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Challenges in Auto Adjudication of Motor Claims

Updated: Jun 12

Auto adjudication in motor insurance promises faster settlements and fraud detection. However, challenges like data quality, lack of standardization, and potential bias hinder its smooth adoption in India. The IRDAI is taking steps with draft guidelines, but collaboration on data quality and coding practices is crucial. Transparency and explainability are essential for building trust.

The Indian motor insurance industry is a significant contributor to the overall insurance sector. For the period from December 2022 to December 2023, Non-Life Industry has underwritten GDP of Rs 66,139 Cr under Motor segment with a growth rate of 14.26% as compared to GDP of Rs 57,883 Cr. The entire Non-Life segment has a GDP or Gross Written Premium (GWP) exceeding ₹2.1 lakh crore (₹2,10,000 crore) in FY 2022-23 [Source: Insurance Regulatory and Development Authority of India (IRDAI)]. To streamline claim processing and improve customer satisfaction, the IRDAI is pushing for the adoption of auto adjudication for motor insurance claims.

What is Auto Adjudication?

Auto adjudication refers to the automated processing and settlement of insurance claims using pre-defined rules, algorithms, and machine learning models. This technology offers potential benefits for the Indian motor insurance sector:

  • Reduced Processing Time: Automation can significantly reduce claim processing time, leading to faster payouts for policyholders.

  • Minimized Manual Errors: Eliminating human intervention minimizes errors and inconsistencies in claim decisions.

  • Enhanced Fraud Detection: Advanced analytics can identify potential fraud patterns more effectively, protecting insurers from fraudulent claims.

  • Improved Customer Experience: Faster claim settlements and reduced paperwork lead to a more positive customer experience.

However, implementing a robust auto adjudication system in the Indian motor insurance landscape faces several challenges:

1. Data Quality and Standardization

  • Incomplete and Inaccurate Data: Accident reports, repair estimates, and policy documents often contain missing or inaccurate information. This can lead to misinterpretations and errors during automated processing.

  • Lack of Standardization: Damage assessment procedures and repair cost estimates vary across garages and workshops. This inconsistency makes it difficult for algorithms to accurately assess claim value.

2. Regulatory and Compliance Issues

  • Evolving Regulations: The IRDAI is actively developing regulations for auto adjudication. Insurers need clarity on data privacy concerns and compliance requirements.

  • Explainability of Decisions: In case of claim denials, policyholders need clear explanations for the automated decisions. Transparency builds trust in the system.

3. Bias and Fairness

  • Algorithmic Bias: AI models trained on historical data might perpetuate existing biases. For example, location-based biases could disadvantage claims from certain regions.

  • Human Oversight: While automation offers efficiency, human oversight remains crucial for complex cases and to ensure fairness in claim decisions.

Recent Actions by IRDAI

Recognizing the potential of auto adjudication, the IRDAI is actively involved in facilitating its implementation:

  • Draft Guidelines on Claim Settlement Processes: These guidelines outline the framework for auto adjudication, highlighting data standardization, claim processing timelines, and grievance redressal mechanisms.

  • Focus on Data Quality: The IRDAI emphasizes the importance of data quality in accident reports, repair estimates, and policy documents. Standardization of data formats is a key focus area.

  • Promoting Collaboration: The IRDAI encourages collaboration between insurers, repair workshops, and technology companies to develop robust claim processing systems.

Moving Forward:

Despite the challenges, auto adjudication offers significant potential for the Indian motor insurance sector. Here are some key steps to ensure its successful implementation:

  • Improved Data Quality: Insurers, repair workshops, and authorities need to collaborate on improving data quality in accident reports, repair estimates, and policy documents. Standardisation of data formats is crucial. Drona Pay has pre existing libraries to process standardised motor claims data to automate claims processing. 

  • Investment in Analytics: Insurers need to invest in advanced analytics and machine learning capabilities to develop robust claim processing systems. There is a need to develop strong profiles for various damages, spares, towing, labour and other expenses to to identify outliers and anomalies which need adjuster / surveyor review. Drona Pay offers a new age Decisioning Platform to help automate claims processing. 

  • Transparency and Explainability: The claims adjudication process needs to be transparent, with clear explanations provided for claim decisions, especially denials. This builds trust in the system. Explainability and usage of Profiling and Rules is a key building block of the Drona Pay platform. 

  • Human-in-the-Loop Approach: A human-in-the-loop approach is crucial. While automation handles routine claims efficiently, human expertise remains essential for complex cases and medical judgement. Drona Pay offers a case management and BPMN modeller to support review by Adjudicators, Surveyors and Risk Analysts along with facilitating communication with Garages and Claimants. 

Continuous Monitoring and Improvement: Continuously monitor the performance of auto adjudication systems and refine algorithms to address emerging trends and prevent bias. Back testing and Simulation are key features of the Drona Pay platform which helps insurers test and monitor the platform.

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