Intelligent Automation in Insurance: A Deep Dive into Use Cases and Implementation

Intelligent automation is poised to transform the insurance industry by overcoming inefficient, manual processes. This in-depth guide provides everything insurance leaders need to know, from use cases to vendor selection to change management.

You’ll learn:

  • The top automation use cases generating ROI for insurers
  • Real-world examples and data points proving the benefits
  • How AI is supercharging traditional RPA bots
  • Key challenges to address for successful implementation
  • An expert view of the future of intelligent automation in insurance

By the end, you’ll have unparalleled insight into leveraging intelligent automation to achieve operational excellence while exceeding customer expectations. Let’s dive in.

Why Insurance Needs Intelligent Automation

First, what exactly is intelligent automation? It combines traditional robotic process automation (RPA) with cutting-edge AI technologies like machine learning, natural language processing (NLP), and computer vision.

This enables bots to handle unstructured data like insurance paperwork, learn from experience, and mimic human judgment for processes like claims assessment.

Now, you’re probably wondering why insurance needs intelligent automation when legacy systems have sufficed thus far. Here are three compelling reasons:

1. Relentless pressure to improve efficiency and cut costs

2. Better customer experiences crucial for retention

  • 41% of consumers say they would likely switch insurers due to poor digital capabilities. Intelligent automation delivers seamless omnichannel self-service.
  • Allstate’s chatbot Abi can answer common customer queries 24/7. After deployment, Abi handled 1 million conversations per month, improving customer experience.

3. Intense competition from insurtech upstarts

Let‘s explore the top use cases where intelligent automation can transform insurance.

Claims Processing: The Key Automation Opportunity

Claims processing automation should be the top priority for most insurers looking to drive efficiency gains. Why?

Let‘s look at real-world examples of insurers transforming claims with intelligent automation:

  • Faster payment with AI damage analysis – Tractable‘s AI reviews photos of auto damage and estimates repair costs in minutes. This helps insurers like Ageas settle claims in hours rather than weeks.
  • Automated document processing – RPA bots and OCR tools extract data from documents like repair estimations submitted in any format. For German insurer Talanx, this cut policy data entry time by 80%.
  • Flagging fraudulent claims – AI analyzes historical claims data to build predictive models that identify suspicious claims patterns. Allianz Italy uses such AI to detect 36% more fraud cases.
  • Chatbots for faster FNOL – USAA‘s chatbot can capture details for first notice of loss (FNOL) and even take pictures of damage. This simplifies the process for customers and eliminates manual effort for agents.

The data shows why claims automation should be top priority:

[Insert infographic showing stats like:

  • $X billion spent annually on claims processing
  • Y% of customer complaints about claims delays
  • Z% efficiency gains from automation per insurer case studies]

More Automation Use Cases Driving Insurance Transformation

While claims automation may have the most potential, other business processes can also benefit greatly from intelligent automation.

Automated Underwriting Removes the Risk of Manual Errors

Insurers like Aviva are using automation to achieve straight-through processing for underwriting new policies. Key capabilities include:

  • Extracting data from submissions – OCR tools classify documents and capture information without human review.
  • External data integration – Hundreds of data sources can be screened to supplement a customer’s application.
  • Predictive analytics – Algorithms crunch all this data to score risk levels and identify discrepancies.

Such automation provides insurers a holistic view of the customer from the start. Additionally, automated underwriting has proven results:

Clearly, automated underwriting eliminates human errors and inefficiencies to make insurers vastly more competitive.

Seamless Policy Administration Improves Customer Loyalty

Managing policy records is ripe for automation, though few insurers have tapped this opportunity. Policy administration involves high volumes of manual tasks like:

  • Processing new applications
  • Communicating policy details
  • Handling renewals
  • Updating customer data

RPA bots excel at these repetitive tasks while machine learning continually enhances document processing capabilities. The benefit? Improved data quality and customer experience.

An Argentinian insurer, for example, deployed automation for new business processes. This delivered 50% faster processing and 50% fewer errors – huge improvements!

Compliance Assurance Through Complete Automation

Insurers must track various regulations across states and countries where they operate. Intelligent automation makes compliance easier:

  • AI continuously monitors regulatory notices and flags relevant updates.
  • Bots instantly check applicants against watchlists to catch banned entities.
  • Reporting and filings are generated automatically in proper formats.

With real-time compliance monitoring and reporting powered by bots, insurance companies can avoid penalties and reputational damage from non-compliance.

Spanish multinational Mapfre automated regulatory compliance processes to gain a holistic view of obligations across 150+ products and 18 countries. Such intelligent automation creates peace of mind for insurers and customers alike.

Key Challenges Insurers Face with Intelligent Automation

While the benefits are compelling, insurers can’t expect overnight success with automation. Here are key challenges to anticipate with mitigation strategies:

Legacy IT systems limit agility

Many insurers run decades-old policy admin systems ill-suited for automation. P&C insurers spend nearly 70% of their IT budgets just on maintaining legacy systems.

Mitigation – Assess integration needs and modernize systems incrementally. Cloud migration can enable flexibility.

Data problems like low quality or silos

Insurers often have customer data trapped in siloed systems. Critical data also sits in unstructured documents. Such issues get amplified with automation.

Mitigation – Invest in data integration, cleansing, and structuring before scaling automation.

Organizational resistance hinders adoption

Lack of change management can derail automation projects. Employees may fear job losses or lack the skills to work alongside bots.

Mitigation – Get buy-in from agents and staff. Provide training and clearly communicate benefits.

Difficulty identifying the right automation opportunities

With limited internal knowledge, insurers struggle to pinpoint the highest value automation use cases across lines of business.

Mitigation – Work with external consultants and advisors who can map your processes to find “automation goldmines”. Alternatively, use process mining tools like Celonis to analyze processes and identify bottlenecks prime for automation.

The Path to Intelligent Automation Excellence

While overcoming these common barriers takes work, it’s entirely possible with the right strategy:

Start small, score quick wins: Focus initial automation on high ROI but simple processes like reconciling reports. Quick wins build confidence for larger initiatives.

Evaluate solutions suited for insurance: Leading intelligent automation platforms like Appian and Pega have robust features for claims, underwriting and more. Read our guide comparing top vendors.

Get good partners: Implementation partners like EY can help with change management and technical challenges. Their experience accelerates automation success.

Invest in integration: Allocate budget for API development, data virtualization and other integration needs identified in assessments. This pays off by enabling end-to-end automation.

Build for continual improvement: Design to continually collect feedback data and feed it to AI models. Like self-driving cars, bots get smarter over time with results data.

With this advice in hand, you’re ready to make intelligent automation a competitive differentiator that delights customers while driving operational excellence. The insurance industry is conservative by nature, but market pressures leave no choice – it’s time to embrace intelligent automation.

What questions do you still have about automation in insurance? Let me know in the comments.

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