By Viren Patel, Financial Services Industry Strategist at Workday
It’s been a year since IFRS 17 was introduced for UK insurance firms. Has it enabled a more data-driven way of working for businesses?
For some insurance firms, IFRS 17 has been another painful turn of the screw. Its data requirements are complex and detailed, with an emphasis on ongoing data collection and the interrogation of historical data.
The new code calls for robust, standardised reporting of insurance data, from policy terms and premiums to claims data and more. Compiling the information from disparate business functions will be an uphill battle for firms already struggling with data. But for those with the structures in place to compile and act on their business unit information, there is an opportunity to turn a hefty compliance requirement into a new strategic asset.
Combining business and operational data and sharing it transparently will build investor confidence, inform smarter and more timely decisions on risk, and streamline reporting processes in the future.
IFRS as opportunity
RSA Insurance Group’s latest Broker Pulse Survey[1] showed that insurers are feeling the pinch. Over three-quarters of respondents (79%) said the cost of doing business remains a ‘significant concern’ as more customers drop out or change cover mid-contract.
Uncertainty in insurance contracts is a challenge that regulation like IFRS 17 seeks to solve with data, and it’s up to firms to make the most of the opportunity this information presents. Just as carbon emission reporting helps identify new areas for energy efficiency improvements, IFRS 17 data can and should be used across the business to drive better decision-making.
Turning mandatory reporting into meaningful insight
If analysed correctly, insurance contract data offers valuable insights into customer preferences, claims patterns, and market trends. Putting it to work will help firms adapt to shifting market conditions and provide better products and services that fit customer needs.
Bringing this broad operational data into firms’ decision-making will help to illuminate how business events affect profitability and vice-versa. An intelligent data backbone links individual transactions to top-level decisions, so they know they’re doing the best for their customers and business.
As economic uncertainty and market disruption continue, insight and agility have become business imperatives. Insurers need to plan and forecast multiple scenarios amid volatile real-world conditions continuously.
Detailed, real-time data will help insurers pivot confidently, prepare for potential roadblocks, and recognise and seize opportunities as they arise.
If insurance firms are meeting the requirements of IFRS 17, it means they have the data. Now they need the framework to ensure this data is adding value across their business.
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Escaping uncertainty: Using data to mitigate risks
The most obvious first place to put this data to work is in risk mitigation. It’s pertinent, necessary and made easier by the data types IFRS 17 requires. Using policy and claims data to build predictive analytics models will help guide firms around potential risks and emerging opportunities.
In late 2021, McKinsey reported that even the leading insurers could see loss ratios improve by three to five points with digitised underwriting. Data-driven risk modelling doesn’t just enhance the underwriting process through speed and consistency – it can reduce exposure across accounts.
This benefit becomes even more powerful when you consider the specificity and consistency of the data IFRS asks for. Data-driven models can be targeted to consider individual characteristics or risks of individual clients and help insurers tailor support and services to each of their needs.
Ongoing data collection also opens the door to continuous monitoring and real-time decisioning models. With solid data, insurers can lay the foundation for slicker operations and improved competitiveness.
The long-term value of data-led decision making
Risk mitigation is just the first (and most obvious) application of data. We’re already seeing other powerful ways in which insurers are using data across their operations.
● Personalised pricing: With robust data analytics collected routinely, insurers are creating new pricing models that reflect individual risks and can allow for premium rates. Neobanks and fintech were quick to jump on ‘personalised service’, but the requirements of IFRS 17 should be a pull – not a push – towards better customer service.
● Innovative insurance products: Insurers are using their real-time data collection to create policies and products that customers love. For example, new health insurance models are offering reductions in rates to people with fitness trackers. In automotive insurance, subscription-based ‘pay-per-mile’ plans recommend ways for drivers to avoid accident blackspots.
● Fraud detection and prevention: Data analytics is another powerful tool in the ongoing fight against insurance fraud. Text and behavioural analysis on claims have the potential to spot fraud faster and more proactively. Real-time data can also support rule-based detection, in which insurers can set up triggers and alerts to safeguard against fraud as soon as it is spotted.
The race is on
IFRS 17 should be seen as the trigger in the race for better insurance operations. If insurance firms decide to maximise this opportunity, the robust data requirements can filter through to powerful new insights across their operations. Building an intelligent data foundation is the first step out of uncertainty and towards long-term success.