AI have already made a place in our day-to-day lives, but their headway into the business world has been slow until now. While the adoption of AI is rising, the primary use cases are limited to customer and operational support. However, AI analytics has a huge potential to drive decision-making in most industrial applications, especially banking and finance.
Banking can pursue AI implementation more successfully due to its access to larger and richer databases. As the wave of digital transformation rolls out, banks are taking this opportunity to implement data technologies to support AI. However, supporting decisions with AI requires a comprehensive integration of data from all aspects of the business. AI can then directly run predictive analytics, real-time monitoring, and control processes.
Decision-making using AI can be based on two formats: aided decision-making, where AI provides insights and recommendations to help decision-makers; and automated decision-making, where AI can act on behalf of the organization and makes decisions. AI-powered decision-making turns the responsiveness of a business into predictive and preparatory practice. Here are the most important aspects of AI-enabled decisions.
Enhanced Customer Relationships and Customer Lifetime Value
Customer-facing functions can greatly improve their service experience with AI. From predictive analytics to personalized communications and support, AI can enhance customer lifetime value through better relations and revenue generation. Advanced analytics with AI is performed in real-time and parallel to understand customer needs and deliver the right service at the right time. Predictive analytics also helps retain customers. This can enable a highly personal and need-based customer engagement not limited by resources to cater to every customer as per their behavior.
Lowering Risks and Costs of Business Critical Decisions
Banks are involved in many financial transactions, from loans, investments, and other services for their customers. This enables the banks to earn and provide returns to their customers but also involves significant risks. Predictive analytics and advanced analytics can monitor such risks and costs to recommend actions or perform them autonomously to reduce the overall cost of these activities. This is especially true for loans and investments where the risk of defaulting and losses can be significant if not approached correctly. AI can be used as a tool to assist in these decisions.
Innovations and Experimentation with New Products and Services
Today’s markets are highly competitive with changing demands for new products and services. These market changes can be predicted by AI and even come up with solutions. AI can provide multi-faceted solutions to these challenges and help prepare for the future. However, new products and services can not be rolled out on demand without significant testing and business analysis. This is where AI can help test the products internally and externally in a controlled environment to assess the business performance of the product. With agile DevOps, AI can help banks remain competitive through new offerings without taking considerable risks. More importantly, banks can be prepared to deal with continuously changing market conditions.
SLK is a technology consultant with data, analytics, and AI products developed in-house. Our clients include Fortune 500 companies that have leveraged the power of AI/ML in their smart systems to drive business goals. We have delivered technological solutions with an ROI-led approach to tackle key business challenges through technology and AI.
Decisions in a business environment are always based on research and data. Manually performing these analyses can be time-consuming, costly, and might incur opportunity costs. However, using an expansive data library, AI can power this process with real-time analytics capabilities. The analytics are more holistic and present a clearer picture for making decisions. AI assistance in all aspects of the business has become a long-term vision for most banking institutions. We are in an early (sweet spot) phase of adoption which will benefit early adopters much more than later-stage adoption because of the mature technology that remains unused by competition.