While the insurance industry is a vital component of the global financial services landscape, its path towards embracing automated solutions has been a journey marked by surprising inaction, setting it apart from other sectors. The primary culprit behind this slow progress lies in mismanaged data. However, the tides are turning as market participants recognise the impact technology can have on their business and realise the costs of further delays.
In this article, we delve into the main obstacles that have hindered the use of automation in insurance, the remarkable shifts underway, and how automation is rewriting the rules in one of the oldest sectors of financial services.
Data is the lifeblood
Anyone following the technological transformation of financial services has no doubt heard the expression ‘data is the lifeblood of financial services’ or some equally familiar cliché. While this phrase might sound overused, it remains relevant to the challenges currently faced by many companies, particularly those in the insurance sector. While data is widely recognised as the essential lifeblood, the aspect of maintaining its quality is too often overlooked or at least not correctly prioritised at the right levels within organisations.
Where the market is adapting
Data quality is essential for the successful integration of automation in insurance because it underpins the reliability, efficiency, compliance, and overall effectiveness of automated processes. Insurers know that without a focus on improving data quality and transforming their operating model (including a robust data quality & controls framework), full automation across their operations will always be out of reach.
One notable example of this recent commitment to improving data quality in the industry is evident in Lloyds Blueprint Two programme, which has been in the works for the past few years. The project recognises the urgent need to move away from legacy manual processes and towards a faster, more efficient, and automated digital process.
A vital component of this transformation at Lloyds is the Core Data Record (CDR), a central repository designed to collect standardised, quality data, harmonising disparate data sources. This consolidation underscores not only a big step towards improving data quality but also that it has the potential to be a source of substantial revenue for the industry, making it faster and cheaper to do business at Lloyds
Regulatory reporting – driver of change
Every insurance company irrespective of size or speciality holds both assets, liabilities, and reporting on these is at the centre of their business. Therefore, keeping pace with evolving regulatory requirements is essential. Data quality is intrinsically linked to regulatory reporting, and getting this wrong can have serious financial and reputational implications. We are seeing this more and more in the industry, where new regulatory initiatives such as T+1 and the upcoming EMIR refit are putting even greater demands on companies, reducing reporting timelines, and therefore increasing the focus on improving data quality controls and automation.
These upcoming reporting deadlines need to be considered by insurers in their quest to improve data quality controls, automate operations, and meet compliance standards. Another recent industry example was the move this year to IFRS 17, the new global accounting standard. This new reporting standard has had a major impact on how insurance companies report their financials, often requiring a transformation in operating models driven by reduced reporting timelines and an increased need for data granularity. This enhanced level of granularity, again, drives a shift towards more efficient, automated data management, and controls processes.
As the insurance industry adapts to these changes, the quality and accuracy of data used in these processes are hot topics of discussion, with a direct bearing on compliance and operational efficiency.
Practical examples: Inter-system reconciliation & Bordereaux management
Inter-system reconciliation is a critical process in insurance, ensuring that data across various systems aligns accurately. These data quality controls and processes guarantee the precision and consistency of financial records and policy information, reducing the risk of errors, fraud, and regulatory non-compliance. For example, checking and reconciling data is an integral part of any claim’s payments process.
Automating reconciliation processes fosters operational efficiency and lowers operational costs. High-quality data, which is the result of a robust reconciliation framework, not only enhances decision-making, but also improves investor confidence and the overall customer experience.
Similarly, high-quality bordereaux data streamlines operations, minimises errors, and supports fraud detection. Moreover, it facilitates valuable insights for optimising underwriting and portfolio performance, ultimately influencing reinsurance negotiations and business sustainability. In an era where data-driven decisions are the norm, this stands as a cornerstone of insurance operations, safeguarding financial stability, regulatory adherence, and customer trust.
Looking ahead
Ensuring data quality, reliability, and modern data architecture are amongst the main roadblocks for companies looking to scale, including the top performers. Data quality is not only a technical concern, but also strategically crucial for the insurance industry. It serves as the foundation for the sector’s capacity to adapt to new regulatory standards, streamline and automate operational processes, and most importantly, maintain its hard-earned reputation and financial integrity. As insurers navigate the sea of data and embrace modernisation, they must consistently place data quality at the forefront of their strategic agenda. In this data-driven era, it’s not just an edge – it’s your lifeline to success as it can influence decision-making, improve risk assessments, and enhance the end customer experience, right at the heart of it all.
About the author: Jesse Power is Insurance Director at data automation company, Duco. He graduated from University College Dublin with a degree in Actuarial Science and Finance. He began his professional career at Millman as an Actuarial Consultant before moving to Moody’s Analytics as Director and Sales Manager for Insurance Solutions across Europe and Africa. Early last year, Jesse joined Duco and was tasked with further advancing the insurance offering and strategy and collaborating with existing clients including WTW. He is a Fellow of the Institute of Actuaries (FIA) and a Chartered Enterprise Risk Actuary (CERA).
