By Vivek Gujral
Insurance is often referred to as the dark horse of financial services, most likely because until very recently, it has struggled to get away from its perception as a non-performing item on the family or corporate expenses. This is changing.
There is more competition, and if our legislators find a way, there will be more. The mix of insurance products being sold has diversifiedand expanded from vanilla flavored Life and Motor to include curious and excitingthings such as investments, mobile phones, travel, and health. Usage based, 'micro'insurance is likely on its way as well. So yes, the day is coming when the truck driver who cuts you off on NH-8 will pay his insurance carrier dearly for that particular lapse in judgment, thanks to the black box transmitting under his seat.
These products and innovation simply involved customer-touch-points and high product complexity, thereby allowing carriers to distinguish themselves from the herd.
What this mandates for insurance companies are software systems that enable low-cost operating models, high-quality customer-service, e-commerce, as well as product and process adaptability. Finally, a dash of machine learning helps.
A low-cost operating model implies software-based automation of the entire value-chain. It is not as if insurance companies are not automated. Instead, they feature islands of automation, with manual links between these islands.
Unsurprisingly, these links are where the bulk of the avoidable cost, friction and service quality degradation occurs. Designing and implementing business processes where the default is full-automation and only exceptions are handled manually is what insurance carriers need to do. This is not easy. The major issue isn't software - instead it is managing cultural and organizational change. This issue is exacerbated because few carriers realize that their line-managers, while talented practitioners of insurance, are not usually trained, or qualified to manage change.
Clearly, it is impossible and cost-prohibitive to automate fully. So, a low-cost operating model also needs software to measure and monitor non-automated processes. This provides oversight on human decisions, enabling management to ensure uniformity in decision-making even when processes are manual. In particular, this has a huge impact both on risk underwriting and claims adjudication - both of which have long been bastions of 'insurance as an art'.
It has taken me multiple weeks to get a quote for business insurance. On occasion, my policy has expired, and my carrier has not bothered renewing, thereby walking away from recurring revenue. Some outliers notwithstanding, the policy sales and service process is not particularly customer friendly. And much the same is true of claims, where there is strong perception that for a customer, settling claims, whether in timeliness, adequate status communication or payment is painful.These are all symptoms of an inward focus within carriers. Fortunately, newer software offerings to this sector are customer and partner centric. This helps in several ways.
First, these systems include lead management and customer management functions, a customer portal, as well as other pre-packaged customer touch points. This enables more business to be closed and improves customer communication. Second, these systems present a holistic view of the customer - allowing the carrier to identify and then focus on the most profitable customers. Third, they enable cross and up selling of additional products, thereby increasing revenue per customer.
Above all, this change engenders a shift to a product and sales model that is driven by customer needs, preferences and lifestyle.
Similar benefits accrue to business partners, prominent amongst which are agents, brokers and aggregators, not to mention service providers on the back end. Enabling these parties to interact via the web, and via their mobiles in real time with the insurance companyremoves additional layers of delay and improves productivity.
Finally, there is the issue of adaptability and modifiability. Insurance carriers, as with all businesses, are often taken by surprise by events in their environment. This could take the form of a new product opportunity, a mandate from the regulator or a process insight. What sets the winners apart is the ability to rapidly adapt to change. This is not merely their adaptability as an organization; it is also the ability of their software platform to be rapidly modifiable as well. The result should be an orchestrated, graceful transition to the new reality.
On the subject of surprises, machine learning is a good way of avoiding these or at least anticipating some of them. Machine learning techniques arise from the confluence of statistics, software and cheap processing. Essentially these look at all of the data generated by insurance company operations and search for meaningful patterns within. If implemented well this creates a culture of anticipated change that is carried out proactively.
To conclude, the insurance industry needs to transition away from the concept of insurance software as mere record keeping. Instead, it needs to view software systems as semi-sentient, decision-making, and constantly evolving parts of the entire value chain. When this occurs, it has the potential both to create huge growth in the market, and to enable us to leapfrog world insurance markets from a structural perspective.
(The author is CTO & Co-Founder at OneShield India)
Insurance is often referred to as the dark horse of financial services, most likely because until very recently, it has struggled to get away from its perception as a non-performing item on the family or corporate expenses. This is changing.
There is more competition, and if our legislators find a way, there will be more. The mix of insurance products being sold has diversifiedand expanded from vanilla flavored Life and Motor to include curious and excitingthings such as investments, mobile phones, travel, and health. Usage based, 'micro'insurance is likely on its way as well. So yes, the day is coming when the truck driver who cuts you off on NH-8 will pay his insurance carrier dearly for that particular lapse in judgment, thanks to the black box transmitting under his seat.
These products and innovation simply involved customer-touch-points and high product complexity, thereby allowing carriers to distinguish themselves from the herd.
What this mandates for insurance companies are software systems that enable low-cost operating models, high-quality customer-service, e-commerce, as well as product and process adaptability. Finally, a dash of machine learning helps.
A low-cost operating model implies software-based automation of the entire value-chain. It is not as if insurance companies are not automated. Instead, they feature islands of automation, with manual links between these islands.
Unsurprisingly, these links are where the bulk of the avoidable cost, friction and service quality degradation occurs. Designing and implementing business processes where the default is full-automation and only exceptions are handled manually is what insurance carriers need to do. This is not easy. The major issue isn't software - instead it is managing cultural and organizational change. This issue is exacerbated because few carriers realize that their line-managers, while talented practitioners of insurance, are not usually trained, or qualified to manage change.
Clearly, it is impossible and cost-prohibitive to automate fully. So, a low-cost operating model also needs software to measure and monitor non-automated processes. This provides oversight on human decisions, enabling management to ensure uniformity in decision-making even when processes are manual. In particular, this has a huge impact both on risk underwriting and claims adjudication - both of which have long been bastions of 'insurance as an art'.
It has taken me multiple weeks to get a quote for business insurance. On occasion, my policy has expired, and my carrier has not bothered renewing, thereby walking away from recurring revenue. Some outliers notwithstanding, the policy sales and service process is not particularly customer friendly. And much the same is true of claims, where there is strong perception that for a customer, settling claims, whether in timeliness, adequate status communication or payment is painful.These are all symptoms of an inward focus within carriers. Fortunately, newer software offerings to this sector are customer and partner centric. This helps in several ways.
First, these systems include lead management and customer management functions, a customer portal, as well as other pre-packaged customer touch points. This enables more business to be closed and improves customer communication. Second, these systems present a holistic view of the customer - allowing the carrier to identify and then focus on the most profitable customers. Third, they enable cross and up selling of additional products, thereby increasing revenue per customer.
Above all, this change engenders a shift to a product and sales model that is driven by customer needs, preferences and lifestyle.
Similar benefits accrue to business partners, prominent amongst which are agents, brokers and aggregators, not to mention service providers on the back end. Enabling these parties to interact via the web, and via their mobiles in real time with the insurance companyremoves additional layers of delay and improves productivity.
Finally, there is the issue of adaptability and modifiability. Insurance carriers, as with all businesses, are often taken by surprise by events in their environment. This could take the form of a new product opportunity, a mandate from the regulator or a process insight. What sets the winners apart is the ability to rapidly adapt to change. This is not merely their adaptability as an organization; it is also the ability of their software platform to be rapidly modifiable as well. The result should be an orchestrated, graceful transition to the new reality.
On the subject of surprises, machine learning is a good way of avoiding these or at least anticipating some of them. Machine learning techniques arise from the confluence of statistics, software and cheap processing. Essentially these look at all of the data generated by insurance company operations and search for meaningful patterns within. If implemented well this creates a culture of anticipated change that is carried out proactively.
To conclude, the insurance industry needs to transition away from the concept of insurance software as mere record keeping. Instead, it needs to view software systems as semi-sentient, decision-making, and constantly evolving parts of the entire value chain. When this occurs, it has the potential both to create huge growth in the market, and to enable us to leapfrog world insurance markets from a structural perspective.
(The author is CTO & Co-Founder at OneShield India)
Source:-The Economic Times
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