Three user cases of artificial intelligence and machine learning in the BFSI industry

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Artificial intelligence and machine learning are an indispensable contribution of the modern company. Studies estimate that almost 38% of total companies use artificial intelligence and machine learning algorithms to detect patterns from their vast volumes of data. Machine learning applications are increasingly being used in business language to:

  • Predict customer behavior
  • Detect financial fraud
  • Analyze for predictive maintenance
  • Focus on targeted marketing

 

Artificial Intelligence, ML-Based Business Use Case at BFSI

 
  • Fraud detection and conversational artificial intelligence
    Banks and financial services companies use artificial intelligence applications to detect fraudulent activity through large amounts of financial data to determine whether financial transactions are validated based on the customer profile. BFSI relies heavily on conversational artificial intelligence to offer chat functionality where customers can speak to automated support or sales representative to resolve their queries. These AI Chabot’s are programmed with NLP algorithms to understand human conversations. This enables BFSI professionals to easily assist clients in their purchasing behavior, inquiries, and complaint resolution.

  • Management
    Banks and financial services companies face losing customers or shifting their customer base to the competition. Natural language processing and machine learning can help them understand the customer’s intent for a potential change. Sentiment analysis can uncover important trends in a customer’s tone and tone of voice and detect the micro-emotions that drive the decision-making process. This proves to be a trigger for the bank or financial services company to improve their matrices on customer satisfaction levels.

  • Targeted Customer Service
    BFSI leverages natural language processing (NLP) and computer vision to identify customers for specific services using customer behavior data available on social media.
    This helps them to detect the nature of their customers’ needs, which later helps them to target who to sell to and what to sell.

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