Artificial Intelligence and Machine Learning are an essential takeaway to modern-day business. Studies report that almost 38 per cent of the total companies under review have used artificial intelligence and machine learning algorithms to identify trends from their large volumes of data. Machine-learning systems are constantly being used in market parlance to:
- Predict Customer Behaviour
- Detect Financial Frauds
- Analyse for Predictive Maintenance
- Focus on Targeted Marketing
Artificial Intelligence finds its usage cases in many areas offering market transformation.
Artificial Intelligence, ML-powered Business Use Cases
BFSI Inc.
- Fraud detection: Banks and financial services firms use AI applications to detect fraudulent activity through large chunks of financial data to determine whether financial transactions are validated using customer profiling.
- Conversational AI: BFSI relies heavily on conversational AI to provide chat features where customers can address their question resolution with automated support or sales representative. These AI chatbots are programmed vis NLP algorithms to understand human conversations. This helps BFSI professionals to assist consumers readily in their purchasing behaviour, question, and complaint resolution.
- Turnover Management: Banks and Financial Service firms face customer churn or the transfer of their customer base to rivals. Natural language processing and machine learning can help them understand the intent of a possible shift being made by a client. Sentiment analysis can reveal important trends on the level and pitch of a customer’s voice and detect the micro-emotions that drive the decision-making process. This proves that the bank or financial service company is a trigger for improving its matrices on customer satisfaction levels.
- Targeted customer service: BFSI leverage Natural Language Processing (NLP) and Computer Vision to identify customers for targeted services using the data on the social networks’ customer behaviour. This helps them detect the essence of the needs of their customers to target for whom to sell and what to sell.
- Cyber Security: Cybercrimes have become more sophisticated, leading to higher investment in cybersecurity by companies to ensure adherence to customer data protection. In this case, Artificial Intelligence is increasingly being deployed to detect, mitigate and prevent real-time threats. IT and security experts consider AI solutions that are helpful in tracking behaviour, adapting and reacting to threats and detecting anomalies.
2. Healthcare
Pregnancy management: Machine learning can be used to monitor the health of mothers and fetuses for pregnancy management and to rapidly recognise possible health threats and complications. The use of AI and ML has proven to show lower rates of miscarriage and diseases related to pregnancy.
Patient Data Analytics: Predictive data analytics uncover data from third parties to discover smart insights and suggestive actions. Based on diagnostic data, ML algorithms have great potential to reduce mortality rates and increase patient satisfaction.
Customized Medicines: Patient data contain their genetic profile and details of medical history to help establish a customized prescription or treatment plan. This data serves as important inputs to the Artificial Intelligence models to identify a patient’s best-customized care plans.
3.HR
Digital Assistant: Automated RPA bots help HR professionals automate email communication 24/7, ensuring that all employee requests are answered within the specified timeframes. In the organizational system, automatic emailers are often used to schedule meetings and monitor the day-to-day activities of the employees.
Performance management: ML algorithms help HR managers monitor the performance of their workers This will increase their satisfaction and increase their levels of productivity.
Employee monitoring: HR analytics track workers to see how well they function for better assessment of productivity. What else? With the availability of massive data such as previous performance metrics, working hours and productivity data, they are powerful tools for forecasting the overall performance of the employee over the quarter and overall financial year.
HR Retention Management: Predictive ML algorithms can detect the underlying reasons behind employees seeking new opportunities. Therefore predicting which workers are likely to exit the company most likely. Employee management is a tough task, finding those who fit the organizational culture is more difficult, AI can certainly help manage retention throughout the company.
