How AI Is Disrupting And Reshaping The Future Of Banking
AI-powered predictive analytics are becoming valuable tools for banks to improve their operations and provide better customer services.
The banking industry has always been at the forefront of adopting new technology. Driven by the need to stay competitive and meet evolving demands, banks have transformed with cutting-edge technologies — especially artificial intelligence (AI) — that enhance customer experience, streamline operations, detect fraudulent activities, reduce costs and other novel use cases.
The potential cost savings for banks from AI applications is estimated at $447 billion, according to Autonomous Next research. UBS Evidence Labs reports that 75 percent of banks with over $100 billion in assets currently implementing AI strategies in some form. Another survey by Opentxt reports that 80 percent of financial institutions have a high awareness of the potential benefits of using AI. AI use cases have already evolved to gain prominence across the sector, while new ones promise to revolutionize it in the coming years. Here is a roundup of the state of AI adoption within banking processes:
Service Automation
Chatbots and virtual assistants on web and mobile apps that automate banking services are now very widely implemented. Procedural requirements and rule-based processes such as loan approvals and credit checks are also being automated with integrated data analytics and decision algorithms. This has given banks an edge by providing faster and efficient service, reduce operational costs leading to better customer satisfaction and loyalty, and improving business performance.
Risk Management
The sheer volumes of data that banks handle makes manual analysis difficult. AI-powered technologies identify patterns and anomalies in data in real-time, enabling banks to detect and take swift proactive action to prevent financial loss or potential harm to their customers.
Personalized Offers And Services
By analyzing customer data, AI algorithms create precise customer segments and discover their needs. These insights are used to run targeted marketing campaigns, contextual product recommendations, offer customized investment recommendations and financial planning services tailored for individual customers.
Compliance
AI-powered compliance checks have the ability to monitor transactions for money laundering, terrorist financing and other financial crimes, thereby ensuring that banks stay within the defined regulatory boundaries and prevent potential violations.
While these use cases have already found wide adoption, here are some others that are slowly breaking grounds:
Cybersecurity
Banks are prime targets for cyber-attacks for sensitive customer information. AI-powered cybersecurity solutions can continuously monitor and analyze large volumes of data, identify potential threats or anomalies and quickly respond to security breaches. AI can also help banks strengthen their authentication processes through biometric identification and advanced analytics that prevent identity fraud, essential for banks for safeguarding their customers' information and maintaining their trust.
Wealth Management
AI-driven solutions are being designed to offer tailored investment advice and portfolio management services. Advanced algorithms analyze customer data, recognize profitable investment avenues and provide recommendations that align with customers' risk appetite and investment preferences. With this analysis, Relationship managers deliver more personalized and effective investment strategies, and optimize returns for clients.
Predictive Analytics
AI-powered predictive analytics are becoming valuable tools for banks to improve their operations and provide better customer services. Machine learning algorithms analyze vast amounts of data to gain insights into customer behaviour, identify patterns and trends, and predict future outcomes. Banks can use AI and predictive analytics for a variety of purposes, such as fraud detection, credit risk assessment, customer segmentation, and personalized marketing. This enables them to make data-driven decisions that improve their business processes, reduce risks, and increase profits.
In conclusion, using ML and NLP, banks are automating routine tasks, personalizing services, and making data-driven decisions that optimize operations, elevate CX and minimize risk. Adoption of AI is however not without challenges, like ethical considerations, algorithmic bias and the need for continuous learning and adaptation, but its potential to transform banking is undeniable. As AI technology advances, it is expected to have a deep and profound effect on the future of banking through innovative applications that will shape the banking industry.
(The author is the Principal Solution Architect at Intellicus Technologies, a business intelligence reporting tool and analytics platform)
Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.
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