TDS: 10 applications of AI to fintech

In this article, Towards Data Science goes through ten applications of AI and its subdivision, machine learning, in fintech.

#1. Digital Financial Coach/Advisor: Transactional bots are one of the most popular use cases in AI, probably because the range of applications is so broad — across all industries, at several levels.

#2. Transaction search & visualization: Chatbots can also be used in banking to focus on search tasks.

#3. Client Risk Profile: A critical part of banks and insurance companies’ job is the profiling of clients based on their risk score. AI is an excellent tool for this as it can automate the categorization of clients depending on their risk profile, from low to high.

#4. Underwriting, Pricing & Credit Risk Assessment: An AI-powered model can provide an instantaneous assessment of a client’s credit risk, which then allows advisors to craft the most adapted offer.

Manulife, a Canadian financial service group, is the first player in the country to use AI for its underwriting services. The insurance company uses a specific AI, Artificial Intelligence Decision Algorithm (AIDA), which is trained on previous underwriting methods & payouts and can have different classifying processes such as large loss payout or price.

#5. Automated Claims Processes: What the bot does is to take charge of the entire cycle: it walks the customer through the process, step by step, in a conversational format.

#6. Contract Analyzer: Contract analysis is a repetitive internal task in the finance industry. Managers and advisors can delegate this routine task to a machine learning model.

#7. Churn Prediction: Predicting attrition rate can be extremely helpful to take preventive actions. AI can support managers in this mission by providing a prioritized list of clients who show signs of considering to cancel their policy. The manager can then address this list accordingly: give a higher degree of service or improved offering.

#8. Algorithmic Trading — the most advanced ML you will never see.

#9. Augmented research tools: In investment finance, a large portion of time is spent doing research. New machine learning models increase the available data around given trade ideas.

#10. Valuation Models: Valuation models are usually applications for investment and banking in general. The model can quickly calculate the valuation of an asset using data points around the asset and historical examples.

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