The Transformational Impact of Intelligent Automation on the Insurance Industry

The insurance sector has lagged other industries in harnessing advanced technologies. However, the drive towards digitalization has made automation imperative for insurers to stay competitive. One of the most disruptive solutions in this regard is intelligent automation – combining robotic process automation (RPA), artificial intelligence (AI), machine learning (ML) and related technologies.

This comprehensive guide explores how intelligent automation is elevating efficiency, accuracy and speed across insurance processes. We will cover the capabilities and benefits of these emerging technologies, real-world use cases, implementation best practices, overcoming adoption barriers and the future outlook.

Core Capabilities and Benefits of Intelligent Automation

Key Technologies Powering Intelligent Automation

Intelligent automation integrates the specialized capabilities of several advanced technologies:

Robotic Process Automation (RPA) deploys software programs for automating repetitive, rules-based tasks by simulating user actions. RPA bots can interpret structured data from multiple formats, execute defined workflows across various systems, and integrate with both legacy and modern IT systems.

Artificial Intelligence (AI) focuses on enabling computer systems to perform tasks that traditionally required human cognition and decision making. Key focus areas include visual perception, speech recognition, language translation, emotion detection and complex strategic planning.

Machine Learning (ML) allows computer systems to learn patterns from data and improve their performance independently without explicit programming. Techniques like supervised learning, unsupervised learning and reinforcement learning enable machines to build predictive algorithms, identify anomalies and optimize outcomes.

Natural Language Processing (NLP) facilitates understanding and automated processing of human languages by machines. Capabilities like text classification, content summarization, sentiment analysis, intent detection and document comprehension are achieved using NLP.

Computer Vision (CV) enables computers to accurately identify, classify and apply context to understand visual elements in digital images and videos. Object detection, facial recognition, image labeling and scene understanding are key focus areas of CV with applications across sectors.

Combined appropriately, these technologies enable end-to-end process automation replicating human-like judgement for everything from simple rules-based tasks to complex cognitive decisions. This results in tremendous value addition for insurance companies.

Major Benefits of Adopting Intelligent Automation

By deploying integrated automation solutions spanning RPA, AI and advanced analytics, insurers can drive step function improvements:

Improved efficiency by automating repetitive, manual tasks across both back and front-office
Faster processing by reducing process cycle times through straight through processing
Cost optimization by lowering expenses associated with manual effort and redeployment of talent
Enhanced compliance through automated controls and reduced errors or oversights
Better customer experience owing to quicker response times and 24/7 availability

According to McKinsey, intelligent automation can lower insurance operating costs by 25-40%. Leading global insurers have witnessed over 50% efficiency gains in early automation programs.

The magnitude of potential impact makes a compelling case for insurers to accelerate their intelligent automation programs.

Major Application Areas and Use Cases

Many core insurance processes involve extracting insights from masses of unstructured data, analyzing information from multiple systems, and undertaking repetitious administrative workflows. These characteristics make them ideal candidates for automation leveraging AI-based algorithms.

Claims Management

As the shopfront for policyholders to avail their coverage benefits, the claims process significantly impacts customer satisfaction. Manual claims handling procedures extending days or weeks reflect poorly on insurers. Intelligent automation is driving dramatic improvements:

Chatbots expediting straightforward claims by obtaining essential customer data
Computer vision analyzing photos of damaged assets for accelerated processing and settlements
Machine learning detecting fraudulent claims behavior to minimize losses
Natural language processing classifying claims based on description texts quickly

By using telemetry data from connected vehicles and IoT devices, insurers can even facilitate real-time automated claims initiation and settlements. Increased transparency and faster payouts through intelligent automation boost customer loyalty. For instance, California-based auto insurer Mercury leveraged automation to reduce claims cycle times from days to just 90 seconds.

Underwriting and Policy Administration

Risk assessment and pricing are crucial for sustainable profitable growth. Yet underwriters often struggle with information overload. Intelligent automation facilitates:

Data ingestion from documents and systems followed by classification using NLP
Customer lifetime value analysis through predictive models to optimize acquisition and renewals
Accelerated automated underwriting workflows for instant policy quoting
Proactive identification of at-risk policies for interventions using machine learning

By integrating enriched data encompassing credit scores, health histories and behavioral analytics into pricing algorithms, insurers can achieve more granular risk segmentation across product lines without increasing costs. For instance, by factoring in driving telematics and usage analytics from connected cars, Progressive insurance optimized risk models and improved loss ratios substantially.

Regulatory Compliance

Increasing regulations is proving challenging for insurers even as compliance budgets escalate. Non-compliance risks significant financial penalties and reputational damage. Intelligent automation allows proactive controls and monitoring such as:

Aggregating compliance data across systems seamlessly using ingestion bots
Applying machine learning models to detect transactions at risk of breaching regulations
NLP bots to interpret regulatory edicts and announcements for required changes
Automating generation of compliance reports and filings for regulators

For instance, by centralizing compliance processes on an integrated automation platform, MetLife reduced manual efforts by over 60,000 hours annually while improving accuracy.

Practical Examples and Case Studies

Global insurers and reinsurers have showcased substantial benefits from intelligent automation adoption:

FM Global

Specializing in commercial property coverage, FM Global automated its premium audit process using an AI solution called COGNICA. The virtual bot assistant can answer client queries, request necessary information, compile reports and submit for underwriting approval. FM Global increased quality and efficiency with over 50% productivity improvements without any reduction in audit accuracy.

John Hancock

The large life insurance provider streamlined processes across new business, underwriting and claims departments using integrated intelligent automation. For death claims, the solution facilitates identification of deceased customers, collects documentation, confirms identities, calculates payouts and disburses approvals. By automatically validating eligibility and settlement amounts, processing times reduced from 7 days originally to less than 5 hours currently.

USAA

USAA wanted to simplify documentation submission for military members making disability claims. Their intelligent automation solution allows claimants to use mobile phones for scanning and transmitting claim paperwork. Documents are classified automatically based on machine learning models. Relevant data is extracted using optical character recognition and natural language processing to instantly start claims processing. This has minimized turnaround times to under an hour through straight through processing enabled by AI-based algorithms.

Emerging Technologies Powering Next-Gen Automation

While RPA, traditional machine learning and NLP are driving the current wave of intelligent process automation, more advanced technologies are poised for widespread adoption:

Computer Vision (CV)

Insurers receive millions of documents from customers daily, spanning new business applications, claims forms, invoices, medical reports etc. Human review of such volumes is immensely time-consuming. Computer vision facilitates automated document classification and data extraction at scale using deep learning algorithms.

By analyzing photos of damaged assets, CV models can accelerate claims assessment and settlement. Convolutional Neural Networks (CNNs) can be trained on labeled images of previous property damage, floods or car crashes to determine repair estimates or assign severity scores automatically.

Conversational AI

Chatbots and voice-based virtual assistants are allowing insurers to engage customers conversationally. Natural Language Processing (NLP) and Machine Learning power automated queries, ability to understand context and intents, formulate relevant responses and even handle complex transactions.

USAA implemented a virtual assistant called EVA for seamless self-service. State Farm’s chatbot trained on industry data handles nearly 5 million customer conversations monthly. Platforms like Clinc, Cogito and Boost.ai offer plug-and-play enterprise grade solutions for insurers to launch smart virtual agents.

Process Mining

Process mining utilizes data captured in existing information systems to provide objective visibility into real processes, as-is workflows, deviations, bottlenecks etc. Leveraging process mining, insurers can fully understand existing claims, underwriting and policy servicing pathways before optimizing them through automation. It allows factual assessment of whether expected benefits of automation actually materialized post implementation.

Best Practices for Implementation

The right approach is crucial for scaling automation initiatives successfully:

1. Discovery:

Focused identification of the best opportunities using well-defined selection criteria:

Prioritizing processes with highest manual effort, compliance impact etc.
Benchmarking efficiency metrics like turnaround times and straight through processing rates
Creating detailed process maps documenting current workflows
Estimating automation potential across assessed processes

2. Solution Design

Detailed design aligned to process complexity covering:

Finalizing appropriate technologies – RPA, OCR, NLP, AI/ML etc.
Architecting automated flows balancing bot and human touchpoints
Incorporating fail-safes, security controls and validations into solution

3. Development & Testing

Employing DevOps approach and proven platforms to accelerate progress:

Leveraging low-code for rapid visual assembly of automations
Ensuring adequate solution training using representative datasets
Extensive simulations and scenario modeling during user testing

4. Deployment & Maintenance

Managing automation solutions effectively post go-live:

Deploying across modular process areas for easier migrations
Instituting monitoring covering usage metrics and user feedback
Formalizing protocols for minor enhancements and major upgrades

Getting these fundamentals right paves the way for scaled automation success. Leading insurers have established automation Centers of Excellence (CoEs) encompassing all requisite capabilities under one roof.

Overcoming Barriers to Automation Adoption

Despite a compelling rationale, insurers also encounter barriers in harnessing automation:

Data and Analytics Challenges

Insufficient data volume and inadequate data quality impedes AI/ML model development
Information spread across legacy systems with limited APIs or interoperability
Lack of analytical talent such as data scientists and ML engineers

Using technologies like data lakes, enterprise service buses and cloud platforms can help consolidate data in a unified environment conducive for analytics. Collaborating with IT solution providers allows insurers to augment niche digital capabilities.

Risk Management Considerations

Unclear regulatory guidance regarding responsible usage of AI in financial services
Data privacy concerns emanating from increased data harnessing
Lack of transparency regarding functioning of black-box AI algorithms

Instituting protocols like ethics boards for algorithms, external algorithmic audits, model risk management procedures aligned with regulators can alleviate these risks.

Organizational Change Challenges

Insufficient executive sponsorship to drive enterprise-level strategy and investments
Pockets of internal resistance restricting uptake of new technologies like AI/ML
Inadequate communication regarding automation objectives and employee impact

Continuous capability building via global reskilling programs, emphasizes on micro-learning using mobile platforms and gamification to drive technology awareness can secure buy-in across the organization.

The Outlook for Insurance Automation

As competitive intensity increases, intelligent automation will become the fulcrum driving insurers’ transition into agile digital enterprises. Efficiency improvement is just table stakes – leveraging data and analytics to refocus from products to personalized customer experiences will be the key differentiation. McKinsey estimates over $1.1 trillion value at stake from disruptive technologies across the insurance sector.

We are already experiencing this disruption. Growth in cloud, IoT sensors, conversational platforms, computer vision and other exponential technologies will elevate customer engagement potential to new heights. Early automation adopters have realized over 80-90% efficiency gains in optimized processes. Continued innovation promises countless possibilities to delight end consumers. However, the risks from opaque algorithms and ungoverned AI systems necessitate balanced oversight through framework spanning machine ethics, external audits and explainable AI.

By proactively harnessing automation-led transformation, insurers have an opportunity to redefine services that provide financial safety nets to customers across life, health and P&C segments. The window for action is now. Are you ready to reimagine this industry?