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No function is more essential for success in the insurance industry than underwriting. The ability to accurately evaluate the risk of insuring a customer (existing or new) and pricing their coverage appropriately has been fundamental to the industry for hundreds of years. But like almost every other part of the insurance industry, underwriting is going through a period of fast examination and transition.  

In a recent global survey, insurance executives listed the top three underwriting process changes they need to make: 

  1. Increase the use of automation to increase speed, efficiency and profitability and lower costs 
  2. Utilize more data from more diverse sources to create better risk models and improve accuracy 
  3. Increase the use of Artificial Intelligence (AI) for more cost-effective access to the above. 

A variety of different terms have been used to describe this new approach to underwriting, including “intelligent underwriting” and “digital underwriting”.  

 

Challenges to the Traditional Underwriting Process in Insurance 

Data, and asking questions of that data, has always been critical in insurance. When did a similar event last occur, and how often? What can I learn about my customer’s behavior in order to understand their bind and renewal patterns? Many insurers would argue that the ability to interrogate this data is the single most important asset they have and is fundamental to their competitive edge.  

Today, there is more data available for driving this competitive edge than ever before. However, this brings with it a new set of challenges. How do you access it quickly, and iteratively evaluate its potential value? How do you analyze its predictive power and derive meaningful context from it? Answering questions like these is all part of the journey to digital underwriting.  

Other hurdles on the path to intelligent underwriting, now and in the future, include: 

  • Managing data volumes – Handling data is becoming increasingly difficult. According to IDC, 57% of companies find data fragmentation an obstacle to being more data-driven and therefore requires enterprise scalability. 
  • Spending time wrangling & connecting unstructured data with structure data – It’s estimated that more than 80% of enterprise data is unstructured, but only 10% of it is used alongside structured data. On top of this, over 60% of data teams’ time is spent normalizing and cleansing data from different formats. With the growing complexity of data formats and structures, it is crucial to move away from rigid data format tools to a more schema-less data fusion approach, leveraging tooling such as a dynamic entity resolution.  
  • Uncovering context about the customer – Less than 30% of organizations create any form of holistic customer view, and those that do struggle to do it accurately and dynamically for different business uses. The ability to quickly extract context from relevant public and third-party data sources alongside your internal systems to build a more unified view of risk is becoming increasingly valuable.  
  • Managing regulatory & organizational governance concerns – There is an increasing pressure by regulatory bodies on ensuring fair value and adequate underwriting due diligence. Both for fairly offering services to customers at the right price, but also to manage Environment, Social & Governance (ESG) risks as well as protecting from financial crime fraud – this is a tough balance for insurers to strike.   
  • Using agile data IT processes which also protect customer privacy – It is crucial for insurance organizations to be flexible and scalable in their technology to adapt as customer needs, business needs and the data landscape evolves. Not only this but everything should be built with a robust privacy model to ensure that sensitive data is protected, and is in the right hands at the right time.

The digital underwriting process in Insurance

Insurance Underwriting Is More Than Just Pricing Risk 

In its simplest form, insurance has effectively been – and will continue to be – centered around its ability to sell a predictive analytical model using a balance of historic data, future predictive insights, and domain expertise. However, being able to discern what data is necessary and valuable in helping to predict the likelihood of a future event and – if it does occur – its financial impact is still a challenge – one which, based on the wide variation of loss ratios the industry has, it is yet to master.  

As well as this, the increasing pressure of fair and transparent pricing is putting added strain on insurers’ ability to determine their pricing strategies freely and without prejudice.  

But insurance is far more than just pricing a risk model. This is due to: 

  • Increased competitiveness of products and niche, tailored services which go beyond the traditional distribution processes (e.g., embracing embedded insurance to make more bite-size insurance products more readily available, bridging the well-known global insurance coverage gap). 
  • Being bold and looking at new ways to bound your risk (e.g., empowering parametric or consumption-based insurance prices to frame more transparent claims adjudication processes –simplifying claims reserving, billing and payment process to allow more touchless servicing). 
  • The need to create a more seamless and integrated customer experience (e.g., building hyper-personalization strategies which allow customers to interact with their insurer, much like they would with their favorite online e-commerce or tech company). 
  • The need to understand how to maximize customer value, for them and for you – being able to understand how to retain good customers and keep them happy with the products, but to use the concept of connect cross and up-selling to maximize the potential business. 
  • Increasing the focus on the role of insurance within society – ensuring insurers offer socially responsible products, monitor their supply chain and perform accurate due-diligence on customers for environment, social or governance (ESG) impacts/concerns. 
  • Going from basic commercial KYC to KYC++ – the ability to know your customer’s customer (or supplier), and their customer (or supplier). Insurers are investing more in processes to properly assess highly complex commercial structures and their organizational network to increase context. 

There is no one size fits all approach to this, nor will traditional statistical or demographic approaches, using siloed data, be enough. Insurers must consider their transition to intelligent digital underwriting which forms the basis for an agile, dynamic, and accurate decision ecosystem – one which truly leverages a critical and often under-utilized asset: data.  

 

The Future of Digital Underwriting is in Decision Intelligence 

With the continued explosion of data and the growing importance of profitability, speed and customer experience, insurers must act now.  All insurers should be re-evaluating if they are really set up for intelligent underwriting and are well equipped to solve the above challenges.  

The race is now on. To succeed, there are three core areas for focus:  

Strategic area & challenges to overcome How decision intelligence helps 
Managing data complexity by ensuring you have enterprise scalability of your data platforms and analytics tooling, which can cater to the breadth, volume, and myriad of formats and data sources. Decision intelligence avoids historic fixed/predefined data models by providing a fast to ingest, schema-less data fusion framework with low-code configuration to ingest data from any format or source.  
Unlock a more holistic and contextual analytical view of your data, and by extension, your customer by extracting meaningful insight about customer behavior, traits, preferences to provide more tailored products and prices.  Dynamic Entity Resolution and Network Generation enables single views of customers and uncovers entity connections to provide more context and improve decision-making. Contextual Data Exploration tools allow operational underwriting teams, actuaries, and analysts to interrogate the data with intuitive data visualization tools.
Remove decision silos to allow teams across different parts of the business to have access to high-value data assets faster, reducing time and cost spent on teams wrangling data in separate ways. Provides a unified and normalized process of the data-decision estate across the enterprise ensuring the right data is in the hand of the right person, at the right time.

Insurers should be considering how they can bring data to the center of their business, harnessing the power of new data-driven and connected decision ecosystems. This is contextual decision intelligence. And this is the journey on the quest for digital underwriting. 

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