomation delivers a lights-out approach to streamlining your policy lifecycle. Are you ready to learn more?

Accelerating Claims Processing with Automation

Claims are the moment of truth – your ability to pay quickly and accurately directly impacts customer loyalty and business costs. Yet for many insurers, the claims process remains stubbornly manual. Let‘s explore how leading carriers are using hyperautomation to digitally transform the entire claims journey:

First Notice of Loss (FNOL)

  • AI chatbots provide 24/7 self-service options for policyholders to instantly report claims via conversational interfaces.
  • Integrated computer vision analyzes photos of property damage, vehicles, etc. to estimate repair costs in seconds with 98% accuracy based on similar past claims.
  • IoT sensors in connected homes, cars, or wearables capture loss data in real-time, speeding validation and preventing fraudulent claims.

For example, Lemonade allows customers to file claims via chatbot and typically pays in 3 seconds using AI.

Claims Assessment and Resolution

  • RPA bots process documents and forms 10x faster than human adjusters, classifying claims and determining payouts per established rules.
  • For complex claims, computer vision and NLP still deliver efficiency gains by extracting required details from paperwork, reducing time spent on manual review.

According to McKinsey, hyperautomation solutions are already enabling leading insurers to cut claims processing/payout costs up to 50%.

Fraud Detection

  • AI algorithms detect fraudulent patterns in claims data – e.g. similarities to known fake claims or unusual geolocations.
  • Blockchain networks can be used to share and validate claims details between insurers, mitigating double-dipping fraud.

With AI, Allstate has reduced soft fraud losses by $15 million, showcasing the power of automation.

In summary, hyperautomation represents the cutting edge of claims management – accelerating cycles, reducing expenses, and enhancing customer satisfaction. Don‘t get left behind.

Serving Customers with Hyper-Personalization

77% of customers expect insurance providers to use their personal data to deliver hyper-relevant recommendations and experiences. Are you ready to meet these new expectations?

Personalized Products and Pricing

With intelligent analytics, you can offer dynamic, personalized policies tailored to each customer:

  • IoT connected devices provide real-time telemetry on their driving, health, home usage patterns and more.
  • AI algorithms then analyze this data to derive an individualized risk score and automatically adjust coverage limits or premiums accordingly over time.

Imagine being able to continually refine risk assessments and pricing as behaviours evolve. Hyperautomation makes it possible.

Streamlined Onboarding

  • Chatbots serve as intelligent guides, answering applicant questions and ensuring they complete all required steps.
  • Computer vision validates ID documents, while integrated facial recognition provides biometric authentication to further accelerate KYC.

24/7 Virtual Assistance

  • Your customers can query their policy details or ask common questions at any time using voice or text via AI-powered chatbots.
  • With natural language capabilities, these virtual assistants deliver instant, personalized answers with human-like conversational ability.

According to Gartner, 75% of customer inquiries will be handled completely by AI by 2023, up 400% from 2018 levels. Join the leaders providing 24/7 support via chatbots.

In summary, hyperautomation enables you to know and serve customers better than ever before. Are you ready?

Maintaining Regulatory Compliance with Automation

Frequent regulatory changes create constant compliance challenges and risks for insurers. Let‘s examine intelligent solutions to stay on top of this problem:

  • RPA bots continuously scan regulatory sites for new guidance, automatically logging changes for your review. No more relying on humans to stay on top of updates.
  • When new rules are issued, bots can be configured to automatically update your workflows and processes to integrate compliant steps.
  • Process mining compares your actual processes to regulatory policies, detecting risks like missing procedures, incomplete documentation or incorrect sequencing.

According to Capgemini, compliance automation can reduce associated overhead costs by 30-50% while avoiding fines and reputation damage.

For example, Aviva automated over 1,000 controls to enable real-time compliance monitoring, saving thousands of human hours per year. See what‘s possible?

In summary, intelligent solutions enable you to cost-efficiently adapt to evolving regulations and avoid non-compliance risks. Let‘s discuss options that fit your needs.

Centralizing Data Management via Automation

Harnessing data‘s value requires centralizing disparate information into a "single source of truth." Attempting this manually is hugely expensive and error-prone.

Enter hyperautomation:

  • RPA bots can integrate siloed legacy systems by extracting data from each and consolidating it into a cloud data lake in a unified format.
  • AI algorithms profile this aggregated data, seamlessly merging records related to the same entities and resolving inconsistencies using probabilistic matching.
  • Natural language processing (NLP) structures free-form text data from documents into standardized, analyzable fields for including in analytics.

According to Mckinsey, automated data consolidation improves accuracy by up to 90% compared to manual efforts while cutting costs by 40-60%.

For example, Progressive‘s data management automation now enables real-time pricing derived from integrated telematics data. What results could unified data deliver for your business?

The takeaway – hyperautomation solutions enable insurers to finally aggregate data at scale to unleash its value. Let‘s get your data working for you.

Unlocking Predictive Insights via Hyperautomation

To price risk and guide strategy, actuaries need advanced analytics – but compiling and correlating massive amounts of quality data requires automation.

Let‘s explore technologies providing predictive superpowers:

  • IoT sensors provide real-time, high-fidelity telemetry data to complement historical data sets.
  • Machine learning algorithms uncover complex relationships between thousands of variables like demographics, behavior, geography, weather, and more.
  • Access to scalable cloud infrastructure allows simulating millions of risk scenarios to derive probabilistic forecasts and intelligently plan for uncertainties.

According to Willis Towers Watson, data-driven price optimization models can improve margin by up to 14% while maintaining market competitiveness.

For example, Allstate deployed machine learning techniques to improve loss forecasting accuracy by 15%, enabling pricing aligned with true risk levels.

The potential? Hyperautomation delivers the data and analytical capabilities insurers need to continuously refine risk models and planning. Let‘s discuss your analytics goals.

Know Your Customers Inside and Out

Hyperautomation solutions help insurers compile integrated customer profiles, gain actionable insights about behaviors and preferences, and then act on these insights to provide personalized engagement.

  • Customer data platforms automatically aggregate telemetry and transactional data from all touchpoints into unified profiles.
  • AI-powered segmentation analyzes attributes and behavioral patterns to categorize customers into distinct groups for targeted strategies.
  • Propensity models identify customers likely to renew policies, switch providers, or file claims based on predictive analytics.

Armed with these insights, you can craft personalized experiences and optimize engagements to improve loyalty and profitability.

According to BCG, data-driven customer analytics and engagement can increase policyholder retention by up to 30%. Don‘t let competitors know your customers better than you do.

Let‘s strategize how hyperautomation can help you better understand and serve your customers using data.

Illuminating Process Inefficiencies via Mining

Many insurers operate with processes that evolved over decades into convoluted flows riddled with friction points. Attempting to optimize these manually can be like finding a needle in a haystack.

This is where process mining comes in:

  • Process mining algorithms analyze system logs to map out actual end-to-end processes and highlight deviations from ideal flows.
  • Anomaly detection identifies bottlenecks, repetitive rework loops and variants causing delays or quality issues.
  • With a quantified view of problem areas, simulations help model the impact of process changes before deploying updates.

According to research from Everest Group, process mining typically uncovers 10-15% potential efficiency gains previously hidden within legacy workflows.

For example, Zurich Insurance used process mining to find and fix problems that improved customer onboarding speed by 50%. What potential could be uncovered in your workflows?

The data and visualization makes inefficiencies impossible to ignore. Let‘s discuss mining your processes.

Discovering Automation Opportunities via Task Mining

Many insurers understand that greater automation is necessary to remain competitive, but struggle identifying where to start amidst thousands of processes. This is where task mining comes in.

  • Task mining uses AI algorithms to observe how your employees work – their screens, apps, clicks and more.
  • It categorizes these behaviors into distinct tasks and processes to reveal experience gaps.
  • By quantifying time on task, task mining pinpoints high-volume repetitive tasks that automation can take on.
  • Opportunity analysis then ranks these candidate tasks by potential ROI to guide your roadmap.

According to research from Hyperthink, task mining typically identifies 2X as many worthwhile automation opportunities compared to manual reviews of workflows.

Where could task mining uncover automation potentials within your organization? Let‘s connect to find out.

The data shows clearly – hyperautomation is no longer just an option, but a mandate for insurance companies to remain competitive. The efficiencies, customer-centricity and analytical capabilities unlocked by combining RPA, AI, analytics and more add up to transformative benefits.

I hope reviewing these top 10 use cases provided some fresh inspiration regarding the art of the possible. My advice is to pick 1-2 areas that align with your strategic goals and run controlled pilots to build initial expertise. With the right partnerships, the possibilities are truly endless.

I‘m excited to further discuss automation opportunities tailored to your business needs. Please reach out! I‘m here to help guide your journey.

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