AI and the Importance of Data Quality

Understanding the types of data you have is crucial to preparing and incorporating AI into your business processes. This involves data discovery, assessment, and categorisation, laying the foundation for building effective AI models and systems. This blog explores the different things to consider when developing these processes. 

AI has proven industry success

The use cases for artificial intelligence (AI) in insurance are strong, but the barriers to entry are worth noting. The Forbes article "Want your company's AI project to succeed? Don't hand it to the data scientists, says this CEO" estimates that between 83% and 92% of AI projects fail. 

Good vs Poor quality data

Good-quality data should be accurate and complete, e.g., when an insured's information, coverage type, policy term, premium, deductible, coverage limits, and endorsements are clear and up to date in an insurer's system. 

Poor-quality data contain nonsensical information and inconsistencies, creating inefficient and ineffective systems. For example, inconsistencies may take the form of one claim on two policies or a single policy attached to multiple agreements. 
Poor-quality data can hinder the training and operation of AI models, obscuring the insight sought through AI integrations through:

• Underpricing or overpricing of policies due to incorrect risk assessments 
• Inefficient and ineffective fraud detection 
• Delays, mistakes, and inefficiencies in claims processing 
• Incorrect assumptions about customers leading to inappropriate marketing strategies and customer interactions 
• Non-compliance with regulations and legal requirements, resulting in the potential for fines, legal actions, and reputational damage 
• Reduced trust in a company's services 

Structured vs Unstructured data

Understanding the types of data you have is a crucial step when preparing to incorporate AI into your business processes. This process involves data discovery, assessment, and categorisation, laying the foundation for building effective AI models and systems. There are two main categories of data: structured and unstructured. 

Structured data refers to information that is organised and stored in a predefined format, making it easy to search, analyse, and process. This type of data is typically stored in databases and can be readily categorised, sorted, and used to gain insights, identify trends, and make data-driven decisions more easily. Structured data is ready and able to be manipulated by AI. 
For example, an insurer's structured data might include: 

• Policyholder information: Name, address, contact details, policy type, coverage details, premium payments, etc.
• Claims data: Details about past claims, such as claim amount, date of loss, cause of loss, settlement amount, etc. 
• Underwriting data: Information used to assess risk and determine policy eligibility, such as medical history, driving records, credit scores, etc.

Three tips for maintaining solid data

Clean data is critical to a successful AI implementation and data transformation. And yet, it's human nature to take shortcuts when under pressure, and the consequences of inserting data midstream can set an organisation back in its digital transformation and AI implementation journey. 
Here are three best practices that insurers can engage in to maintain strong data: 

• Establish clear data governance policies and procedures to ensure data is managed consistently across the organisation. Include defined roles and responsibilities for data ownership, quality monitoring, and maintenance. These policies and procedures should also extend to third-party partners that filter in data through integration. 
• Train employees who interact with the company's core system(s) to ensure they understand the importance of data accuracy and proper data entry and maintenance procedures. Help employees understand how their initial time upfront can save them and customers time in the long run. 
• Conduct regular data audits to identify inconsistencies, inaccuracies, or outdated information. Develop protocols to rectify the issues identified during these audits. 

Time to harness the power of high-quality data in AI

Understanding and managing data quality is paramount to effectively leveraging artificial intelligence in business, particularly in the insurance industry. 

By prioritising good data practices, such as clear data governance, regular audits, and comprehensive training, companies can mitigate the risks of AI project failures and harness AI's full potential to drive efficiency and innovation. 

Ultimately, the quality of data not only influences the success of AI applications but also shapes a business's overall integrity and competitive edge.

The future of business automation

The integration of AI, IA, and RPA into business operations is transforming industries by streamlining workflows, enhancing accuracy, and improving customer interactions. As businesses utilise these technologies, the potential for increased efficiency and error reduction is immense. Companies must take advantage of these technologies and learn how they can be applied within their operations to remain competitive. The future of business process automation is here, and AI powers it.

For support with your data practices, contact
Brighter.