Artificial Intelligence (AI) and Machine Learning (ML) can help financial service companies improve the customer experience by providing faster, more efficient service and personalized recommendations, while also streamlining internal processes and reducing the risk of fraud.
In this article, we will explore some of the ways that AI and ML can be used to achieve these goals, and discuss some of the considerations that financial service companies should take into account when implementing these technologies.
Improved Customer Experience with AI & Chatbots
One of the key ways that financial service companies can use AI and ML is to improve the customer experience. Chatbots, can provide 24/7 support to customers by answering common questions and directing them to relevant resources. There are several considerations for using chatbots in financial operations as part of AI posture:
- Ensuring accurate understanding and response to customer inquiries: This can be achieved through the use of natural language processing (NLP) techniques, which enable the chatbot to understand and respond to questions written in a way that is similar to how a human would.
- Expansion of topics and line-of-thinking, including escalations: Ensure that the chatbot has been trained on a wide range of topics and is able to provide accurate and helpful responses to a variety of customer inquiries. While chatbots can handle many common customer inquiries, there may be times when a customer has a more complex or specialized question that requires the attention of a human representative. In these cases, the chatbot should be able to escalate the inquiry to a human representative as needed.
- Providing a clear and easy-to-use interface: Customers should be able to easily access the chatbot and understand how to use it. This may involve providing clear instructions and prompts to guide the customer through the process.
- Maintaining the privacy and security of customer information: Chatbots represent a progressive leap but only if they are designed and implemented in a way that maintains the privacy and security of customer information. This may involve implementing measures such as encryption and secure data storage.
Operational Efficiency with ML
There are a few general uses for machine learning already “in the wild” at financial services companies. In most cases, these are still fledgling endeavors but even so, a pattern of usage can fall into these common categories and considerations. These can include:
Predictive analytics: Analysis of customer data and create predictions about their needs and preferences. This can be used to provide personalized recommendations and offers to customers.
Fraud detection: Analyzing transactions and identifying patterns that may indicate fraudulent activity.
Risk assessment: Evaluate and assess risk decisions about loan applications and other financial products.
Process automation: Routine tasks, such as data entry and paperwork processing, can be automated to free up staff to focus on more complex tasks.
There are several considerations when analyzing data and informing decisions, especially when based on customer data:
- Data Accuracy: ML relies on data to make predictions, so it is important to ensure that the data being used is accurate and relevant. This may involve cleansing and preparing the data before it is used for analysis.
- Choosing the right algorithm: There are many different algorithms that can be used for ML and it is important to choose the one that is most appropriate for the specific problem being addressed. This may involve testing different algorithms to see which one performs best.
- Actionable predictions: The predictions made by the ML model should be actionable and relevant to the business problem being addressed. This may involve identifying key performance indicators (KPIs) and setting targets for the model to aim for.
- Protecting customer privacy: Financial services companies should ensure that they are compliant with relevant privacy regulations, such as the General Data Protection Regulation (GDPR), when using ML to analyze customer data. This may involve obtaining consent from customers before collecting and using their data.
- Iterating the model: It is important to regularly monitor the performance of the ML model and make updates as needed to ensure that it continues to provide accurate and relevant information.
In conclusion, the use of artificial intelligence (AI) and machine learning (ML) can bring significant benefits to financial service companies by improving customer service, streamlining processes, and reducing the risk of fraud.
Chatbots, predictive analytics, fraud detection, risk assessment, and process automation are all examples of how AI and ML can be used in the financial industry. However, it is important to carefully consider factors such as the accuracy of the data, the appropriate algorithm to use, and the privacy and security of customer information when implementing these solutions. By carefully considering these and other factors, financial service companies can effectively leverage AI and ML to improve the customer experience and drive business success.