Role Of Ai And Ml In Financial Industry | Yantra Inc

By Author: Yantra Inc
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The Finance industry has seen a steep rise in the number of use cases of AI and ML applications to create better outcomes for both consumers and businesses. From lead generation, customer onboarding, and optimizing portfolio composition and management, to speeding up the underwriting process, performing model validation, detecting fraud, and assist the credit operations team with reporting, operational management within Finance has a lot to gain from AI and ML. Want to learn more about the different use cases? Read on!
Use Cases of AI and ML in Finance Operational Management
Customer Onboarding
Customer onboarding is usually the first operational process that new clients go through when they first sign up at a bank or financial institution. The onboarding experience is important because it gives new clients a first initial impression of the relationship they will have with the organization. Onboarding increasingly takes place digitally, so it is important to have the means to measure and aggregate data points collected before, during, and after customer onboarding. For example, collecting data on how many times ...
... a client views a particular onboarding video or view a particular webpage can be used to improve the onboarding process. Imagine all the rich, useful insights that could be gained from having that data at your fingertips!
AI and ML can be used to study patterns of user activities on the company website and across social media platforms, telephone conversation analytics, and in-person branch visit metrics. These data can then be aggregated to create models to predict new client behaviours, such as the finding that new clients prefer to complete onboarding online or using mobile phone as opposed to in person, so website and mobile tools should be enhanced to ensure a smooth onboarding process.
Fraud Monitoring, Detection, and Prevention
Do you ever wonder how banks determine which activities appear to be fraudulent, as you receive those “potential fraud” alerts on your mobile phone from time to time? Financial institutions routinely collect data on customer transactions. Machine learning algorithms can be used to monitor, detect, and prevent fraud. It does this by examining historical payment data and every action performed by each cardholder. Any deviation or sudden anomaly would be marked as suspicious, and an automated hold on a credit card transaction or large amount of withdrawal could be made without any manual effort. These machine learning models have been used by most large financial institutions around the world, and they have been proven to be able to detect suspicious behavior with high precision. So rest assured, your bank likely has the tools and technology in place to keep you safe from possible fraudsters and hackers (although you still need to practice good habits to protect your data to avoid having your financial and personal information compromised).
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