Why reinsurance data is your biggest asset

Data as an asset, or as some call it, business analytics, has already proven its worth in the business-to-consumer space. It isn’t just the likes of Google and other sophisticated retail operations that benefit from it; it’s working well for direct insurers too.

It can generate opportunities from the knowledge inherent in the organisation’s data in a variety of ways. Whether this comes through nipping customers’ concerns in the bud—in an effort to maintain loyalty or reduce defections—or to act on customer feedback to more effectively target new products; in essence, it gives companies of all sizes the chance to use business intelligence in order to avoid risks and embrace opportunities.

Reinsurers have historically focused on the merits of a single transaction, rather than the collective knowledge of a host of transactions. Reinsurers have also managed far lower volumes of data than direct insurers and this has led to an overriding emphasis upon automation to reduce costs. In the rush to achieve this, data analysis, as a forward-looking indicator of past and present performance, has been overlooked. Instead, a culture of information silos has prevailed in reinsurance, going against the move towards shared information and transparency.

However, without a comprehensive way to analyse data, reinsurers are likely to face greater challenges, particularly as they try to grow their business against the backdrop of difficult economic and trading conditions. Failing to embrace this technology, which gives them the best way to review, interpret and share data, could see them miss out on a timely and cost-effective way to adopt better risk selection and to manage risk more effectively.

"The crux of moving to a foolproof system is being able to analyse the vast quantities of so-called 'big data' at the macro level and to get at the information that will  make a difference. Mining this data can positively impact the enterprise, not only in more accurate decision-making, but also in identifying and avoiding risk."

Today, good governance and effective risk management are essential selection criterion; so using data analytics to bring these about makes infinite sense. In fact, without it, it’s fair to say that reinsurers are reducing their chances to compete, particularly as new market entrants have been quick to realise what data analytics has to offer and will typically be unencumbered by the legacy issues of their more established rivals. It’s no surprise to learn therefore that CSC is currently providing its analytics offering to reinsurers in Brazil, where privatisation of the market is enabling operational processes to be radically altered.

A recent report estimated that up to 80 percent of an organisation’s data is never used. That’s an awfully high margin for error, especially if redundant data is allowed to skew the real picture of what’s happening inside the enterprise.

As stressed earlier, the issue is not about sourcing data but interpreting it. There’s no doubt that all reinsurers use sophisticated techniques to retrieve and analyse data. From cat modelling and claims reserving, to performance measurement and forecasting, a plethora of automated processes have emerged, particularly recognising—in the wake of recent disasters around the globe—that more accurate informationgathering techniques were needed.

Whilst these systems might be a given—often because the systems used are many and varied, and at different stages of evolution— they nevertheless need harmonising, particularly to draw beneficial conclusions to determine risk, the likelihood of claims and so on.

Undoubtedly, reinsurers will find ways to use the reports generated from these systems to their best advantage, but imagine the amount of time and effort that is spent interpreting these different data sets? Surely this time could be better spent on more productive revenuegenerating opportunities for the business?

What’s more, a percentage of data might not only be failing to add value—by being inaccurate or out of date—it could also lead to misreading those signals vital to the health of the business. It’s possible too that, should a very rapid response be required, the enterprise—or anyone else in the supply chain—will get a snapshot that’s out of touch, especially if real-time data has not been gathered in the first place.

The crux of moving to a foolproof system is being able to analyse the vast quantities of so-called ‘big data’ at the macro level and to get at the information that will make a difference. Mining this data can positively impact the enterprise, not only in more accurate decision-making, but also in identifying and avoiding risk, and garner an accurate overview of business trends, such as comparing and contrasting performance across geographical markets.

Without this overview, or ‘executive management dashboard’ as we like to call it, reinsurers will lose out, plain and simple. Not making the right decisions could lead to extreme financial losses, for example, when changes on the horizon, such as a move to a soft market from a hard one, fail to be anticipated.

Information needs to be leveraged, particularly as accurate information is one of the most effective tools for negotiation. Reinsurers and insurers will have more credible, fact-based information that can be leveraged in discussions with brokers to secure better pricing, moving away from the temptation to work on assumptions. This will help both insurer and reinsurer—ceded and assumed—assess where they want to spread their risk.

Reinsurers need to decide what data is important. However, whilst a plethora of different systems to manage different functions is common; the capacity to cast across all of this information and extract the detail that could fundamentally change the direction of the business is where the intelligence of analytics comes into play.

In order for reinsurers to identify relevant trends in the business, they need to have the full view in order to monitor the enterprise. The beauty of the executive management dashboard is its ease of use. Anyone can interpret its information, particularly as there’s so many ways in which the information can be presented.

Once a dashboard is built, it can be developed to integrate tools and graphs. Key performance indicators can be tracked at a glance and underlying information can be drilled into to find root causes of issues, such as underperformance or unexpected trends. Typical indices for reinsurers include:

• IBNR (incurred but not reported) reserve, expenses and projections

• LAE (loss adjustment expense) allocated (ALAE) and unallocated (ULAE) loss reserves Loss cost trends

• Litigation expense

• Reinsurance provision—unauthorised, non-slow pay, slow pay authorised

• Open and closed claims information • Growing profitable business, supporting strategies for divestitures

• Aggregate excess of loss reinsurance (or tracking of stop loss reinsurance)

• Rating information, reinsurance programme information (facultative cover, treaties, proportional and non-proportional)

• Reinsurance recover data, including aged debt

• Adverse loss development

• Cat loss monitoring (all losses related to a single event can be viewed).

Overall, there’s a host of ways to dissect the information, depending on what’s being monitored and which parties you are disseminating the information to; the possibilities are endless.

For instance, underperforming areas of the business can be identified, as well as trends, such as when there will be a turn in the market and how the impact of increased premiums will impact your business, especially considering current pressures in the market.

Put simply, data analytics represents a sophisticated way to feed data throughout the enterprise and to other parties in the supply chain. Having the tools to glean a definitive, accurate overview of your key business drivers is surely the most powerful basis for well-informed decision-making.

Moving to this new model is a far cry from what happened in the past. It took days to complete reports and there was always the potential for this data to be out of date already or for the information to be inaccurate or duplicated in the first place.

Now this information reporting is dynamic. Models can be built today, in real time, and it’s clear to see how all areas of the business are performing, how customer segments are responding to particular products and services, and how the corresponding claims information aligns with this. This can give any senior executive the ability to see, at a glance, how the business is performing against targets, against the previous and what is required to achieve certain goals and to build on the performance scorecard. Analytics can emerge as the deciding factor in an organisation’s efforts to improve business performance and achieve its growth plans.

Michael Mackewich is lead principal, reinsurance, for the US, Bermuda and the Caribbean, at CSC. He can be contacted at: mmackewich@csc.com

Michael Cook is an associate partner in the consulting practice, financial services for Europe, the Middle East and Africa, at CSC. He can be contacted at: mcook41@csc.com