Artificial Intelligence and Banking: 3 Essential Uses
The word essential is not to be thrown around but when it comes to the uses of AI in banking, it’s not at all an exaggeration to say there have been essential usage applications.
Here are 3 examples.
Fraud Detection and Prevention
Primarily, fraudulent activities pose a significant threat to both banks and customers, often leading to financial losses and erosion of trust. But AI is a security measure here; swiftly identifying and responding to suspicious transactions.
How? Primarily AI algorithms analyze large datasets of transaction history, often considering factors such as transaction frequency, location, and amounts. Then they learn patterns of regular behavior and raise alerts when deviations occur. Plus, machine learning models evolve by adapting to new fraud tactics, making them more effective over time.
Say a customer makes transactions within their home country. If an AI system detects a sudden large withdrawal in a different country, it might flag this as a potential fraud, sending an instant alert to the customer for verification or even blocking the transaction until further confirmation.
Customer Service Chatbots
Even in the banking sector, customer service is crucial for retaining clients, and a Chatbot in Banking means instantaneous and personalized assistance; handling routine queries, providing quick access to account information, and really freeing up human agents for more complex tasks.
How? Natural Language Processing (NLP) enables chatbots to understand and respond to user queries in a human-like manner as they can access customer data, and transaction history, and provide relevant information.
Say a customer, John, wants to check his account balance. Instead of navigating through menus or waiting on hold, he engages with the bank’s chatbot via a messaging app. Using NLP, the chatbot understands John’s request, retrieves his account information, and promptly responds with the current balance.
Credit Scoring and Risk Assessment
Unfortunately, traditional credit scoring models may not capture the full financial picture of an individual, even limiting access to credit for some. AI-driven credit scoring can consider a broader range of data for more accurate assessments and financial inclusion.
How? AI algorithms analyze various data sources, often including transaction history, social media activity, and even online behavior. Machine learning models then identify patterns indicative of creditworthiness and default risks. This holistic approach often allows for a more nuanced evaluation.
Say Sarah, a young entrepreneur, has a limited credit history but a strong online presence showcasing her business acumen. AI-based credit scoring may take into account her social media engagement, positive online reviews, and consistent cash flow from digital transactions. As a result, Sarah is granted a business loan, which might have been overlooked by conventional credit scoring methods.
Bringing Artificial Intelligence into banking is a big deal. It helps stop fraud, makes talking to customer service easier with smart chatbots, and even changes how banks decide if you can get a loan. This AI stuff isn’t just about technology; it’s making banks safer, friendlier, and fairer for everyone.