“Most businesses believe that machine learning models are opaque and non-intuitive and no information is provided regarding their decision-making and predictions,” — Swathi Young, host at Women in AI.
Explainable AI is evolving to give meaning to artificial intelligence and machine learning in insurance. The XAI (explainable AI) model has the key factors, which are explained in the passed and not passed cases. The features that are extracted from the insurance customer profile and the accident image are highlighted in the XAI model. The rules and logic for claim processing are presented in the output. In every case, passed cases are explained by showing the passed rules or coverage rules associated with the claim. Similarly, in the case of failed cases, the failed rules are displayed by the XAI model.
In many enterprises in the insurance vertical, the underwriting engine or policy rules engine is a black box. The recommendations, quotes, insights, and claim rejections/passes are generated by the black box without any explanation. The decisions are trusted by not only IT team members but also by the business team members. Usage of AI/ML for claim processing or generating a policy quote is high in the insurance domain. AI/ML algorithms are based on different techniques, which might lead to issues related to bias, cost and mistakes. Explainable AI has come to the rescue by explaining the decisions and comparing/contrasting them with the other decisions. This helps in customer experience improvement, customer satisfaction, operational efficiency, financial performance and enterprise performance.
Most of the AI projects are a failure because enterprises in insurance always thought AI models are not trustworthy or are biased. AI models never had implicitly explained the output. XAI helps in closing the gap between the black box and trustworthy AI (responsible AI). XAI has been used in enterprise risk management, fraud prevention, customer loyalty improvements and market optimization. XAI has not just improved operational efficiency but also the fairness in the recommendations, insights and results. The explainable AI provides the software strengths, weaknesses, features, criteria for decisions, conclusion details and bias/error corrections.
Let us now look at the basic tenets of XAI (Explainable AI). Those tenets are transparency, fidelity, domain sense, consistency, generalizability, parsimony, reasoning and traceability. Many insurance enterprises are planning to adopt explainable AI in their decision-making. The decisions that affect customers, like quote generation, policy quote payment options and policy package options, are being modified with XAI showing the differencing based on the criteria and features.
“A recent survey found that 74% of consumers say they would be happy to get computer-generated insurance advice” — Forbes
Regulatory policies can be imposed and explained by XAI to the insurance enterprise. This helps them to abide by regulation laws. Claim processing can be improved, and analysis presented can be enhanced with the bias corrections and decisions that were not taken. Fraud can be prevented easily using AI/ML with XAI. Fraud rules can be verified, and the violations can be displayed to identify the area of the fraud. This helps in improving the revenue of the enterprise and cutting down the losses. The detection accuracy can be measured using true positives and false-positive analysis. This helps in cutting down the cost as the claim process is better streamlined.
Customer loyalty and retention can be improved by using AI/ML for customer behavior analysis. The prediction algorithms can be used for churn prediction and recommendation engines. Insurance pricing engines can use AI/ML for price prediction. The price predicted can be explained based on the customer profile, history and customer expectations. This helps in improving customer satisfaction and loyalty. XAI helps in making the AI model management more responsible. Business users like to know why the decision or the output is better. They can use the decisions easily and improvise.
Responsible AI will be the next technology that ensures that decisions are taken wisely and trust is developed on the AI model. Casual AI can help in making the model more operational. The causes and effects can be described during the modeling, training, testing and execution. The complexity hidden will be simplified by inference engines and causality details. The next-level AI models and engines can help in adapting to new scenarios and make fair decisions with implicit causality.