By David Northmore, Vice President EMEA at MarkLogic
Transformation has swept across industries globally since the dawn of the digital age, with innovation being a key driver. ‘Going digital’ was revolutionary in insurance, with much of the manual work involved in creating and locating files being removed to allow more time for insurers to focus on customer service.
The amount of data being collected and stored today is creating demand for similar solutions through the likes of automation, machine learning and artificial intelligence. Legacy IT systems are beginning to buckle under the pressure of effectively storing and retrieving information from more complex datasets.
The adoption of new solutions has been intermittent across the insurance industry to date as there are still many barriers to effective roll-out. The need to embrace technology and analytics to help overcome challenges in insurance is clear, though the industry overall has been historically slower than other sectors to acknowledge this and act.
The primary driver for insurers is customer engagement for the purposes of loyalty and retention.There are always going to be challenges in this space as expectations fluctuate and the ‘average customer’ becomes harder to define, with customer experiences becoming more and more complex. This focus area for insurance has seen more competition in cost and value-added services, though the adoption of predictive analytics is a newer phenomenon that insurers have begun to explore.
An effort to develop and maintain a drive towards innovation in insurance is evident, but more often than not, insurers are falling down as they lack the agility to keep up with an ever changing landscape. Regulation is one key factor at play, with GDPR for example creating a need for the adoption of enhanced security features that can aid insurers in mastering and sharing data, as well as keeping customer data safe. This acts as an important area to be tackled by insurers in their effort to pursue customer engagement and loyalty as services in the space evolve.
Automation, Artificial Intelligence and Machine Learning
Automation can be supported by both machine learning and artificial intelligence, so long as high quality data is leveraged in its introduction and maintenance. By integrating machine learning into a central platform, insurers can automate ‘lower-value’ activities so more time can be spent building sophisticated algorithms for areas such as underwriting and customer engagement.
AI enables pattern recognition at scale and performs repetitive and mundane tasks with ease, meaning the workforce of claims processors and other insurance personnel can focus their efforts on more people-centric tasks. The problem is that legacy IT systems often result in the build up of huge silos of data, and as the years pass we often see that several layers of tech exist on top of one another among insurance organisations. These layers need to be stripped back and the data effectively organised into an operational data hub that provides a 360-degree view of all the data across the organisation, while remaining completely secure.
Increasing the chances of success
Many insurers have incorporated Master Data Management (MDM) systems into their day-to-day operations in order to support the drive to gain a holistic view of their data. However, all too often speed and accuracy pose problems with these MDM tools, which is where ‘smart mastering’ using a data hub platform can make a difference.
Smart mastering is the process that enables the effective organisation, protection and retrieval of datasets, meaning insurers can master data quickly and automatically. It involves taking all entity information and standardising it to improve quality and accuracy, thereby enabling the linking of data across various existing databases. An advantageous use case for this would be the linking of policyholder and transaction data across different policies to provide a complete, 360-degree view of customers.
Implementing this process successfully requires the underlying data architecture to be designed in a way that means data is not siloed across the organisation. In fact, when it comes to technologies such as AI and machine learning, this set-up is a prerequisite for insurers seeking to incorporate these solutions into their operations.
Modern data hubs are instrumental in supporting insurers as they embrace technology that seeks to automate and streamline processes. By quickly integrating vast quantities of data from across the business, these platforms generate quality data sets which can be used to feed machine learning algorithms and drive automation. They also provide insurers with the tools needed to adapt and demonstrate flexibility when tackling their customers’ ever changing needs and make headway on the path to digital transformation.
