The global pandemic has generated a range of market challenges within the insurance industry, and one which continues to build in prominence is that of mergers and acquisition (M&A). M&A activity remained high in 2020 compared to 2019 and now, as insurers grapple with the fallout from Covid-19 and re-assess the market conditions, all signs point to a continued surge in M&A this year – potentially reaching the highest level in almost seven years.
Many insurers consider M&A activity to be one of the best ways to pursue innovation, find synergies and advance their digital transformation initiatives. The takeover of RSA (and break-up) by Intact & Trygg A/S, Allstate’s acquisition of National General (their largest ever), Hollards purchase of Comminsure from Commonwealth Bank and the expected merger between AON & WTW are among the most prominent deals. But recently, countless new M&A deals of varying sizes have been hitting the headlines.
The Impact of M&A on Data
Making mergers and acquisitions successful requires smart strategic planning and excellent execution in a range of functional areas – from legal to regulatory affairs, finance to IT, human resources to operations. But researchers and industry analysts have found that many mergers and acquisitions fail or flounder in delivering maximum value due to problems with integrating disparate IT systems and data. This can make it difficult for operational teams to serve customers effectively, often leading to friction in the customer experience as well as within portfolio risk management.
However, with M&A complexity also comes opportunity. It would be a mistake to think about M&A-related data integration and consolidation challenges only as a pitfall to be avoided. While there are risks for getting it wrong, there also tremendous opportunities to use M&A as a catalyst for digital transformation, to gain a better understanding of customers and improve competitive advantage.
Ultimately, an insurer gains access to richer and broader data which should inform customer experience and relationship management, and application, underwriting and claims processes in both personal and commercial lines. Not leveraging this data insight would be a missed opportunity.
In most cases, insurance IT teams turn to large scale data migration projects and/or master data management (MDM) solutions as key tools for managing their data consolidation challenges after any M&A. But traditional solutions in this space are not built to scale for the high volumes of distributed, disparate data that is generated by various applications and external sources within M&A.
Traditional MDM data matching doesn’t work well with siloed data sources, leading to data duplication and inaccurate record linking. And most insurance companies have significant data silo challenges, even before any M&A, based on historic silos between lines of business and business units. Traditional MDM often misses connections and context, results in decision-making inaccuracy, and leaves business value on the table. In short, an ineffective MDM solution can negatively impact everything from customer experience to operational performance.
Driving a Competitive Edge from Richer Data Insights
An alternative, and in fact a complement, to traditional MDM is Contextual MDM, which enables you to:
Connect siloed data and add greater context using advanced Entity Resolution to create a joined-up view of all data assets across each business unit around your customers, third parties and supply chain (people, businesses, addresses and other entities) from a range of data points across internal and external sources.
Understand relationships and uncover hidden connections using Dynamic Graph Analytics to link the people and organizations you do business with in order to generate richer insights into related parties and hierarchies. These can be analyzed and identified automatically through data.
Maintain “the golden record” – records with the highest level of accuracy and trustworthiness that can inform your operational process and customer experience in everything from customer portfolio management, marketing, renewal and retention modelling to application pre-population – providing a richer view of all relevant attributes across those linked records.
Update data dynamically as each data point/record changes, not just in a periodic/offline batch mode, so you can maintain up-to-date customer views and trigger processes in response to any data changes.
Enable your data team to build a solid foundation for effective data and analytics across the enterprise by accelerating and simplifying data and system migration through data fusion, predictive entity and network-based AI, Visualization and unified Exploration. This dramatically reduces the time and effort your data scientists spend on preparing and maintaining data and dealing with data quality issues, enabling them to focus more on high value work – for predictive analysis, decision intelligence and process automation.
Accelerating your Data Strategy
Many insurers are demonstrating a sense of urgency about both M&A activity and their digital innovation efforts. In a recent global survey of insurance executives, 79% reported that the coronavirus pandemic exposed shortcomings in their company’s digital capabilities and transformation plans. In response, 95% of those surveyed said they are accelerating their digital transformation to maintain resilience with better use of cloud computing (59%) and data analytics (49%).
Establishing a strategic approach to data integration is key to tackling the steady rise of M&A in insurance, and accelerating your digital transformation journey.
ARTICLE WRITTEN BY: ALEX JOHNSON
Alex Johnson is the Head of Insurance Solutions of Quantexa.
Quantexa is part of our Batch 6 Fintech program in Singapore.
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