Cyber-threat and fraud detection
Advancement in technology means more sophisticated tools and techniques for cyber-threat and fraud and these activities are dominant threats in the banking and finance industries affecting the institution and its consumers, the public.
With AI in banking and finance, fraudulent activities can be detected in real-time, threats addressed, and the safety of financial assets is enhanced.
One of the main benefits of AI-powered fraud detection systems is their ability to identify patterns and anomalies in transaction data. These systems can use machine learning algorithms to analyse transaction data and identify patterns that are indicative of fraudulent activity, such as unusual purchase amounts or locations. The algorithms can also learn from past fraudulent activity to improve their accuracy over time.
Another benefit of AI-powered fraud detection systems is their ability to monitor customer behavior for suspicious activity. These systems can use machine learning algorithms to analyse customer data, such as login times, IP addresses, and device types, to identify patterns that are indicative of fraudulent behavior. The systems can also use biometric data, such as facial recognition or voice recognition, to verify the identity of customers and prevent identity theft.
AI-powered fraud detection systems can also provide real-time alerts to bank staff, enabling them to quickly respond to fraudulent activity. For example, if a system detects a fraudulent transaction, it can send an alert to a bank’s fraud department, which can then take action to prevent further fraudulent activity.
Credit Analysis and Dynamic Risk-Based Pricing for Loans
Credit analysis and dynamic risk-based pricing for loans that evaluate a borrower’s creditworthiness and determine appropriate interest rates. AI can help improve the accuracy of credit analysis and enable dynamic risk-based pricing, which can benefit both the financial institution and the borrower.
Credit analysis using AI involves analysing a vast amount of data, including credit scores, payment history, income, employment history, and other factors that may impact a borrower’s ability to repay a loan. By analysing this data, AI algorithms can create a more accurate picture of a borrower’s creditworthiness and determine the likelihood of loan default.
Dynamic risk-based pricing is a pricing strategy that allows financial institutions to set loan interest rates based on the borrower’s risk profile. By using AI to analyse data, financial institutions can offer lower interest rates to borrowers with a lower risk of default, while charging higher rates to borrowers who are deemed to be at a higher risk. This approach can help financial institutions to reduce losses due to loan defaults and can also enable them to attract borrowers with lower credit scores who may have previously been denied loans.
Customer Experience and Growth
AI helps banks deliver better customer experience and drive growth by having the ability to offer more personalised and efficient services to their customers.
Service improvements and customer service is among the areas in service which has greatly improved because of AI. AI-powered Chatbots and Virtual Assistants provide 24/7 customer service, answering common questions and providing assistance with simple tasks. This reduces wait times and improves customer satisfaction.
AI algorithms can analyse customer data to identify patterns and preferences, allowing banks to offer personalised services to each customer. For example, AI can recommend customised financial products, suggest investment opportunities, and provide targeted marketing campaigns that are more likely to resonate with each customer.
