Unlocking Underwriting Potential: The Power of Data-Driven Transformation

In the midst of economic uncertainties and emerging challenges, traditional insurance and banking business models face threats that demand adaptation as a necessity rather than a choice. While banks have made some progress in their transformations, they still lack the leadership required to unlock new business and operating models that can restore revenue and profitability. On the other hand, insurance companies have lagged behind in successful digital transformations, leading to decision paralysis among their leadership as they grapple with where to invest to ensure customer satisfaction and responsible growth.

In their digitalization efforts, insurers often tread a path similar to that of banks from years past. They aim to digitize processes to reduce operational costs and enhance customer satisfaction, but they tend to approach this journey with excessive caution, leading to small initiatives that result in financial losses and widespread resistance to digitalization. Through my experience as a board member and tech entrepreneur, I have come to understand that successful digitalization requires leadership, vision, courage, and focus. Unfortunately, many board members in banks and insurance companies struggle with this and tend to emulate competitors rather than taking bold steps. This risk-averse behavior fosters decision paralysis and, consequently, hampers the impact of digital transformations.

I argue that, although risk management is essential, leadership teams should shift their focus toward better serving their customers. Especially now new generations have definitely come into play! They should engage in fact-based discussions about building the necessary capabilities to seize next-gen customer-driven opportunities. Datalization, the process of transitioning into a fully data-driven organization and operating model that employs data and algorithms responsibly to expand the business, is undeniably one such capability. McKinsey reports that insurers who successfully transform their traditional operating models in this way outperform the market by 20% to 25% in terms of profitability. The challenge now lies in developing the core capability to operate in a data-driven manner across the entire organization, converting data into actionable intelligence that empowers the organization to capitalize on critical opportunities. This is where Datalization comes into play.

While insurance companies have embarked on digitalization projects over the past two decades, the focus has shifted from centralizing information to permeating data-driven insights throughout all operations and the entire value chain. Some insurers, learning from the banking sector, have highlighted specific use cases in this journey. In this blog, I emphasize the "underwriting" use case.

Underwriting is the process of assessing and calculating the financial risk associated with an individual or institution. It encompasses three categories: loans, securities, and insurance. Loan underwriting traditionally relies on four main factors: the borrower's income, appraisal, credit score, and assets. With the increasing automation of the loan underwriting process, there is a significant opportunity for improvement through datalization. In my work as a practitioner and academic in innovative credit scoring, I have witnessed the substantial impact of strategies that embrace "all data is credit data" with neo-banks. For instance, research by Professor Jagtiani shows that Funding Circle's credit decision quality surpasses that of traditional FICO credit scores, with Gini scores approximately 18% higher. Funding Circle's success is attributed to its connection of over 80 data sources to its credit scoring engine. Similarly, at AdviceRobo, we have integrated data from 90 sources over the past decade, resulting in credit score quality comparable to Funding Circle's. Our platform also covers a wide range of asset classes and geographies, further enhancing the robustness of our scoring. We have observed increased acquisition rates of up to 30% with reduced bad debt levels to 20%. These advancements hold great promise for insurers as well.

Insurance underwriting involves assessing potential policyholders for various types of insurance, including life, wellness, and property insurance. It helps determine the risks associated with frequent and extensive claims while calculating the coverage percentage for insured individuals. Life insurance underwriting, in particular, assesses health risks based on factors such as age, health, family medical history, occupation, lifestyle, fitness, and more. Currently, insurance underwriting remains a predominantly manual process of risk evaluation.

As previously mentioned, there is a significant opportunity for insurers and insurtech companies to digitize and datalize their underwriting processes. Firms like Friss, Shift, and Pinpoint focus on automating fraud and claim processes, while companies like Quantexa, Carpe Data, and my company, AdviceRobo, facilitate the datalization of these automated processes using structured and unstructured data. This combination accelerates insurers' digital transformations in underwriting, enabling them to meet the service expectations of millennials and Generation Z.

In conclusion, I believe that insurance leaders should be encouraged to embrace full-scale digital transformation and accelerate customer impact by adopting datalization strategies. One of the most impactful use cases in this matter is in underwriting for the next generations! Now it’s up to the leaders to really unlock their underwriting potential.