Using AI to Energize Claims Processing for Insurance TPAs  

By Marc Rothchild, Senior Vice President – Head of Claims, Xceedance

The insurance industry was valued at around $5.5 trillion in 2021, putting it at a level larger than the GDPs of several superpower nations, barring the United States and China. This number comes from the time when Covid-19 was at its peak and its impact was being felt globally. Post the pandemic, the industry picked up pace, resulting in enhanced load for the TPAs, who now handle more claims than ever, both life and non-life. Despite growth in underwriter headcounts, the overall claims handling efficiency continues to hover around 66%, according to a report prepared by Accenture. This has resulted in operational losses for insurers, projected upwards of $50 billion. 

Role of AI in Claims Processing 

Given these growing challenges, artificial intelligence (AI) based solutions have become a critical differentiator between success and failure. The Accenture report noted two crucial aspects that could severely curtail growth in the industry. The first is that more than $170 billion in premiums was at risk over the next five years due to customer dissatisfaction over the claims process. The second is that underwriters lose 40% of their time on non-core activities, translating into an efficiency loss of $85-$160 billion in five years. 

These factors are good enough reasons for insurers to take a cue from other industries and adopt AI into their operations faster. There’s also an apparent increase in the investments on this front, expanding its use. Five years ago, AI-led tools had limited usage in the business. With technology maturing and historical data becoming available through digital transformation processes, AI can deliver substantial impact, especially in claims settlement, to help insurers boost productivity, possibly 20% or more. 

There are multiple use cases for AI in the insurance industry, ranging from customer engagement and retention to sales & marketing and underwriting & claims processing. Insurers already using the technology and tracking its impact on their operations claim that operating profits have increased between 10% and 25%. However, they warn that AI implementation takes time to show results. These numbers may not appear big enough at the moment, but research suggests that the impact could grow in tandem with the growth in the industry prospects. 

The immediate shift that enterprises have reported through AI usage is the automation of several manual and repetitive tasks. The entire AI and machine-learning process has helped insurers create customized data templates across multiple divisions to get zero-error data acquisition and storage. This represented a significant shift from the era of manual processing that carried data-entry challenges and took up a substantial amount of third-party administrators’ time. 

Tangible benefits of AI usage in claims processing

The impact of using artificial intelligence in claims processing is slowly gaining ground. Right from integrating and structuring historical data to improving fraud detection and prevention, recording processing time and cost, and enhancing accuracy and consistency in claims assessment are some of the key benefits. Of course, there is also the strategic impact of having historical data for better claims management and its use alongside predictive analytics in processing to get a better hold on forecasting. Let’s delve into some details:

●      Integration and structuring of data – The TPAs were hard-pressed to integrate data collected from multiple sources such as email, WhatsApp, and text messages. Now, AI is capable of compiling, structuring, sorting, and analyzing all of this for generating insights that serve better decision-making amongst TPAs. Additionally, the AI can handle processes across activities, enabling TPAs to leverage historical data effectively for managing their business. 

●      Improved fraud detection and prevention – TPAs can leverage AI to reduce the risk in insurance claims by establishing oversight of claims and highlighting anomalies through historical data comparisons. Insurers can also better forecast claims more accurately using AI, thus allowing them to optimize risks that will positively impact their combined ratios. 

●      Reduced processing times and costs – Since AI can process data faster and more accurately, the overall time taken to process claims gets reduced with greater efficiencies built in. These two factors cut down operational costs while driving better customer retention due to better relationship management. 

●      Better accuracy and consistency in claims assessment – Since AI can manage mundane work consistently, it leaves the TPAs with time to plan for additional workloads and its adjusters to focus on more value-added claims handling activities. Error reductions compared to manual data entry also ensure that insurers can deliver better customer experiences and deeper and more accurate data to the insureds they service. 

Some challenges and considerations

As is the case with any new technology innovation, AI also brings with it some major challenges. Besides ethical considerations, there are questions about data privacy issues, compatibility across legacy systems, and preparing the workforce for a paradigm shift. Let’s explore some of these: 

●      Ethical considerations – A lot has been spoken about AI biases and how Insurer’s Claims teams and TPAs must be wary of them. AI models are being continuously trained, and insurers must assist in this process by sifting out obvious biases and correcting them. This is where human intervention plays a key role as the systems mature and learn to handle situations termed outliers. 

●      Data privacy and security – Data privacy and privacy fall under the purview of global laws, making it imperative for insurers to install adequate measures. Any leaks could result in repercussions for the insurer and the insured, besides bringing the regulatory norms into focus. 

●      Integration and compatibility – Integrating new technologies with legacy systems is a challenge at the best of times. TPAs need to make early investments to upgrade their systems to build compatibility so that AI can function seamlessly. 

●      Training and upskilling – Finally, the TPA workforce must be skilled in using AI in claims handling. Retraining and capacity-building investments would be crucial for getting optimal productivity from AI investments. 

Future trends and outlook

As the insurance industry adopts AI in claims, it is becoming evident that the industry is benefiting through enhanced customer experience, faster claims processing, automation of manual and repetitive tasks, and improved accuracy and consistency of data, resulting in better insights. This is important as Insured and TPA claims organizations look to tackle the evolving claims adjuster staffing market challenges. 

The next stage of this evolution involves broadening the scope of data collection from connected devices, which further simplifies the claims processing through better risk assessments. 

In conclusion

Artificial Intelligence is already making an impact on the claims processing efforts. They have considerably reduced manual operations and given the TPAs more time to play a strategic role in the business. Early adopters have reported improved bottom lines by up to 20% in their operations, taking the first step towards integrating structured and unstructured data and obtaining historical information for better quality analysis and insights. The growing use of Internet of Things (IoT) devices in the health, automobile, and real estate business would add considerably to the data sources and deepen the insurance industry’s involvement with AI. All these innovations would drive a positive impact not just for the insurers but also for its customers.