Estimating the risk of insurance fraud based on tonal analysis

Sammanfattning: Insurance companies utilize various methods for identifying claims that are of potential fraudulent nature. With the ever progressing field of artificial intelligence and machine learning models, great interest can be found within the industry to evaluate the use of new methods that may arise as a result of new advanced models in combination with the rich data that is being gathered. For this end, we decided to evaluate a Long Short-Term Memory (LSTM) - as well as a residual (ResNet) type of neural network, with the purpose of estimating the risk of insurance fraud based on acoustic properties of conversations between customers and company representatives. Furthermore, we drew a connection between identifying conversations that regard fraudulent claims and detecting deceptive speech. With this connection in mind, we simulated data representing deceptive speech by artificially altering the pitch and used it to evaluate four types of acoustic features: Filter bank energies, cepstral coefficients, mel-frequency filter bank energies, and mel- frequency cepstral coefficients (MFCC). We found that a LSTM model could be viable with either feature tried. Additionally, we found that the filter bank energies yielded the best performance and it did so on the grounds of having been computed over a multitaper spectrogram. We did not find any combination of model and feature that could generalize results from training data onto data used for validation with respect to real conversations between customers and company representatives.