Neural Network Application in Insurance Industry Essay Example

Neural Networks (NNs) are models of intelligence that consist of simple processing units also known as nodes that collectively are able to carry out complex pattern matching tasks. In comparison to traditional techniques, NNs play a crucial role in the current Artificial Intelligence era and have proved to be a success in predictive data mining. The accuracy of the NNs has seen them deployed in various disciplines including, bankruptcy prediction, credit scoring, the insurance industry, and quality control among other areas. This essay will focus on their applications in the insurance industry.

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Insurance companies rely on tools using NNs in the underwriting process in order to minimize the ratio of insurance premiums to losses. NNs have been trained to estimate severities The neural network systems tasked with underwriting analyze questionnaires, motor vehicle records, names and locate signatures. In health insurance underwriting they analyze prescription histories as well as attending physician statements. The summarized output is then provided to the underwriter who makes a decision. In cases where an applicant has inserted another applicant's information, the system can spot the error faster. NNs can accommodate as many as five features including age occupation and education. The system has also been modeled to incorporate data of a client past performance of insurance policies into the risk assessment. Incorporation of NNs in the underwriting process has minimized the risk associated with traditional methods of underwriting insurance.

The insurance industry has also employed NNs in predicting insurance insolvency. From simulations conducted in research by Ibiwoye, Ajibola, and Sogunro, (2012), the findings indicated a positive ability of the NNs to predict insurance insolvency. They noted that NNs harmonized various inputs into a most desirable output. The inputs, in this case, took note of internal factors such as auditor's reports and management as well as external factors such as government policies. In a similar research was carried out by Brocket et al (1994) who used a three-layer NNs to come up with an early warning system for US property-liability insurer two years prior to insolvency. A comparison of the results with those (Discriminant Analysis) DA showed that NNs outperformed DA and perfomed better than rating organization and the regulators. NNs have been used in the insurance insolvency prediction since they have high predictability and generalizability.

Firms that offer insurance policies have also used NNs in the detection of fraud. They are extensively used in healthcare insurance fraud detection due to their capability of handling complex structures and non-linear variable relationships. Moreover, backpropagation neural networks can propagate a large number of instances with tolerances to noisy data and have shown the ability to categorize emerging patterns on which they have not been trained. They have also shown an ability to learn and adjust to numerous new techniques hence highly effective to the evolving maneuvers of fraudsters. Neural networks are more efficient in detecting subtle or non-intuitive patterns to help identify fraudulent transactions. Time taken to detect fraud has been cited as one of the leading causes of loss to insurance fraud. However, with the employment of NNs insurance companies can get the analysis in real time. Additionally, advanced models such as NNs automatically update to reflect the latest trends. Fraud detection in the insurance sector has also extended to accommodate other cases of fraud such as automobile and faults in personal injury claims. Brockett et al (1994) noted that NNs was successful in uncovering automobile injury claims fraud. They concluded that it was a consistent and reliable tool.

Neural networks have also been used to the model the Premium price of insurance customers. Premium prices play an essential role in enabling insurance companies to find a balance between making the profit as well as maintaining their growth. The challenge has always been to fix premium prices to cover the anticipated claims and still gain profit and at the same time not to set premium prices higher than the market share is compromised as the customers can shift to other insurers. Use of neural networks in the determination of premium prices has played a crucial role in the prediction of retention rates from policy and demographic information. Concerning retention rates, neural networks have learned to distinguish policyholders who are likely to dismiss their policies from those who are likely to renew and consequently predicting the retention rate prior to price sensitivity analysis. Neural networks are then used to analyze the consequence of a change in premium price on whether a policyholder will terminate or renew his or her policy.

The essay has expounded on the use of the neural networks in the insurance field. In the underwriting process, it has minimized the risk in assessing potential policyholders by scrutinizing the information they provide in a faster, efficient way. In insurance insolvency, enough literature shows NNs have been deployed in the prediction of insolvency while in fraud cases they system has caught up with the ever-changing trick and minimized losses to the insurance companies as a result. Moreover, by analyzing changes in premium price in the past years the NNs have proven useful in prediction of customer retention.

References

Brockett, P. L., Cooper, W. W., Golden, L. L., & Pitaktong, U. (1994). A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency. The Journal of Risk and Insurance, 61(3), 402. doi:10.2307/253568

Ibiwoye, A., Ajibola, O., & Sogunro, A. (2012). Artificial Neural Network Model for Predicting Insurance Insolvency. International Journal of Business Research, 2(1), 59-68 .