Top 4 Ways AI Rewriting the Prospects for Fintech

Banking CIO Outlook | Friday, July 19, 2019

In the finance industry, firms run and thrive on innovations and customizations, which are derived from cutting-edge technologies.

FREMONT, CA: Fintech is synonymous to innovations and high accessibility in the world of finance. Equipped with Artificial Intelligence (AI), the core purpose of fintech is enabling the masses to get benefits from the mainstream financial system. Being aware of these market dynamics, banking institutions are adopting AI and its applications.

· Digital Financial Advisor

The range of AI applications are broad and lies across all industries ar several levels. Transactional bots are one among the famous use cases of AI. In finance, transactional bots are used to offer users finance coaching or advisory services. Digital assistants are designed using Natural Language Processing (NLP), a type of machine learning model that can process information in the format of human language.

· Client Risk Profile

A critical part of financial institutions and insurance companies’ 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. Building on the categorization activity, advisors can decide to associate financial products for each risk profile and offer them to clients in the form of automated product recommendations.

· Intelligent Prediction

AI can support financial managers by providing a prioritized list of clients who show signs of considering to cancel their policy and helps to address this list accordingly. This gives a higher degree of service or enhanced offering. Banks can serve them better by adopting their offering and pricing by processing consumer data.

· Augmented Research Tools

A large portion of time is spent researching the financial sector. New AI and machine learning models are increasing the available data on given trade ideas. Sentiment analysis can be used as it allows an analyst to understand the tone of large sets of text data. It can also provide insight into how a financial manager reflects their organizational performance.

Customers demand more transparency when it comes to money-related operations. The involvement of technology is not just a choice but a need for financial institutions.

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