To say that artificial intelligence (AI) and machine learning (ML) are transformative technologies would be an understatement. A recent Deloitte survey of IT and line-of-business executives found that 86% of financial services AI adopters believe AI will be critical to their business’s success in the next two years.
AI can assist in improving efficiency, enabling a growth agenda, increasing differentiation, managing risk and regulatory requirements, and positively influencing customer experience.
Organizations are making targeted investments in areas such as cloud, big data platforms, and data applications that use updated architecture (e.g., microservices and event hubs), thereby eliminating the need for up-front capital investment in order to develop, deploy, and scale AI solutions.
Banks will need to invest (or are investing) more than ever to personalize the services offered to customers and, in turn, retain their trust and loyalty. Banks must employ data driven AI capabilities to conduct micro segmentation of existing customers and prospects. This level of granularity can help banks more accurately predict customer and prospect needs and behaviors. A large bank has recently used data-driven AI to offer personalized reward programs (related to travel, shopping, etc.) by predicting customers’ redemption preferences.
AI agents can engage in personalized discussions by tapping into data sources that include customer data, social media, current economic conditions, historical customer information, call center patterns, and more. In addition, AI can help improve operational efficiencies in areas such as routing customer calls and calculating appropriate customer hold times.
The COVID-19 pandemic exacerbated several current underwriting concerns, namely volume, speed, and risk. Robotic process automation and ML models and varied data sources can expedite the loan underwriting process and improve risk assessment. This process can be expedited by automating document scanning and manual processes involved to gather relevant data. ML models can run on the data gathered from multiple data sources (e.g., social media posts and third-party data) and can be used to accurately assess borrowers’ risk and quickly make loan decisions.
Customers are delinquent for many reasons pandemic-related job losses, a simple missed payment due to lack of reminders, change of address. As such, banks must customize their outreach, especially during uncertain economic times. AI can drive efficiency and create preemptive strategies to help customers and lenders. Banks can benefit by leveraging customer data to identify warning signals for possible delinquencies and defaults, predict why customers might miss payments, and offer customized solutions to catch up.
Banks spend a lot of money to comply with government rules and regulations. Banks can create efficiencies—and save money—by leveraging AI to automate labor-intensive compliance processes and automatically detect regulatory changes to ensure they remain in compliance. Initial regulatory reporting configurations can take years of effort and require continual manual supervision to stay current with evolving regulations. The time frame and effort level can be reduced if banks use AI in the setup process. Deep learning and natural language processing can help shorten implementation time frames by reading compliance requirements from regulatory websites, notifying banks about updates, and incorporating changes automatically in the systems that generate reports.
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