Automating fraud risk management in the insurance sector

Digital transformation has increased the digitization of insurance processes. Accelerated leverage of technology across the insurance value chain has improved customer experience, awareness, innovative products, and interactions with intermediaries. This has however also led to increased exposure to fraud and its impact. As per the PwC Global Economic Crime Survey 2022; 63% of Eastern African respondents reported having experienced fraud compared to 46% globally. The need for automated risk management and fraud control strategies using Artificial Intelligence, Machine Learning, and Blockchain to strengthen fraud prevention and protection capabilities is thus more crucial than ever before.

According to the Association of Certified Fraud Examiners (ACFE); a fraud control activity is a specific procedure or process intended to prevent fraud or detect fraud quickly if it occurs. Automated fraud control activities established through monitoring and real-time or near real-time alerts are critical to improving fraud detection and prevention capabilities while reducing operational costs. The automation must envisage the possibility of fraud occurring at any stage of the insurance value chain: application, policy issuance, customer service, claims processing, payments, or exit. Automation leverages capabilities in data analysis, pattern recognition, and anomaly detection to process large volumes of data. This data is from various sources, such as financial transactions, customer profiles, and historical records. Automated solutions examine relationships between different data points to detect fraudulent behaviour.

Some of the common fraud schemes that can be detected through automation include:

The lack of data, and good data quality because of data errors or omissions, including missing, inaccurate, or inconsistent data across operational systems is a big challenge for insurers. The data challenges directly affect the efficiency of the automated systems and technology tools being implemented. The lack of data governance structures across business units, operations, and other functions, tends to create challenges in aggregating and analysing data. 7 / 32 Data governance and good data quality practices are essential if insurance companies are to effectively implement automated fraud risk systems.

Data governance and good data quality practices are essential if insurance companies are to effectively implement automated fraud risk systems. Data governance establishes the rules and processes for managing data across the organisation. It ensures that data used in fraud risk automation is reliable, accurate, and consistent and reduces the risk of errors and false positives. It can also help ensure that insurance companies comply with regulatory requirements, identify the right data sources, build a collaborative culture, and improve operational efficiency by continuously monitoring and improving systems. 

In summary, automating fraud risk management using Machine Learning, Artificial Intelligence, and Blockchain can help insurance companies improve their fraud detection and prevention capabilities while reducing operational costs. However, it is critical to identify the right data sources, develop data governance structures, build a collaborative culture, and continuously monitor and improve the technology systems to ensure their effectiveness.