In this piece, Manish Sood, Founder and CEO at Reltio, looks at how AI can turbo-charge data in so many ways.
Like banks, insurers are looking to implement the latest artificial intelligence (AI) tools to improve business processes, deliver better customer experiences, and turbo-drive smarter data analytics on understanding and pricing risks.
While insurers are no novices in their use of AI and machine learning to improve products, claims, risks management and prevention, the new generative AI technologies like Chat GPT promise a significant step change in how AI transforms how an insurer operates, interacts with customers and generates revenues and profits. The rewards look considerable. For example, McKinsey’s consultancy estimates that AI technologies could add up to $1.1 trillion in annual value for the global insurance industry.
Poor Data Management Holding Back AI Progress
However, the implementation of new game-changing technologies like Gen AI has exposed a persistent problem that AI cannot fix – fragmented, poor-quality data. The proliferation of poor-quality data means AI systems fall short of their promise because they rely on precise and reliable information to function correctly. As inaccurate data accumulates daily, the obstacles to how AI can proliferate and add value within an insurer will continue to accumulate, inhibiting the flow of potential benefits from AI adoption at the scale so many insurers would want.
To fuel technologies like AI and better serve customers, insurers need reliable, up-to-date data about customers, risks and so much more that flows seamlessly across all systems – a state known as data interoperability. Achieving truly unified, interoperable data platforms will allow institutions to realise the promise of data-driven innovations.
Despite the urgency of digital transformation and the ambition to be data-centric, many insurers lack the modern tools and know-how to implement this vision fully or consistently across all aspects of the business.
Antiquated systems and siloed operations continue to frustrate the integration of new technologies. The scarcity of in-house technical skills also hampers progress. Research by McKinsey shows only 30 percent of financial institutions have successfully completed a digital makeover.
Why have insurers not created the best digital foundations for AI adoption? These complex overhauls prove deceptively tricky for many insurers. Legacy systems still power many critical functions, limiting scalability and agility. Outdated architecture struggles to support omnichannel customer experiences, run much more granular risk analyses within key workflows or do complex regulatory reporting.
Data Deficiencies Abound
Quality data should drive any AI strategy, but insurers, like many large, established enterprises across most industries, have trouble harnessing their information. Critical
customer data often resides in disparate systems and often contains errors despite the best intentions of insurers.
With the sheer volumes of data financial institutions hold, it is no wonder most are lagging in their data unification efforts. A FI Works study notes that as much as a quarter of the data in the average financial institution’s customer information file/customer information is incorrect. All this points to the fact that they are not ready for AI. Research from FI Works indicates the average financial company accumulates a staggering 500 million data points for every $1 billion in assets. Still, critical customer and transactional records lie fractured across legacy systems and siloed databases.
This sector also shoulders stringent data regulations around security, privacy, and governance – locally and globally. Lapses leave sensitive customer data vulnerable to cybercriminals.
Rather than rip and replace complex legacy environments, insurers should first focus on making existing data usable through unification initiatives. A unified modern data layer will empower institutions to meet customer expectations, adopt innovative technologies, ensure compliance, and gain a competitive edge. Sturdy data unification forms the bedrock, enabling business responsiveness and digital success.
Modern Data Unification Tools are Changing the Game
Modern, cloud-based data unification and management tools such as entity resolution, Master Data Management (MDM), and pre-built innovative data products are at the forefront of the battle against data fragmentation. These tools simplify the integration and harmonisation of disparate data sources and accelerate the time to value for data initiatives, with some enterprises experiencing tangible benefits in as little as 90 days.
Adopting these cutting-edge technologies enables institutions to unlock new revenue potential by providing a more comprehensive and nuanced understanding of their customers. Enhanced automation and improved efficiencies are further benefits, as streamlined data processes reduce manual intervention and the scope for error. These technologies are also pivotal in enhancing fraud detection capabilities, safeguarding enterprise assets, and reinforcing customer trust.
While the journey toward becoming a fully data-driven insurer is fraught with data fragmentation and siloed information challenges, modern cloud-based data unification and management solutions provide a clear pathway to overcoming these obstacles. Insurers that embrace these technologies are optimising their data assets and positioning themselves to reap the benefits of improved decision-making, operational efficiency, and competitive advantage in an increasingly data-centric world.
The Path Forward: Clean-Connected Data
Rather than overhauling complex legacy IT environments, insurers should focus on making quality data available to feed their modern systems, including AI. With clean and unified data as a foundation, insurers unlock the true potential of technologies like AI/ML while avoiding the “garbage in, garbage out” trap. Trusted data is the critical building block of digital transformation.
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