In comparison to other industries, the insurance sector has struggled to keep up due to low levels of innovation.

This is partially because consumers are less interested in insurance goods until an urgent need arises, and partly because providers must adhere to stringent risk and compliance regulations and use rigid legacy systems.

To make their product more relevant to clients, insurers have therefore had less need—and less ability—to innovate. As a result, many existing players have concentrated on solidifying their positions, making business expansion more challenging, expensive, and complex.

As a result, insurers are increasingly looking to collaborations with insurtech companies as the “magic solution” to quicken the insurance industry’s digital transition.

Customers may manage their insurance portfolio and submit a claim with the assistance of chatbots powered by AI, for instance.

When combined with behavioral data from smart watches or cars, the use of AI and machine learning technology can aid insurance carriers in the detection of fraud, support the development of new business models, and help customize plans and lower premiums.

The idea of “embedded insurance” is, nevertheless, possibly, the most significant trend and the strongest growth lever for insurance providers. This is not a brand-new business model; rather, it expands the reach of what we have come to refer to as “bancassurance” to new prospective retailers that sell goods or services to customers online.

When a consumer is making a transaction, embedded insurance allows suppliers to sell micro-insurance goods or services. A prime illustration of that is when you get travel insurance along with a ticket or vacation.

Because of this, integrated insurance must be digitalized, whether through online sales channels, e-commerce platforms, apps, standalone websites, or even at non-points of sale. And to accomplish this, most insurers must collaborate with insurtechs.

Legacy insurers can modernize by expediting innovation in the sector

There are many other ways for businesses to update their outdated platforms, and not all of them entail integrating with an insurtech’s platform.

There is no one technique that works for everyone, and the best course of action greatly depends on the current status of the organization’s strategic goals and the design of its systems.

Many businesses should start by prioritizing modernization and innovation and spending the necessary time and money to outline their strategies.

Implementing innovative working practices like Agile or DevOps, which will hasten innovation and the development and deployment of systems, is one such strategy.

Additionally, businesses may try to re-architect their systems using techniques like microservices, modularization, or containerization to increase agility while reducing complexity and interdependencies.

Companies may need to adopt an API-first strategy or migrate (or partially migrate) their old systems into the cloud if they wish to benefit from insurtech innovation. They might even decide to replace their legacy systems completely, which would be a huge move.

Companies may think about collaborating with an expert partner to assist them sort through the plan and determine the best course of action given the variety of possibilities, the potential cost, risk, and complexity involved.

Key factors to prioritize when modernizing legacy systems

When modernizing systems, there are several factors to consider. Among the crucial ones are:

Business impact: How does the current system affect the company’s present and long-term strategic goals? How well does the system satisfy customer needs, and what maintenance risks exist, such as downtime and security issues?

Cost-benefit evaluation: What are the costs associated with operating the current system, taking into account the cost of construction, upkeep, licenses, and the financial impact of unavailability? What are the expenses incurred by modernizing, as well as the financial gains in terms of spending, productivity, and client retention?

Technical debt and complexity: How much complexity has built up over the years, considering obsolete technologies, unsupported software, and intricate interdependencies? What effect do these have on technology integration, maintenance, and stability?

Interoperability and integration: How effectively does the current technology suit the needs for interoperability and integration with internal and external systems now and in the future? Can the system support contemporary APIs, data formats, and protocols? How well does it integrate with other technologies, such as cloud, mobile, and third-party systems?

Future scalability and innovation: To what extent is the system capable of supporting foreseeable expansion and innovation? Can it support cutting-edge technologies like artificial intelligence (AI) and machine learning, and can new features and functions be added quickly and easily thanks to the architecture’s support for modular upgrades and agile development?