AI is becoming increasingly important as organizations automate their day-to-day operations and understand the COVID-19 affected datasets. It can be leveraged to improve the stakeholder experience as well.
FREMONT, CA: Banks can utilize artificial intelligence to manage record-level, high-speed data and gain valuable insights. Additionally, digital payments, AI bots, and biometric fraud detection systems contribute to high-quality services for a more extensive consumer base. Machine Learning, Natural Language Processing, Expert Systems, Vision, Speech, Planning, Robotics, and other technologies fall under artificial intelligence.
The following are five Artificial Intelligence applications that are wreaking mayhem in the banking industry:
Robo Advice: One of the most contentious issues in the financial services industry is automated guidance. By evaluating data supplied by customers and their economic history, a robo-advisor tries to assess their financial health. The robo-advisor will be able to provide appropriate investment suggestions in a particular product class, even down to a specific product or equity, based on this research and the client's goals.
Customer service/engagement (Chatbot): Chatbots have a significant return on investment in cost reductions, making them one of the most widely deployed AI applications across industries. Most routinely used tasks, including balance inquiries, viewing micro statements, and fund transfers, may be handled successfully by chatbots. This helps to lessen the pressure on other channels like call centers, internet banking, etc.
Credit Scoring / Direct Lending: By evaluating data from various standard and non-traditional data sources, AI can assist alternative lenders in determining a client's creditworthiness. This enables lenders to create innovative lending systems backed by a robust credit scoring model, even for persons with a low credit history. Affirm, and GiniMachine is two notable companies.
General Purpose / Predictive Analytics: General-purpose semantic and natural language applications, as well as generally applicable predictive analytics, are two of AI's most prevalent use cases. Traditional technologies are incapable of identifying specific patterns in data that AI can. These trends could point to untapped sales prospects, cross-sell opportunities, or even operational data indicators, all of which could result in a direct revenue impact.
Cybersecurity: By utilizing data from previous threats and understanding patterns and signs that appear unrelated to forecast and prevent assaults, AI can significantly improve the effectiveness of cybersecurity systems. In addition to mitigating external risks, AI can detect internal threats and recommend compliance measures to prevent data theft.
