Artificial Intelligence, RPA and Intelligent Automation should be seen through the lens of business capabilities rather than transformative technologies.
Artificial Intelligence and Machine Learning are an indispensable takeaway of the modern-day enterprise. Studies estimate that almost 38% of the total businesses under study have used artificial intelligence and machine learning algorithms to detect patterns from their vast volumes of data. Machine-learning applications are increasingly being used in the business parlance to:
- Predict Customer Behaviour
- Detect Financial Frauds
- Analyse for Predictive Maintenance
- Focus on Targeted Marketing
Artificial Intelligence finds its use cases in several domains bringing business transformation. Analytics Insights brings you in-depth analysis of Artificial Intelligence applications in the BFSI, Healthcare, HR vertices-
Artificial Intelligence, ML powered Business Use Cases
- Fraud Detection: Banks and financial services companies use AI applications to detect fraudulent activity through large chunks of financial data to determine whether financial transactions are validated on the basis of customer profiling.
- Conversational AI: BFSI relies heavily on conversational AI to offer chat functionality where customers can talk to an automated support or sales representative for their query resolution. These AI chatbots are programmed vis NLP algorithms to understand human conversations. This lets BFSI professionals readily assist customers in their purchase behaviour, query and complaint resolution.
- Churn Management: Banks and Financial Service companies face customer churn or the shift of their customer base to competitors. Natural language processing and machine learning can help them to understand a customer’s intent of a possible shift. Sentiment analysis can uncover important trends on a customer’s voice level and pitch and detect the micro-emotions that drive the decision-making process. This proves as a trigger to the bank or financial service company to improve its matrices on customer satisfaction levels.
- Targeted Customer Service: BFSI’s leverage Natural Language Processing (NLP) and Computer Vision to identify customers for targeted services using the customer behaviour data available on the social networks. This helps them to detect the nature of their customers’ needs to target whom to sell and what to sell.
- Cyber Security: Cybercrimes have become more sophisticated resulting in higher enterprise investments in cybersecurity to ensure customer data protection adherence. In this case, Artificial intelligence is increasingly deployed for real-time threat detection, mitigation, and prevention. IT and security experts find AI solutions helpful to monitor behaviour, adapt and respond to threats and detect anomalies.
- Pregnancy Management: Machine Learning can be used to monitor mother and foetus health for pregnancy management and uncovering potential health risks and complications quickly. Use of AI and ML have proven to show lower rates of miscarriage and pregnancy-related diseases.
- Patient Data Analytics: Predictive data analytics uncover 3rd party data to discover intelligent insights and suggestive actions. ML algorithms built on diagnostic data have a great potential to lower the mortality rates and increase patient satisfaction levels.
- Customised Medications: Patient data comprises of their genetic profile and medical history records assisting to create a custom medication or care plan. This data acts as indispensable inputs to Artificial Intelligence models to find the best-customised treatment plans for a patient.
- Digital Assistant: Automated RPA bots help HR professionals 24/7 to automate email communication, ensuring all employee queries are answered within the stipulated timelines. Automated emailers are also used in the organisational setup to schedule meetings and track employee’s ongoing day to day activities.
- Performance Management: ML algorithms assist HR managers to manage their employees’ performance This will increase their satisfaction and increase their productivity levels.
- Employee Monitoring: HR analytics monitor employees for better productivity measurement to see how well they function. What more? with the availability of massive amounts of data like previous performance metrics, hours of working and productivity data they are potent tools to forecast an employee’s overall performance in the quarter and the overall financial year.
- HR Retention Management: Predictive ML algorithms can detect the underlying reasons behind employees seeking new opportunities. Thus, predicting which employees are potentially most likely to leave the organisation. Employee management is a tough task, more tough is finding those who fit the organisational culture, AI can surely assist here to manage retention across the enterprise.