While AI can have great contributions to the fight of financial crime, one of the biggest problems with AI at present is the comprehension of the decision-making process. Explainable AI serves as a transparency technique by providing an explanation of the output, while maintaining a high level of prediction accuracy. This helps banks and financial institutions to tread the fine line of decision making in compliance.
Even though we are already used to speech recognition in the car, in smart homes or elsewhere, the progress in deep learning is significant. State-of-the-art AI models can solve the most difficult problems by recognizing complex patterns in real-time. Those intelligent systems can support work flows as an interface between man and machine. targens offers a solution here for speech processing, speech comprehension and speech output with a high degree of linguistic and technological adaptability in German to support the digitization process.
The financial sector is one of the first domains to drive interest in using artificial intelligence (AI), even before high computing machines were available. With the release of Louis Bachelier’s thesis Théorie de la Spéculation (Theory of Speculation) in 1900, advanced mathematics in the financial field originated and the rise of statistical modelling marked the beginning of primitive AI in