Natural Language Processing

Natural Language Processing engines enable tons of use cases. ML6 has identified four major use cases, being self-learning chatbots, content customization, entity extraction and reason-based sentiment analysis.

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Social Listening

After all, increasing your ecosystem by improving the acquisition - and retention rate of your suppliers, customers and investors is what makes your business strive. That is why listening to the market is a key process for a financial institution.

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Insurance Claims

Insurance companies have the one thing AI requires to be successful — data. Machine learning has the ability to make that data actionable. By reviewing data from closed claims, machine learning algorithms can identify both straightforward claims for automatic processing and complex claims that are more likely to require human intervention. By identifying commonalities in closed claims that resulted in litigation, it could predict which new claims might take a similar path and recommend preventative measures.

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Algorithmic Trading

Enhancing a financial institutions’ trading departments to improve their trading process is a key value - and money generator. Each trader has its own trading strategy, which is a combination of gut feeling and (simple) mathematical formulas (e.g., averaging, etc.).

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Fraud Detection

One of the key processes for a financial institution is identifying and detecting fraudulent transactions. Whether the financial institution wants to identify fraud in credit card transactions and/or investment deals, this is something we are doing at ML6 everyday.

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