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Impact of AI in banking sector
This is a user generated content for MyStory, a YourStory initiative to enable its community to contribute and have their voices heard. The views and writings here reflect that of the author and not of YourStory.
Artificial Intelligence is one of the fastest evolving technology and has been widely used by various sectors and banking are never the ones that are left behind when it comes to tech adoption. Customer experience and digital transformation as the topmost priority, banks are adopting AI to deliver the next-gen services to their customers. AI has introduced several cognitive technologies, including machine learning applications, such as natural language processing (NLP) and natural language generation (NLG), that allow AI systems to analyze data and make more-informed real-time decisions.
There are a wide variety of applications for AI in financial services. Following are few main applications of AI in the finance sector:
Building a better customer experience: Banks adopting AI systems will have a clear idea regarding their customer needs and preferences, giving them the ability to improve their services to attract new customers. Banks can use AI to customize products based on their users need and analyzing their behavioral patterns by combining information from transaction histories, queries, search history and even social media sites. One of the AI forms that banks are widely using is chatbots. They are often used to help customers make payments, check their balances, transfer money and even suggest products based on their search history. With the adoption of AI, banks will have the ability to understand their customer’s behaviors and improve customer experiences by providing faster and more personalized services.
Reducing costs:
Banks are reducing cost for low-value-add activities by using robotic process automation (RPA). It also improves operational efficiency. With the evolving technologies, banks are now using AI to reduce time and resources spent on developing are numbers of processes. AI-driven RPA will help in fixing problems that occur in multiple ledgers, systems and subsystems. Few banks are leveraging natural language processing (NLP) to determine the applicability of regulations and identify gaps between regulatory requirements and internal policies and procedures.
Risk management: Analyzing historical data, conducting risk analysis and eliminating human errors will help in delivering customized products to clients. Banks are adopting AI to identify patterns that often lead to regulatory inquiries. The system analyzes previous compliance complaint issues and learns to recognize similar patterns in the bank’s current data and alerts the team about issues as they occur, thereby providing opportunities for the banks to rectify the issues before they self-report to the regulators.
Fraud prevention and detection:
Financial service sectors are highly adopting automated proactive fraud detection solutions across entire banking. Anomalous detection can be used to increase the accuracy of credit card fraud detection and anti-money laundering. Anti-money laundering (AML) are difficult to identify in capital markets. The application of AI, networks and context through data have solved this complexity.
Undoubtedly AI provides greater opportunities for banks in the field of improving customer experience, fraud detection, and cost reduction.