In the increasingly complex world of risk and insurance, most risk managers are well aware of the number of potential risks that lie behind their brokers’ doors that can have major client-side impact.
A big concern is the efficiencies (or lack thereof) their agencies have achieved on three mission-critical fronts: policy management and maintenance; risk placement; and risk advisory services.
Why should the broker’s operational inefficiencies be a concern to client-side risk management teams? Well, a top priority is to keep broker partners ahead of the risk curve and ensure their policies at renewal adequately cover the business today – and tomorrow. Operational risk measurement and management has a major impact on insurers and their brokers’ financial risk. Reducing information asymmetry due to enhanced data and allowing better predictability is key to monitoring and managing operational risk.
It’s increasingly challenging for brokers to deliver on this promise of risk mitigation. One solution is adopting smart technologies that streamline processes and make them more efficient. By digitizing repetitive work that is prone to errors and omissions, technologies enable broker organizations to become more humane, resilient to change and profitable.
By 2025, according to the World Economic Forum, the amount of work done by machines will jump to more than 50%, most of which would be replacing repetitive, boring and low-quality manual work. AI technologies including chatbots, cognitive automation and robotics provide a streamlined, automated and quick insurance experience for its customers, and a highly cost-efficient process to the insurers.
Insurance agencies have an opportunity to get ahead of this trend with technologies – artificial intelligence, machine learning and process automation – that will vastly improve how well they service client accounts. When this occurs, ultimately, everyone wins.
In Search of a Fix
Policy checks and risk placement at renewal remain big stumbling blocks for brokers. While largely manual solutions combining AI and machine learning are starting to make a dent in improving this process, there’s substantial room for improvement.
The numbers speak to the complexities.
Take a typical mid-market policy of 300 pages, with 3,000 or more line items on various schedules. It all needs to be checked for accuracy and variances with supporting documentation such as binding documents, endorsements and exclusions. The initial check – often outsourced – takes about 45 minutes, but then it comes back to the manager who spends another 15 minutes or more looking for variances and validating the policy in a side-by-side comparison.
Each renewal (and new business) policy needs to go out to different carriers for quotes that are competitively structured and priced, comprehensive and right for the client’s needs. Comparing those multiple quotes to determine carriers that will be most responsive to each client’s risk success stories is painstaking, often requiring numerous hours for each client account.
Multiply those policy check and quote comparison activities by the hundreds of policies handled by a mid-sized broker over the course of a year, and the high cost, frequent error drawbacks of the manual system become obvious.
COVID-19 protocols disrupted these and other processes. That disruption, along with escalating turnover among experienced insurance personnel and outsourcing resources that became more restricted, has hamstrung agency operations. Those challenges have led to significant backlogs that delayed policy deliveries by months and created rising E&O risks – and claims – for brokers.
The risks of continuing with inefficient solutions are mounting. Insurers like Lloyd’s of London and some regulators in the U.S. are clamping down on the delays in final delivery of policies. Financial penalties make good incentives for improving the process. Even better than simply avoiding punitive measures, the cost savings for a broker are substantial if an automated, digitized process is adopted.
See also: Why Are We Still Talking About Digital Transformation?
Toward a High-Tech/High-Touch Experience
Solutions are available that address brokers’ administrative risks from within – in a way that focuses on the customer/risk manager experience and leads to vastly improved alignment between them. The stakes are such that investing in these advanced solutions is no longer optional.
By harnessing state-of-the-art technology – artificial intelligence, machine learning, natural language processing (NLP) and cloud – the broker is better-equipped to extract information from unstructured data. Among the benefits:
- A faster and more accurate policy-checking process. Enhanced analytics of policy limits, deductibles, exclusions and demographic information means potential errors and omissions are identified and corrected with speed and accuracy.
- Improved review of carrier quotes. Multiple quotes and important features (like premium pricing and limits) can be efficiently compared, and the analytics of their variations used to guide recommendations to best meet the client’s needs. No longer will brokers be constrained by the labor-intensive manual process of collecting and analyzing the quote data.
- Claims automation. AI-driven touchless claims make claims management more efficient, accurate and, ultimately, faster.
In the end, as more agencies move to adopt advanced capabilities, the trend will work to the risk manager’s benefit. Smarter and more efficient broker operations will mitigate the hidden risk to clients of high-volume, rushed, rote tasks that can lead to oversights and a tenuous work product.
Better yet, when the power of technology is leveraged by brokers, it frees them to provide an improved, high-touch, consultative customer experience. Better use of data improves the broker’s insights into the client’s seen and unseen risk challenges. Those advances foster the kind of relationship that keeps risk management teams ahead of the risk curve, whatever circumstances their businesses face.
It’s the kind of risk mitigation everyone can understand.
