In the data-intensive insurance landscape, significant strides in Machine Learning (ML) and data profiling are driving progress. These technologies enable the anticipation of policy losses and the projection of claim trajectories, offering previously unattainable insights. Unlike conventional approaches, these advanced tools possess the capacity to consider numerous inputs simultaneously, enabling underwriters and adjusters to make informed decisions, leading to improved outcomes for insurance companies and policyholders alike.
This article explores the latest trends in data profiling and ML, highlighting the significant impact these technologies are having in the world of insurance. It will also shed light on how they work together to optimize data-driven decision-making and the challenges faced by insurers in this journey.
What is data profiling?
Data profiling is the process of analyzing data to understand its characteristics and structure and is a precursor for successful ML applications. Data profiling tools generate summary statistics, identify gaps, and detect inconsistencies in a dataset, providing valuable insights for data cleansing and normalization. This process ensures that the data used for training and validation is of high quality, thereby improving the accuracy and reliability of machine learning models. Moreover, data profiling helps identify some types of potential bias in the data, enabling insurers to address fairness and ethical concerns in their machine learning applications.
What is machine learning?
Machine learning (ML) is a type of artificial intelligence (AI) that allows computers to learn from data without the need to explicit program rules or other evaluation logic. ML goes beyond data profiling by leveraging data to discover patterns, make predictions, and perform classifications. Trained ML models can analyze vast amounts of data, ranging from medical history and demographics to driving records, and even external factors like community-level crime and health information, to find correlations in disparate data attributes (also called features) used to expedite the delivery of swifter and more precise insights. The power of machine learning lies in its ability to process complex datasets and recognize subtle relationships that might not be evident through traditional analysis.
When used together, data profiling and ML can provide insurers with a powerful tool for understanding and predicting risk. This can lead to many benefits, ranging from more precise pricing and expedited claims processing to elevated customer service. Here, we discuss some advantages:
Precision: By pooling together historical data, insurers can analyze risk factors with a great deal of precision. Factors spanning the policyholder's vehicle type, geographical location, age, and driving history, give insurers the insight to develop the risk quotient associated with a policy. This precision equips underwriters with the ability to price policies that mirror the actual risk involved.
Streamlined claims: By leveraging these technologies, insurers benefit from automating claim categorization into high and low-risk brackets. This allows for "straight-through processing" of low-risk applications and accelerates claims workflows. As a result, the processing time, energy, and expenses linked with low-risk claims can be significantly reduced, allowing adjusters to concentrate on more complex cases.
Fraud: The combination of data profiling and ML detects anomalies in claims data that may be indicative of potential fraud. Beyond the financial gains, this elevates the company's reputation and credibility.
Challenges
While the collaboration between data profiling and ML is highly beneficial, insurers also face challenges when incorporating these technologies into their systems.
One such hurdle is how to ensure data quality. Real-world data is often riddled with inaccuracies and inconsistencies. Overcoming this hurdle demands insurers to invest time and effort in understanding error origins and subsequently rectifying data quality concerns.
Moreover, the potential for biases in machine learning models poses another challenge. If the training data used to build ML models is biased or imbalanced, the resulting predictions may be inaccurate or discriminatory. It is crucial for insurers to address and mitigate these biases to ensure fair and unbiased decision-making.
Lastly, the quantity of data is critical. This data quantity challenge is like the bias issue; both are integral to effectively harness ML. To effectively capture the inherent variability of inputs and results in your business, it's important to have a substantial amount of data. For example, consider training a ML model for a self-driving car to recognize an octagonal sign as a stop sign. While this would be effective in the US, its performance in Japan would fail since stop signs in Japan are an upside-down triangle, similar to Yield signs in the US. Understanding the business context used in ML training and ensuring that the ML model is used only in that context is a subtle but important aspect of successfully using ML.
As the insurance industry becomes increasingly data-driven, insurers must foster a culture of continuous learning and improvement. Regular updates and refinements to ML models are essential to adapt to changing market conditions, customer preferences, and emerging risks. By staying agile and proactive in embracing technological advancements, insurers can gain a competitive edge and provide better services to their clients.
In the ongoing data-driven future, the integration of data profiling and ML will be pivotal in reshaping insurance practices. By leveraging the power of these technologies, insurers can navigate complex challenges, address bias concerns, and drive positive outcomes for both the insurance companies and their valued policyholders. Adopting data profiling and ML will propel the industry into a future of enhanced efficiency, better risk assessment, and superior customer experiences.
