AI Powered Banking

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Cross-Sell/ Up-Sell

Let’s first understand what is Cross-Sell and Up-Sell?

Cross-Sell simply means selling multiple products/services to existing/new customers. Up-Sell means selling a service/product of higher value to the existing/new customer. Machine Learning Experts and Domain Experts (We at Act21 have both) together can help you and your business find answers to such questions and even more to help you build strategies around the same.

Customer Churn Prediction

Churn models explain the chances of customer to discontinue their relationship with the existing service or product provider.

This use cases turns out to be important strategymaker in Financial Services domain as it helps the Financial Institutes to assess if the existing customer would take up any other product/service from them or not, and if they take up the new one, would the use both/all/one of them only. These questions once answered also allows you to do Prioritization of Customers, Product Bundling, Customer Segmentation between High Value & Low Value, Customer Life Value and etc.

Fraud Detection

Banks and NBFC’s have started taking the leverage of Artificial Intelligence to monitor transactions and put the doubtful ones into severe scrutiny.

Since ML needs less than a second to assess any transaction making it efficient to detect fraudulent behaviour with very high precision & accuracy, thus preventing frauds in real-time & not after the fraud/crime has been committed.

Algorithmic Trading

Algo-Trading becomes an important use case for Time-Series ML problem.

Where stock prices are predicted by Machine Learning with the help of feature engineering and passing important variables to the model to give scores & predictions. Also with the help of pre-defined criteria sets and analysing historical data, it gives us near to accurate optimum strategies to purchase or sell or hold any share. There are various factors that help in predicting the stock prices, like, company financials, total assets & liabilities, debts, future prospects, market sentiments, competitor analysis, growth, revenue, cash flows, etc.

Credit Risk Scoring

This is the most common use case to which Insurance companies, Banks and NBFC’s look upon.

In laymen terms, it enables you to distinguish between a Good or a Bad Loan. Also it will help you understand about the probability if a company or an individual will default or not and can give you a score as well.

Personal Consumer Experience

PCE is an important aspect to which companies are giving a lot of focus.

This use case answers to the questions like how many issues were raised, how many were solved and by whom and what is the turn-around time, which agent is performing how, which are the repetitive issues, which are the most common type of issue and why is it not being solved permanently, etc.

AI-Powered Credit Risk Scoring

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