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Data Remediation: Fixing common data problems in insurance

  • Publish Date: Posted about 1 month ago
  • Author:by Kevin Hall and Kumar Jyoti

​In the intricate world of specialist insurance, a significant challenge looms at the board level: a data problem leading to capital being tied up on the balance sheet, compounded by auditors applying capital charges. This dilemma transcends mere operational hurdles; it intersects crucial domains such as finance and underwriting. Before delving deeper into this issue, let's dissect the current landscape to understand its complexities and implications more thoroughly.

Digital transformations in the insurance sector have typically focused on the development of platforms that drive the user experience and improve data flows, but the actual quality of data being surfaced for underwriting purposes is often overlooked.

The multiple parties involved in the insurance value chain will inevitably have varying approaches to data formatting. Added to this, inconsistencies in the timing of data flows from intermediaries to insurers can make the process of stitching data together for policy administration more complex.

This can create significant accounting issues for both primary insurance carriers, whether writing direct or delegated authority business, and treaty reinsurers, both proportional and non-proportional.

As a result specialist (re)insurers often face challenges relating to aged debt and unallocated cash, both of which can create obstacles to determining a precise financial position for reporting. These are a direct result of discrepancies in written premiums which, in turn, arise from data quality issues in (re)insurers’ PAS and underwriting systems.

While policy data is owned by the underwriting business, it sits in often complex technical architecture and is reported via the finance function, making it unclear who in the organisation should be responsible for its remediation.

This often results in the ongoing conundrum for insurance companies - who actually owns the data?

Problems with data quality can be exacerbated among carriers who make extensive use of offshore vendors to manage insurance admin processes and credit control functions.

The knowledge to unpick more challenging policies is very much a specialist onshore requirement. In an established business, or those in growth mode, an increasing rump of difficult policies annually can remain unresolved.

Is technology the answer?

Technology can assist with data capture and enabling access to that data along the whole value chain, and processes can help limit growth in poor quality data, but essentially the data coming from intermediaries will be the same.

In the absence of a market-wide data standard, the legacy technological environment in the insurance sector continues to be a major cause of data problems in the market, and for clients looking to tackle aged debt and unallocated cash there isn’t a pure technology fix.

With a consolidating market, attempts to drive data standardisation across the wider insurance ecosystem become more challenging. If the acquirer in an M&A transaction has higher data standards than the target company, they may attempt to raise standards across the board. However, introducing data conformity across the merged entity may prove more challenging than it first appears, and could introduce more problems than it solves.

An immediate solution for data quality issues, and one that would work in tandem with any future technological fix, is to implement a data operations Centre of Excellence (COE), along the lines of the ‘two in a box’ model for IT outsourcing (especially for organisations that are highly outsourced).

The COE comprises a core team of highly skilled people who understand the market flow end-to-end and can enable businesses to unpick complex policies.

This end-to-end expertise of the market enables them to work out what’s happened to a given policy in terms of endorsements, coverage layers, and claims made and settled, and then put the policy back together again and outline the remediation steps needed to resolve the problem.

Remediators Assemble

One approach we always recommend is a specialist data assessment team, with each member bringing their own particular set of skills to bear on clients’ data remediation challenges. “Marvel Avengers” for insurance data!

With in-depth underwriting knowledge, extensive understanding of back-end PAS systems, and MI skills to deliver process enhancements and cost savings this can ultimately deliver improved financial results.

The MI skills are particularly important because they enable the team to collect its own data, unpick problematic accounts from credit control through to underwriting and policy changes, re-build that end-to-end reporting journey with the help of the analysts’ deep market knowledge, and dramatically improve reporting.

This approach enables the work to be done at a highly detailed level, assess the organisational landscape, learn about the company’s data, understand the problem, propose solutions, and then deliver working solutions.

Lasting benefits

While the COE approach can only ever be a temporary fix, engaging a specialist team to tackle data remediation in your organisation can have several longer-lasting benefits.

Holding aged debt is a common problem for insurance companies. A very high value of aged debt held on the book over a long period of time necessitates the addition of significant capital charges if the company is to prove to auditors that it practises good governance.

In engagements with insurance clients, we have managed to significantly reduce aged debt provisions for prior year accounts through effective data remediation, delivering a direct financial benefit for businesses in terms of cost of capital savings alone.

With the inevitable churn of talent in any insurance company, if the aged debt issue remains unresolved when people with knowledge of the relevant transactions move on, some of that account history will be lost.

Consequently, there is a sweet spot in this natural cycle of complexity for engaging a specialist team to come in and solve the problem. By reducing levels of aged debt, and implementing operational processes that enable the organisation to be more efficient in future, they can prevent the problem from worsening – and before key people move on.

Where organisations have been hollowed out operationally, and key talent is focused on the highest value-adding activities, it can be easy to lose sight of the fact that controlling aged debt is in itself a high value-add. The cost of engaging a COE versus the capital savings on aged debt delivers a massive return on investment.

Resolving the issue of excess cash due to misallocation of cash between policies, profit commission issues, and missing documentation is a more difficult problem to solve, as the typical pattern involves the accumulation of smaller balances spread across a large number of policies. If you picture a scenario where the policy has eight layers spanning five years, that’s already 40 different accounting combinations, which, if it's a bureau policy, can be equivalent to thousands of signings..

Longer term, keeping excess cash to a minimum involves greater ownership of data on the underwriting side, with more controls and checks on the money coming in from intermediaries to prevent it filtering into the wrong layer year of account.

Improving reporting and processes

Through data remediation exercises we have seen improved financial reporting, leading to more favourable audit outcomes and identifying areas of training for offshore providers.

Sustainable process improvement involves working with BAU teams to improve the quality of DPMs used by offshore teams, across every line of business. By including specific business scenarios, you can reduce the incidence of data entry errors which result in reporting inaccuracies, and create those issues with aged debt and excess cash.

Through analysis and MI, trends and patterns of issues in underwriting can be identified. Those patterns are then reflected in the DPMs which are updated to avoid the same issues occurring in future.

Longer-term, the greatest benefit to insurers is to establish a well-oiled utility model for data remediation that resolves issues more quickly every time it is deployed.

If you would like to discuss this in more detail, please reach out to