Business challenge:
The insurance industry is regarded as one of the most competitive and less predictable business spheres. It is instantly related to risk. Therefore, it has always been dependent on statistics. Nowadays, data science has changed this dependence forever. Insurance fraud brings vast financial loss to insurance companies every year. Data science platforms and software made it possible to detect fraudulent activity, suspicious links, and subtle behaviour patterns using multiple techniques.
To make this detection possible the algorithm should be fed with a constant flow of data. Usually, insurance companies use statistical models for efficient fraud detection.
UFT comes into show:
UFT came up with best usage of machine learning algorithm named Random Forest classifier.
Machine leaning algorithm ability is to learn from the historical fraud patterns and recognize them in future transactions.
The process goes as feeding the data, extracting features from the data, training the algorithm which is named Random Forest classifier then creating the model.
Impact Delivered:
Rule-based fraud prevention systems was proposed. Scaling was achievable where ML algorithm shows better performance along with the growth of the dataset. Model Efficiency increased based on the repetitive work of manual fraud analysis, along with the accuracy.