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Machine Learning for Banks and Fintechs

Banks are facing challenging times. Digital savvy users want easier and faster means to interact with their bank, FinTechs implement lean models, GAFAs and Telcos are aggressively entering into financial services… In this new era of competition, banks need to leverage the value of their data that are most of the time locked and undervalued in old legacy systems.

Cross-functional teams focused on quickly building prototypes can learn to really master the continual cycle of execution, exploration and learning specific to AI. The experimentation of machine learning technics inside small-scale pilots should be encouraged to develop and refine new ideas and answer to the attacks of innovative and fast-moving competitors.

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Machine Learning Use Cases for Insurance

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Technologies are changing the way consumers buy, social factors are modifying the way they own and use their assets, digital savvy users want easier and faster means to interact with their insurance company, InsurTechs implement lean models … Disruption factors are strong and they are here to stay.

Machine learning and data science provide new options to deliver more efficient and tailored services to customers, enhance the customer experience, develop stronger sales performances and optimize processes.

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Machine Learning Use Cases for Retail

Machine learning is one of the biggest and most broadly impactful innovation to shape the retail sector. ML-driven services are changing the sources of competitive advantages and retailers need to develop flexibility and agility, both at human and IT infrastructure levels, to address the pace and degree of change.

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