How secure are systems now in the insurance industry?

Big tech players tackle tech challenges in legacy systems amidst digital vulnerabilities.

A growing consensus amongst insurers that integrating technology is proving to be the most challenging aspect, despite their successes built on legacy systems. However, this digital shift has increased vulnerabilities, leading to a rise in data breaches, phishing attacks, and ransomware incidents, which have surged by almost 20% annually.

Industry experts tell the urgency to adopt comprehensive technology and training strategies to mitigate these risks. 

This includes qualifying artificial intelligence (AI) models from a security perspective, ensuring proper patching, and maintaining up-to-date product versions. Training programmes are also essential not only for staff but also for customers to enhance overall cybersecurity awareness.

“There are also increasing demands being made by data protection legislation, and the insurance industry has been largely in support of these changes,” Shrinidhi Kumar – vice president of Global Delivery, NTT DATA, told Insurance Asia.

/Shrinidhi Kumar, vice president of Global Delivery at NTT DATA

Insurers are responding with advanced technology solutions and improved vendor management practices. However, these adaptations increase compliance costs and policy servicing expenses. 

Consequently, insurers are turning to regulatory technology, a sector forecasted to grow by 20% globally, with Asia expected to lead this growth. 

Prakash Thomas, head of Sales – Insurance, JAPAC & EMEA at Oracle, told Insurance Asia separately that backend legacy systems are the most significant constraints for insurers today.

With the introduction of more complex products, there is a pressing need for a flexible IT ecosystem that can rapidly adapt. “Cloud technologies and solutions play a crucial role in the industry,” he said.

/Prakash Thomas, head of Sales – Insurance, JAPAC & EMEA at Oracle

Operational efficiency, a broad term encompassing various aspects, is one of the key benefits insurers can expect from this migration.

It goes beyond saying that automation and data handling are critical components because data is vital for risk underwriting, actuarial modelling, and product personalisation.

The agility offered by cloud solutions enables insurers to respond swiftly to market needs and consumer preferences.

AI and automation

For the insurance industry, AI can significantly improve data consolidation across systems whilst enhancing compliance measures in areas such as anti-money laundering and risk management. It enhances ETL processes by analysing data structures, automating mapping, and addressing data quality issues.

As such, AI and machine learning stand as vital pillars in the industry. Recent surveys, including one targeting around 200 global insurance executives, revealed a burgeoning consensus: AI is not just a distant promise but an imminent reality. 

A significant 57% of respondents already perceive AI and machine learning as having a profound impact on their businesses, with a third anticipating the most substantial effects within the next five years.

This sets the stage for what Raphael Young, head of Financial Services APAC at EPAM Systems, aptly described as a “mega-trend” with transformative potential.

The focus on AI is not merely about keeping pace with technological advancements but is part of a broader, more holistic strategy.

“We are gearing ourselves up in multiple areas. First, we are focusing on our leadership team and people. Then, we are setting the right organisational structure, processes, and products for our go-to-market strategy,” Young explained to Insurance Asia in a separate interview.

/Raphael Young, head of Financial Services APAC at EPAM Systems

This comprehensive preparation underscores EPAM's commitment to integrating AI seamlessly into its service offerings and organisational framework.

For NTT DATA’s Kumar, data automation is often misunderstood in the insurance industry. Three common misconceptions include the expectation of rapid ROI, prioritising ROI over data quality, and over-centralisation of solutions.

Kumar stressed that data automation is a long-term commitment that requires sustained process improvements and refinement. Ensuring data quality is central to realising the value of data automation. 

Over-centralisation assumes a one-size-fits-all solution, which is rarely effective. Customisation, adoption, and flexibility, with incremental rollouts, prove more beneficial.

What good does it bring?

The practical applications of AI in the financial services sector are both diverse and impactful. Within insurance, for instance, AI's potential to revolutionise claims management is particularly noteworthy. 

“Efficient claims management directly impacts the bottom line by improving the claims ratio and underwriting profit,” EPAM System’s Young said.

By automating many of the traditionally manual processes involved in claims handling, generative AI can dramatically reduce costs. “For high-frequency claims, such as those in property and casualty, the cost could range from $10 to $50 per claim. With generative AI, we can reduce this cost to about $1 per claim,” he said.

This reduction is not merely a matter of cost savings but also efficiency and speed. Automation through AI leads to quicker turnaround times and continuous process enhancements. “When submitting a claim, mandatory processes, like greeting the customer, can be automated using generative AI,” said Young. 

This not only expedites the process but also ensures a higher degree of accuracy and consistency.

As AI becomes more integral to business operations, ethical and regulatory considerations cannot be overlooked.  The insurance and banking sectors, being highly regulated, necessitate strict compliance with local guidelines. “We must use ethical AI that complies with local regulatory authorities,” Young asserted.

This involves ensuring that processes, such as claims adjudication, are overseen by human handlers, with AI providing recommendations based on specific policy wordings and exclusions. Such measures are crucial in maintaining trust and accountability whilst leveraging AI’s capabilities.

Unique cases

Oracle’s Thomas highlights a notable transformation in Australia, where a customer achieved automated claim adjudication and settled claims in less than four seconds.

This example underscores the importance of speed to market and efficient claims processing. "Every aspect of the insurance industry benefits from the transition to the cloud," he said.

The regulatory landscape in Singapore poses additional challenges for insurers. Compliance with evolving regulations, data sovereignty, and client data handling are critical issues.

Thomas pointed out that Oracle provides comprehensive solutions across all layers of the IT ecosystem, from infrastructure to applications. “We are one of the few software companies that also provides infrastructure,” he noted.

Despite the advantages, cloud migration is not without risks. Security vulnerabilities, potential downtime, and data loss are primary concerns.

Customers often demand high availability, with some requiring 99.9% uptime. Oracle addresses these risks through robust protocols for regular security updates, patches, and disaster recovery.

“In Singapore, we opened a second data centre in July this year following the first one in 2021," Thomas revealed, noting how this ensures efficient failover handling and high availability commitments.

The journey towards cloud adoption and AI integration is fraught with potential pitfalls. Thomas identified the rapid pace of technological change and the need for a skilled workforce as critical factors.

“Scaling up resources to understand the evolving technology landscape requires strong partners,” he advised.

Outlook

The path forward for the insurance sector, as forecasted by EPAM Systems, hinges on overcoming the barriers posed by legacy systems. “Many insurers have core policy administration systems that have been in place for 20 to 30 years,” Young said.

Modernising these systems is essential for digital transformation, a sentiment echoed by a majority of surveyed insurance executives. EPAM aims to lead this transition, offering solutions that help clients navigate and surmount these challenges.

The insurance industry, traditionally slow-moving, is experiencing unprecedented changes. Thomas highlighted three key areas transforming the sector: the Internet of Things (IoT), blockchain technology, and machine learning (AI).

IoT is revolutionising risk assessment and personalisation in general insurance. Telematics and IoT technologies are changing product pricing and renewal processes.

Blockchain technology, though slowly adopted, is gradually being integrated into secure transactions, such as payments and claims processing.

AI and machine learning are the hottest areas, dramatically changing customer service, claims processing, underwriting, and personalisation. “Predictive analytics and data-driven decision-making are becoming vital,” Thomas affirmed.

In health insurance, AI is automating processes and aiding in diagnostics, improving underwriting and servicing. 

Meanwhile, NTT Data sees how the next five to ten years will witness substantial growth and innovation. Mainly, thanks to trends like increased insurance penetration, especially in health and life insurance, evolving regulatory frameworks, and significant investments in insurtech.

“In the short term, insurance companies are facing pressure due to adverse economic conditions, and increased claims payouts due to natural catastrophes. However, the economic situation has begun to stabilise in Europe and beyond. Insurance companies are very positive about growth in the emerging markets of Asia, and India in particular,” said Kumar.

The industry will see greater collaboration between insurers, regulators, governments, and technology companies. Generative AI will play a transformative role, generating exceptional insights from diverse data forms, although it will also introduce new risks.