Ten Applications of AI to Fintech

Such service increases user engagement and improves the overall experience of the user with the financial product they are interacting with.Digital assistants can be built using Natural Language Processing (NLP), a type of machine learning model that can process data in the format of human language..Client Risk ProfileA 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.Building on the categorization work, advisors can decide to associate financial products for each risk profile and offer them to clients in an automated way (product recommendations).For this use case, classification models such as XGBoost or Artificial Neural Network (ANN) are trained on historical client data and pre-labeling data provided by the advisors, which eliminates data-induced bias.#4..Underwriting, Pricing & Credit Risk AssessmentInsurance companies offer underwriting services, mainly for loans and investments.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.Using AI for underwriting services increases the efficiency of the proposals made and improves the client experience as it speeds up the process and turnaround time of such operations.Manulife, a Canadian financial service group, is the first player in the country to use AI for its underwriting services, making it “faster for many Canadians to buy basic life insurance, a key to addressing the “protection gap” in Canada.”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.The application of this method is not cantoned to insurance; it can also be used on credit scoring for loans.#5..The bot can at this point calculate and propose payout amounts, based on a payout predictor model it has been trained on.This application is a three in one machine learning solution that holds the potential to relieve a high pain point in the industry.It is what Lemonade, a New York-based insurance startup, has set as a mission..Formulas can be added to the model such as “if this box is checked then this one should be blank.” The model can be trained on existing contracts and learn how to behave with such content.In this case, the accuracy of the model’s outcome is remarkably high because of the repetitive nature of contracts.JP Morgan has harnessed the power of this application of AI, leading to freeing 360,000 hours (yearly) from its employees’ load in only a few seconds.These solutions support contract-related analysis, while blockchain-based smart contracts, a paradigm-shifting upgrade to contracts management, are being more widely adopted.#7..By processing consumer data, banks can serve them better by adopting their offering and pricing.The model used is a classification one trained on historical data of clients who have canceled their policy and others who have remained after considering leaving the institution.A research paper about customer churn prediction for the banking industry showed the importance of consumer research versus mass marketing for this specific industry:The mass marketing approach cannot succeed in the diversity of consumer business today..Machine learning algorithm excels in analyzing data, whatever its size and density.The only prerequisite is to have enough data to train the model, which is what trading has in abundance (market data, current and historical).The algorithm detects patterns usually difficult to spot by a human, it reacts faster than human traders, and it can execute trades automatically based on the insight derived from the data.Such a model can be used by a market-maker looking for short-term trade based on quick price movement..New machine learning models increase the available data around given trade ideas.Sentiment analysis can be used for due diligence about companies and managers..Valuation ModelsValuation 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..These data points are what a human would use to value the asset (ex: the creator of a painting), but the model learns which weights to assign to each data point by using historical data.This model was traditionally used in real estate where the algorithm can be trained on previous sales transactions.. More details

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