Source – cio.economictimes.indiatimes.com
Insurance has become an extremely competitive market! While there are several players, customers are increasingly becoming aware about what they need. They are now seeking a more individualized experience and if you cannot provide it, rest assured they will find another company that does.
So, how do companies find ways to address the ever-increasing customer needs?
While the adoption of new technologies might have been slower than desired initially, the Indian insurance sector is certainly awakening to its benefits now. Several insurers are now deploying these processes to understand their customers better and for product innovation.
Of all these news processes, perhaps artificial intelligence and machine learning are proving to be the most potent!
New data sources like third-party databases, social media activity, internet of things, and more are providing a steady stream of information. Machine learning is helping insurers create opportunities by computing this vast stream of data for hidden insights.
Using complex algorithms, insurers can find patterns in the available data to establish predictive and descriptive data models. However, the most important point to note here is that machine learning can make sense of dissimilar datasets – whether partly structured, unstructured or structured.
An uninterrupted flow of information from multiple sources – click stream, social media interaction, audio clips and more – is processed real time, enabling the systems to incrementally adapt and self-create a continuous learning curve.
Customer is king!
Given that insurance vastly requires customer interface, machine learning has helped companies to stay on their toes. They can quickly and effectively recognise dynamic customer needs to provide them with simple and intuitive experiences. The analytics can predict the future trends and behaviour patterns of the customers.
Using advanced machine learning techniques to analyze the behavioural and socioeconomic activities of a prospective customer, some insurance companies are successfully deriving precise and individualized product recommendations.
In fact, many companies are working towards creating chat bots to enrich customer interaction and looking at automating the issuance process.
Merits of machine learning
While many machine learning algorithms are not completely novel, applying complex mathematical calculations to big data – over and over, faster and faster – automatically is a fairly new. As discussed above in detail, new technologies will only better the way insurers conduct business and serve their customers.
So, here are the key merits of machine learning to show why machine learning is crucial:
a) Superiority in advisory
Virtual advisors based on machine learning algorithms could potentially change insurance advisory by eliminating human bias and improve trust, thereby boosting customers’ confidence. While India may not yet be completely ready for the machines to take over, insurers can certainly use these virtual advisors for tackling routine tasks and allow their consulting team to handle the more constructive and socially interactive challenges.
b) Enhanced operational efficiency
Machine learning can help insurers become more efficient operationally by reducing turnaround time and improving productivity.
c) Digital Marketing automation:
Insurers can automate and optimize digital marketing expenses by implementing machine learning algorithms in multiple areas including identification of optimum bidding price for each keyword, re-targeting as per propensity to purchase, and nurturing leads through right content at the right time using the right medium.
d) Dynamic pricing:
Given the enormous data at one’s disposal, insurers can create a dynamic pricing model as per the socio-economic factors of a prospective customer. For instance, by analyzing data from wearable devices that monitor physical activity and heart rate, policies can be recommended with discounts.
e) Claims management and underwriting
Considering machine learning depends heavily on a repository of data, it will help create a deeper understanding of a customer’s risk profile. This will make the underwriting process much more effective and accurate. It will also significantly reduce the turnaround time for processing of claims, and help identify any fraudulent incidences on a case to case basis. Both these factors are crucial is ensuring loss prevention.