Implementation of Gaussian Process Regression in Estimating Motor Vehicle Insurance Claims Reserves

This study aims to calculate the allowance for losses by applying Gaussian Process regression to estimate future claims. Modeling is done on motor vehicle insurance data. The data used in this study are historical data on PT XYZ's motor vehicle insurance business line during 2017 and 2019 (January 2017 to December 2019). Data analysis will be carried out on the 2017 - 2019 data to obtain an estimate of the claim reserves in the following year, namely 2018 - 2020. This study uses the Chain Ladder method which is the most popular loss reserving method in theory and practice. The estimation results show that the Gaussian Process Regression method is very flexible and can be applied without much adjustment. These results were also compared with the Chain Ladder method. Estimated claim reserves for PT XYZ's motor vehicle business line using the chain-ladder method, the company must provide funds for 2017 of 8,997,979,222 IDR in 2018 16,194,503,605 IDR in 2019 amounting to Rp. 1,719,764,520 for backup. Meanwhile, by using the Bayessian Gaussian Process method, the company must provide funds for 2017 of 9,060,965,077 IDR in 2018 amounting to 16,307,865,130 IDR, and in 2019 1,731,802,871 IDR for backup. The more conservative Bayessian Gaussian Process method. Motor vehicle insurance data has a short development time (claims occur) so that it is included in the short-tail type of business.

A Study on Helping Teachers Learn and Use the Investigative Approach in Science

The purpose of this study was to find out how teachers could be helped to learn and use the investigative approach in science. Two teachers from the lower secondary school participated in the study and my role was to help them learn and use the investigative approach through joint planning, modelling and reflective practice. The study was conducted in three stages. The pre-intervention stage showed the teachers' current practice in science, the intervention stage where the investigative approach was introduced and the post-intervention stage indicated the impact of the innovation on teachers' teaching to detect change. The research draws on qualitative data sources such as semi-structured interview, participant observations and teachers' reflective journals. Findings revealed that teachers came with their beliefs about the way children learn science and this was because of their experiences as students and the way they taught was a manifestation of their beliefs. The findings also showed that the change process is influenced by teacher beliefs, understanding of the change process and contextual factors such as time, syllabus, and relationships. The study found that working closely with teachers and encouraging them to reflect on their practices in a conducive, supportive environment helps teachers internalise change thus enabling them to attempt and implement the investigative approach to change their practice. The teachers were positive about the innovation and their self-confidence and understanding of the teaching learning process was greatly enhanced.