Overview of the Application of Artificial Intelligence in the Financial Industry
Generally speaking, artificial intelligence is a new technological science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial intelligence research includes robotics, language recognition, image recognition, natural language processing, and expert systems.
AI is efficient, and it is a very representative multi-disciplinary subject. Artificial intelligence has been applied in various fields, including machinery, electronics, economics, and even philosophy. Machine learning and deep neural networks have more applications. Today we’d like to talk about the applications in the financial industry.
In the banking industry, intelligent chatbots driven by artificial intelligence can provide customers with comprehensive solutions and reduce the workload of call centers. Voice-controlled virtual assistants are also becoming more and more popular. These assistants are usually supported by Amazon’s Alexa and have self-learning functions. They can check balances, account activity, and schedule payments.
Many banks now have applications that provide personalized financial advice and help achieve financial goals. These artificial intelligence-driven systems can record income, daily expense, expenditure behavior and provide financial plans and recommendations. Mobile banking applications can also remind users to pay bills, compete for transactions, and let users interact with banks much easier.
With the substantial increase in digital customer transactions in recent years, reliable fraud detection models are required to protect data sensitivity. Artificial intelligence can be used to strengthen its rule-based model. It can also be used to review consumption history and behavior to highlight abnormal situations, such as a card being used in different global locations in a short period. AI can also learn from human corrections and apply decisions based on what should be emphasized.
All cases of fraud management have different requirements for AI algorithms, and each case applies them in a slightly different way. Transaction monitoring requires faster response time, lower error rate, higher accuracy. Moreover, it requires the high availability and quality of training data.
Common Labeling Tools in Finance:
Common Labeling Types in Finance:
- Financial Text &Speech Classification
- Chatbot Customer Service
- Fraud Detection Regulation Compliance
- KYC Automation
- Customer Survey Feedback Analysis
- Clauses and Entities Extraction
- Text Categorization
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