As the insurance industry evolves, it is becoming more crucial for underwriting organizations to proactively manage risk. This not only includes traditional risks, but also staying ahead of emerging risks and considering the impact of macroeconomic factors on their business.
In this article, we delve deeper into the importance of data in underwriting innovation strategy; a solid data foundation is crucial for underwriting organizations to make informed decisions and stay competitive in the industry.
The Ingredients of a Strong Data Foundation: Data Quality, Governance and Security
Data is the backbone of underwriting, allowing organizations to make informed decisions and stay competitive. Underwriting organizations must consider a variety of data sets when making decisions. To consider these data sets, they must first be gathered, stored, and exposed. By having a strong data foundation, underwriting organizations can improve decision-making, better pricing of policies, and increase efficiency – all contributing to bottom-line profitability.
Crucial to having a strong data foundation is data quality. Data quality refers to the accuracy, completeness, consistency, and timeliness of data. Poor data quality can lead to incorrect decisions and wasted resources. Therefore, underwriting organizations must ensure that their data is accurate, complete, and up-to-date. Data quality also enables data trustworthiness which is a key need to ensure UW willingness to make decisions based on the data.
Another key ingredient of an organization’s data foundation is data governance. Data governance is the process of managing data as a strategic asset. It involves establishing policies, procedures, and guidelines for data management and ensuring compliance with legal, regulatory, and ethical standards. Data governance ensures that data is used in an ethical and responsible manner and that it is protected from unauthorized access and misuse. Data governance should address the risks of bias being embedded in the data leveraged for decision-making.
In addition to data quality and governance, underwriting organizations must also consider data security. Data security refers to the protection of data from unauthorized access, use, disclosure, disruption, modification, or destruction. With the increasing threat of cyberattacks and data breaches, data security has become a critical concern for underwriting organizations. They must ensure that their data is protected from unauthorized access and that they have the necessary controls in place to detect and respond to security incidents.
All of these roll up into the need for these organizations to have a data management strategy in place. A data management strategy includes the policies, procedures, and technologies used to manage data throughout its lifecycle. This includes data acquisition, data integration, data quality, data governance, data security, and data archiving. A data management strategy ensures that data is being used effectively and efficiently throughout the organization.
Having a solid data foundation – both on the infrastructure end and on the management strategy end – helps underwriting organizations make informed decisions and stay competitive in the industry. By ensuring data quality, governance, security, and having an architecture that supports them, underwriting organizations can improve decision-making, better pricing of policies, and increase efficiency.
Types of Data to Consider in Underwriting
When it comes to underwriting, there are several types of data that organizations should consider when making decisions. These include financial data, risk data, customer data, loss data, unstructured/analog data, IoT/telematics data, 3rd party raw data and 3rd party scoring data.
- Financial data includes information about the financial performance and stability of a company or individual.
- Risk data includes information about the potential risks associated with a particular industry or asset.
- Customer data includes information about the demographics, behavior and preferences of customers.
- Loss data represents the claims incurred over prior policy years and has been the primary foundation of data laid over the past 3-5 years in insurance companies. Loss data, both owned by the insurer (structured) and from third-party carriers (unstructured), have been gathered and organized, and some businesses have even begun digging up old paper records from pre-digital eras. These unstructured and analog data often begin their underwriting life as PDF files which insurers must run NLP-based AI software to extract the core data points from, as each may have unique formatting.
- IoT/telematics data is data that is gathered from IoT devices such as on-board vehicle telematics, satellite imagery of properties, electric and water flow monitoring, equipment diagnostics, computer vision, and even – more frequently – health data from wearables. This data is becoming more and more pervasive, which creates some interesting challenges and opportunities for underwriting. As more data can be measured in real-time, interventions can occur before risk parameters scale too severely, meaning that losses can be avoided altogether. Additionally, precise parameters on behaviors can be reviewed rather than just events (the lagging product of behaviors), which is what have historically been analyzed. These allow for positive risk analysis if properly harnessed, but they also require different approaches than historical actuarial models tend to allow for, introducing challenges for underwriting organizations if they want to capitalize on the prospective benefits.
- 3rd party data is also available for underwriters to consume. 3rd party data is data that is gathered from external sources such as weather modeling sources, government regulatory bodies (OSHA, CPSC, etc), industry analysts, and shareholder reports and financial analysis centers. In its raw form, if extracted via APIs or scraped from websites, it can provide useful contextual information for underwriters to consider. Knowing this, some InsureTechs have built services around collating and distributing that data. These 3rd party scores can be used to augment the understanding of the loss dollars, but it is important to be aware that these scores can be black boxes and may evolve over time without warning, factoring various elements, weights, etc. Underwriting organizations must be careful when considering them in their own analyses to be sure they accurately reflect the risk profile they imply to represent.
Considering all of these types of data when making decisions can help underwriting organizations to make more informed decisions and stay competitive in the industry, but it’s also a lot to digest. Not only do organizations need a robust data management strategy as detailed before, but they must consider the desired outcomes and business behavioral changes needed before pressing forward with technical implementations.
Likewise, before business entities strongly consider exploring the integration of a new data source, like IoT for example, they must be sure the technology organization is equipped to gather, store, and report out such data to make it usable. The business and technology organizations must work together in lockstep when driving forward a data strategy else they risk missing out on a key element of the solution.
Data Analytics and Artificial Intelligence
Data analytics and artificial intelligence (AI) can play a crucial role in underwriting innovation strategy.
- With the right data behind them, these technologies can be used to improve underwriting decision-making, providing insights that would be difficult or impossible to obtain through manual analysis.
- They can power the automation of low-complexity risk processing, enabling underwriters to spend more time analyzing and considering complex risks as simple data gathering is expedited.
- Predictive analytic can showcase loss trends, and AI can be used for risk assessment, price targeting, and fraud detection.
- These tools can also help insurers identify new opportunities and improve the customer experience.
Ultimately, the main takeaway is this: Data plays a vital role in underwriting innovation strategy. By having a strong data foundation and considering different types of data, underwriting organizations can make more informed decisions and stay competitive in the industry.
Furthermore, data analytics and AI are becoming increasingly important for underwriting organizations to stay ahead of the curve and enable underwriters to make faster, better decisions. By focusing on data, these organizations can set themselves up for success in the nebulous, dynamic environment we anticipate facing for years to come.
