Some industries tend to take longer to adopt emerging technologies, both because of complexity issues, but also due to a risk approach to how they run their businesses.
Insurance has been an industry holding tremendous value in the data they hold, but still relying on simple processes which are lagging behind the available mainstream technologies which are already standardised in other industries. Claims processing is one of the most critical touchpoints in the insurance industry, directly impacting customer satisfaction and loyalty.
Traditionally, claims handling has been a manual, labour-intensive process, often leading to delays, inconsistent experiences, and high operational costs. In a competitive landscape where customers increasingly prioritise efficiency and transparency, insurers are seeking ways to streamline and personalise claims processing.
Yet, it's unsurprising that talking about personalisation in processing insurance claims feels like a no-brainer to most non-industry professionals, and a huge effort to the operators of this process. With GenAI, the industry can transition from a reactive, standardised approach to a proactive, tailored experience that meets individual customer needs with precision and speed.
The opportunity goes beyond automating to guiding customers in a truly personalised (and magical) way
What are the key issues with claims and how do they impact the industry?
The conventional claims process has long been encumbered by multiple manual stages, from documentation to evaluation and settlement, which can cause significant delays. This inefficiency frustrates customers and erodes trust, particularly in cases where delays affect financial recovery as well as customers' livelihoods.
Employees, meanwhile, face operational bottlenecks that impact productivity and increase error risk, increasing stress, workplace pressure and burnout.
For businesses, prolonged claims cycles can result in increased handling costs, reduced customer retention, and a subpar market reputation.
How GenAI Could Transform Claims Processing
GenAI enables insurers to analyse customer profiles, claims history, and external factors -such as accident severity and location data - to offer a personalised claims experience. By leveraging data-driven insights, GenAI can categorise claims based on complexity and urgency, enabling instant processing for straightforward cases or routing complex claims to specialised agents.
This model allows customers to receive faster, more accurate service, with GenAI tools keeping them updated in real-time about the status of their claims.
For example, in auto insurance, a GenAI-driven system could leverage device data to securely sign-in customers, leverage photos from an accident scene and voice inputs to instantly process minor claims. The system could assess the damage and immediately calculate repair costs, deploying agents to get the most reliable and up to date information on inputs required (latest quotes, market value, geo-relevant data, weather, road network alerts), offering an instant settlement to the policyholder if they choose to accept it. This streamlines the claims process significantly, resulting in a faster, smoother customer experience.
The catch is, it requires a precise and flexible scale of what can be processed automatically and what needs to be reviewed by a human operator, and for this companies need to leverage their historical records, and in some instances curate gold standard examples - this can be something GenAI helps with, by the way...
Full Automation vs. Assisted Claims Processing
Full Automation: For simple or low-risk claims, GenAI can automate the entire process, from evaluation to settlement, enabling near-instant resolutions - only alerting reviews when it can't complete an action or the output is non-compliant. This approach reduces processing times drastically and improves customer satisfaction by eliminating waiting periods.
Assisted Claims Processing: For more complex claims or high-value cases, GenAI can assist human agents by providing insights, recommendations, and predictive analytics. This empowers agents to make faster, more informed decisions, which improves service quality and enhances customer trust.
Bear in mind the system / solution will be able to comply with policy, regulations and other SOP's from any business, while aiming to deliver an output that is similar to the gold standard defined. the guiderails and policy for the AI element can (and should) be designed for the process and the company.
What are the Metrics and what is the Impact
To evaluate the success and impact of GenAI in claims processing, insurers can track the following metrics:
Average Claim Processing Time: Measures the time from claim submission to resolution. GenAI can reduce this by up to 75%, according to the original dataset, significantly improving efficiency.
Customer Satisfaction Scores (CSAT): Higher customer satisfaction can be a direct result of quicker, more transparent claim resolutions. Post-claim surveys can gauge customer experiences, with targets of increasing CSAT by 15-20% in pilot programs. If done consistently, the metric should become Lifetime Value.
Claims Handling Cost Reduction: GenAI’s automation can lower costs associated with manual handling, such as staff hours and operational overhead. Targeting a 25-40% reduction in handling costs will indicate a strong ROI.
Claim Accuracy and Fraud Detection: By flagging unusual patterns or behaviours in real-time, GenAI can help reduce fraudulent claims and improve accuracy, ensuring that legitimate claims are processed efficiently. Ultimately it can help customers following the appropriate steps and guidelines.
Repetition or Frequency of Claims (Annual Repetition): With GenAI insights, insurers can identify patterns in frequent claims to adjust policies or offer preventative measures, potentially reducing overall claim volume.
Getting Started and Assessing Ongoing Efforts
To introduce GenAI in claims processing, insurers should start with a proof-of-concept (POC) approach, such as handling a specific category of claims (e.g., minor auto claims) to validate GenAI’s efficiency and accuracy. During this phase, it is crucial to compare metrics against traditional processing methods, tracking processing times, cost savings, and customer satisfaction.
As insurers scale GenAI across more complex claims, they should continuously evaluate performance using the metrics mentioned above. Additionally, they can incorporate feedback loops for continuous learning, refining algorithms and improving customer experience over time.
The key question will be how to redesign the experience of the needed stakeholders to keep improving efficiency, and at what pace does that change get implemented as scale.
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