Leveraging Insurance Process Automation Tools for Human Approvals
The real threat to jobs is not automation, it's irrelevance. Process automation in insurance is not about replacing humans, it's about freeing them from repetitive, mind-numbing tasks so they can actually think, analyze, and build relationships.
The insurance industry is drowning in manual processes and approval chains. Unfortunately, one of the reasons why automation is not maximized as much as possible is because of the misconceptions about how it will completely replace human workers.
In fact, I believe that automation will create new job roles that require specialized skills to manage, maintain, and optimize automated systems.
The insurance industry is heavily regulated, with complex and ever-evolving rules. Human experts are essential for subjective judgments and a deep understanding of industry trends.
Therefore, the best approach for efficient approval processes is a combination of process automation and human expertise.
While AI-automation offers significant potential in automating insurance approvals, relying solely on it without human oversight presents several risks:
- Bias: AI algorithms can make biased or discriminatory decisions based on the data they are trained with
- Lack of Common Sense: AI systems often struggle with understanding context and applying common sense reasoning, which can lead to errors in complex decision-making scenarios.
- Lack of Transparency: Some AI models can make it difficult to understand the reason behind certain decisions, hindering trust and accountability.
- Regulatory Compliance: Purely AI-driven systems may struggle to comply with complex and evolving insurance regulations, increasing the risk of legal and financial penalties.
How to Leverage Process Automation Tools for Human Approvals
Here are the different insurance processes that require human approvals and how we can collaborate with automation tools to make these processes more efficient
New Policy Applications:
AI-powered tools can automatically extract, clean, and validate data from policy applications. This includes verifying the applicant's identity, employment, income, and medical history. Human underwriters can then review complex or inconsistent data points. For instance, a manual review may be needed for self-employed applicants with complex income structures.
Risk assessment can be automated based on historical data and underwriting guidelines. Automating this process will provide underwriters with risk scores and potential underwriting scenarios. Underwriters can then leverage their expertise to interpret risk scores, consider qualitative factors, and make final underwriting decisions.
Policy Changes
AI-powered automation tools can be used to verify policyholder information, policy details, and supporting documents. This enables underwriters to focus on complex data inconsistencies that require human interpretation and decision-making like investigating conflicts in policyholder income or occupation.
Automation can be used to assess the risk associated with a proposed policy change based on predefined rules. Underwriters can then leverage their expertise to consider qualitative factors and make informed decisions.
Claim Submission
Automation can be used to validate data from claim submissions. This includes verifying incident details and supporting documents. A human claim adjuster can now investigate conflicts in claim amounts or policy coverage.
Machine learning algorithms can be used to assess the initial validity of claims based on historical data and claim patterns. Human claim adjusters can then review complex or high-value claims, and leverage their expertise to assess the credibility of the claim.
AI-powered image recognition and data analysis can be used to assess damage extent and estimate repair costs. This provides more time for claim adjusters to consider factors like depreciation and salvage value, and make final damage estimates.
Routine inquiries can also be automated to provide information about the claims process. This enables claim adjusters and customer service representatives to address complex issues, provide personalized support, and build customer relationships. For instance, a human agent might assist a policyholder with understanding the claims process or explaining the reasons for a claim denial.
Adjuster Approvals
Automation tools can be used to analyze adjuster performance data, including claim handling time, accuracy of estimates, and adherence to guidelines.
Claims managers can then review complex performance metrics, identify trends, and provide coaching or corrective actions when needed. A claims manager is still needed to investigate an adjuster with consistently low settlement ratios to identify potential issues.
Process automation tools can be used to Implement quality control checks on adjuster-submitted documentation, such as photos, estimates, and reports. This enables claims quality reviewers to focus on complex cases or potential quality issues identified by automation.
Premium Audits
Premium audits are a critical component of insurance risk management and revenue generation.
Process automation tools can be used to extract, and validate data from audit reports, including policy information, exposure data, and financial records. Auditors are then needed to focus on complex data inconsistencies that require human interpretation and decision-making. For example, a human auditor is needed to investigate conflicts in reported payroll or inventory values.
Audit selection can be automated with predefined rules based on risk factors, audit history, and available resources, while audit managers review and adjust audit assignments based on auditor expertise, workload, and complex cases.
Payment Plans
The effective management of payment plans requires a balance of human oversight and automated processes. This combination can enhance efficiency, accuracy, and customer satisfaction.
Automation can be implemented to assess eligibility for payment plans based on predefined criteria, such as policy type, premium amount, and policyholder financial history. Underwriters can then review cases that fall outside standard eligibility criteria or involve complex financial situations. For example, a human underwriter might approve payment plans for policyholders with unique circumstances, such as recent job loss or medical emergencies.
Cancellation Requests
Machine learning can be used to assess eligibility for cancellation based on predefined criteria, such as policy type, policy term, and cancellation reasons.
Underwriters can then review cases that fall outside standard eligibility criteria or involve complex situations. For example, a human underwriter might approve cancellations for policyholders facing financial hardship or experiencing a change in life circumstances.
Process automation can be implemented to route cancellation requests based on predefined rules, assign tasks, and track progress. It enables customer service representatives to focus on complex or high-risk cancellation requests, providing expert judgment and ensuring compliance with company policies.
4 Human-in-the-Loop Process Automation Tools For Insurance
Activepieces is a platform that enables human-in-the-loop (HITL) process automation, allowing insurers to combine the strengths of AI and human expertise to improve the accuracy and efficiency of insurance approval workflows. Activepieces allows insurers to build custom applications that connect AI-powered automation with human authorization and feedback loops. This enables insurers to do the following:
- Automate routine tasks like data extraction, policy administration, and claims processing
- Route complex or ambiguous cases to human underwriters for review and approval
- Capture human feedback to continuously improve the AI models
- Centralized tracking and monitoring of approval status and exceptions
- Reduce turnaround times for approvals and claims
- Improve accuracy and consistency in risk assessment and policy decisions
- Provide a better customer experience through more responsive and transparent processes
Oracle BPM
Oracle BPM provides robust capabilities to enable human-in-the-loop (HITL) automation for insurance approval workflows
The Approval Management Extensions (AMX) in Oracle BPM allow insurers to model multi-stage approval processes and determine the appropriate approvers based on business rules and the organizational hierarchy.
BIZAGI
Bizagi's process automation platform provides capabilities to enable human-in-the-loop (HITL) workflows for insurance approval processes It allows insurers to dynamically route tasks to the appropriate approvers based on business rules and user roles/permissions.
Pega systems
Pega enables insurers to define approval list builders based on supervisor or position hierarchies. It also supports integrating with identity management systems to look up user roles and permissions.