In our previous article, Transforming Indian Insurance Sector with Data Analytics Edge, we explored how the Indian insurance sector can transform its customer acquisition, customer retention and share of customer’s wallet by adopting advanced, actionable analytics. In this article, we will zero-in on one of the most competitive segments of the insurance sector – the Life Insurance segment.
Need for Advanced Analytics in Life Insurance
Traditional Analytics Cannot Tackle Complex Variables Like ‘Trust’ and ‘Confidence’
Globally, the Indian life insurance sector is one of the largest, with about 360 mn1 policies that are expected to grow at an annual average of 12-15%2 over the next five years. Since the country’s population still remains largely uninsured, competition is severe, products are by and large similar, and high growth is constantly tempered by profitability challenges. One key aspect that affects how the life insurance segment functions is this – when people buy insurance, they buy a promise, or a future guarantee. Unlike other commodities, insurance cannot be consumed immediately by the customer. This leads to a key characteristic of the sector – customer’s trust is the most important factor in securing growth and profitability.
We were recently in conversation with life insurance veteran Pinaki Mullick, who has spent over fifteen years in this industry and held the position of a national head in his last stint with a leading insurance provider in the country. Talking about the importance of gaining customer’s trust, he says, “If you look at the graph of the top insurance companies in India during the last five or seven years, you will see that some of the top-ranked companies have slided down in terms of performance, while there are others who have climbed upwards and onwards. What has made this possible? The stark contrast is between those who ignored customer’s trust, and those who made it their center-piece. And how do you build trust? By knowing your customers – that’s exactly where analytics comes in.”
Mullick’s words are echoed in some of the recent findings on the sector. According to a recent study3 conducted on 1,900 insured and uninsured people based in Metro and Tier 1 cities, less than 50% of those with life insurance are very confident that they have purchased the right policy, or that their policy is very valuable. Strangely enough, there is a further drop in the level of confidence for customers who purchased the policy directly from an insurance company or through a bank, the most preferred mode of purchase being through agents. Moreover, 20% of the respondents said that they don’t have any trust in the way insurance is sold, or the information they’re provided.
Traditional Analytics Depicts ‘What’ and ‘When’, Not ‘Why’ and ‘How’
Let’s face it – in the financial year 2016, the life insurance industry exhibited a persistency of 61% on an average in the 13th month4. What does this mean? In simple terms, this means that 1 year post sale, only 61 out of every 100 policies were renewed. And the woes don’t end there – 5 years post sale, only 33.33%5 of the policies continue to exist, the rest being discontinued prematurely along the way. What does this mean for profitability of the insurer? – Disaster.
The customer acquisition and retention costs are a huge overhaul burden on the insurer’s books. When a policy is discontinued prematurely, the costs to open it in the first place cannot be recovered, giving a severe blow to profitability. Madhuri Deb, another industry veteran with over a decade of leadership experience in sales and policy life-cycle management, strikes a chord as she tells us about the vicious cycle of policy lapses. “In life insurance, the field-level salesforce – the pillars of the industry – are not empowered with tools and insights to make informed judgments in everyday decision making. This is at the root of insurance providers not being able to use data and analytics to business advantage. ”
“Besides,” she adds, “the picture is more complicated than it seems. We deal with customers and field-staff who are comfortable in vernacular language, but are intimidated by English – the language used in most of the analytics softwares. In the absence of predictive analysis at the grass-root level, policy lapses are a vicious cycle of customer dissatisfaction, lack of intelligence on reason behind lapses, and policies foregone prematurely. Traditional analytics is not actionable, and does not incorporate human component and textual / linguistic aspects in a contextualized manner.”
Traditional Analytics Cannot Keep Up with Evolving Life Insurance Sector
In the last couple of years alone, with IRDA’s (Insurance Regulatory and Development Authority) blessings, phenomenal changes are all set to transform the way life insurance works in India. To begin with, the emergence of PoS (Point of Sale)-based insurance products will be a game changer in making analytics indispensable. From penetration levels hovering around 4.60% in life insurance in 2009 (post the global downturn in 2008), we have hit a new low of 2.72% as of March, 20166, prompting IRDA to introduce innovative channels like PoS. PoS Saral Nivesh, the country’s first PoS life insurance product, was launched recently by Edelweiss Tokio Life Insurance. Spurred by cutting-edge analytics, the product is expected to complete a policy purchase transaction within 20 minutes, along with verifications7.
Another major change comes in the form of IRDA mandating an increase in premiums through a series of recent circulars8, forcing insurers to revisit their assumptions of growth, profitability and pricing. The third most anticipated regulatory change is the much debated possibility of IRDA introducing life insurance portability9 – under which unhappy customers of an insurance player can shift to a similar product of a competing player instead of closing the policy. With these changes, competition and battle for survival are all set to get tougher than ever!
Mullick offers a simple yet intuitive way-forward. “Instead of losing sleep over the intricacies, let’s learn lessons from our foreign counterparts, who have spent centuries in this domain, while we have barely completed a couple of decades. They perceive insurance analytics as a two-fold feedback loop – one from the happy customers, and one from the unhappy or ‘orphan’ customers. They set up elaborate processes capturing actionable analytics in each of these segments, and ensure that each decision by each employee is data-oriented and analytics-driven. Many Indian insurers have partnered with foreign players. The ones who have derived lessons from their foreign counterparts and applied the same in the Indian context, have made it to the top.”
Mullick’s thoughts are resonated in this article by Analytics India Magazine where a senior official from a life insurance provider which is a joint venture between an Indian and a foreign player, speaks on the analytics trends in the industry and the challenges that come bundled with it.
So, what are the various kinds of advanced analytics that can catapult the life insurance sector to the next level?
The pillars will be formed by the four key components – Descriptive Analytics, Predictive Analytics, Diagnostic Analytics and Prescriptive Analytics. The technology of the future will be a composite of whole-brain analytics – that incorporates both rational and emotional components in capturing and analyzing information. The rise of IoT (Internet of Things), Robotics and AI (Artificial Intelligence) along-with advanced methodologies such as text mining and deep learning will be the mainstay of whole-brain analytics in Indian life insurance sector. In our next article, we will share with you some of the innovative solutions that FORMCEPT has conceptualized for India’s evolving life-insurance sector.