It is also important to carry out compliance regulation in the life insurance industry since it helps to regain the public’s trust, prevent fines from being imposed on the respective industries, stabilize operations of insurance companies and, thus, become significant for further consecutive years. Most traditional compliance frameworks are based on audit checks and rules that are not only error-prone, but also have high operational costs due to high volumes of data. From the current works, this paper presents the application of data science methodologies involving pattern recognition and anomaly detection to improve on compliance in life insurance undertakings. Data mining is thus powerful since it enables the identification of such deviations, the detection of fraud, as well as compliance with different processes. The use of these techniques does not only comply with these regulations but also enhances operations’ effectiveness and compliance with risks. It has been agreed that by using Isolation Forest and Autoencoders for anomaly detection alongside flagging expert systems oriented to domains that are susceptible to compliance violations, the number of these violations decreases. The research evidence indicates that data science has the potential to disrupt the regulatory compliance in the life insurance industry.