With complete and accurate information so key to reporting, compliance and business intelligence, it is more important than ever to ensure the integrity of insurance data through robust reconciliation processes.

Insurers generate and process a lot of data. And since the advent of automation, they’ve put in place an impressive array of systems to manage all that information.

But in an increasingly regulated industry, new solvency regimes and accounting standards like IFRS 17 and Long Duration Targeted Improvements to U.S. GAAP are driving firms around the world to adapt their technology landscapes to support new interactions – especially between actuarial and finance teams who will be working more closely together.

In the introduction or integration of new systems and processes, reconciliation is often an afterthought. And across increasingly complex insurance operations, with their high volumes of claims, policies and premiums, a haphazard approach to data validation leaves you wide open to significant risks.

Critically, data undergoes many transformations as it moves through different insurance systems, supporting the processing of policies, premiums and claims, and feeding risk calculations and regulatory and financial reports.

Without careful monitoring of the operational life cycle, errors can easily occur to compromise data integrity and cause considerable increases in:

1. Financial risk
   Regulation is driving insurers to apply a consistent accounting framework for all insurance contracts and provide a more transparent view of their financial position and risk. With knock-on implications for the bottom line, accurate, auditable data is therefore a must.
2. Reputational risk
   Insurers must also provide consistent information about current and future profits from insurance contracts. At a group level, results will be scrutinized closely by shareholders. So, any misvalued contracts or negative audit findings will carry reputational as well as financial risks.
3. Operational risk
   Without efficient and reliable validation processes, reporting operations are more subject to delays and prone to error. And any additional work to correct audits comes at a cost.

To mitigate these risks and provide total confidence in reporting, disclosures and business decision-making, you need an all-encompassing, systematic approach to data integrity.

Ultimately, that means creating an automated process in which all data is cleansed, transformed and reconciled as it flows through your company – from administration systems to risk models and calculations, and onward to the general ledger.

As well as reducing risk, automating data validation in this comprehensive way will save your organization time on manual processing and free up teams to focus on activities where they can really add value.

What better reasons to make data integrity a priority?