The future of AI in banking

The importance of AI in banking

It would be an understatement to suggest that artificial intelligence (AI) and machine learning (ML) in banking are transformative technologies. According to a recent Deloitte survey of IT and line-of-business executives, 86% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next two years. So, what should banks do to keep current with AI marketplace trends and build with confidence into the future?

While the banking sector has long been technology-dependent and data-intensive, new data-enabled AI technology has the capability to drive innovation further and faster than ever before. AI can help improve efficiency, enable a growth agenda, boost differentiation, manage risk and regulatory needs, and positively influence customer experience. Building sophisticated AI systems was once expensive, restricting deployment to key use cases (e.g., high-frequency trading). Deloitte’s recent AI survey of IT and line-of-business executives of companies that have adopted AI technologies found that, from a technology perspective, cost and other barriers to adoption are falling, and it is becoming easier to implement and integrate AI technologies.

Organisations 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), eliminating up-front capital investment needed specifically to develop, deploy, and scale AI solutions. However, multiple operational and organisational challenges remain, notably skills gaps and the integration of AI into the wider organisation, to name two examples.

Banking reimagined with AI

As banks consider the pros and cons of a broader enterprise AI strategy, use cases can be instructive in decision-making. By focusing on use cases like the ones that follow, executives can make informed decisions that can help tailor deployments to their circumstances, yielding a better return on investment.

While these examples are by no means exhaustive, they demonstrate that data-driven AI can be used in many ways to generate additional value across a banking organisation—from front-office revenue growth to back-office operational efficiencies.

• Customer experience and growth: Banks can employ data-driven AI capabilities to conduct microsegmentation of existing customers and prospects. This level of granularity can help banks more accurately predict customer and prospect needs and behaviours.
• Service optimisation: Conversational AI agents can engage in personalised discussions by tapping into data sources that include customer data, social media, current economic conditions, historical customer information, call centre patterns, and more. In addition, AI can help improve operational efficiencies in areas, such as routing customer calls and calculating appropriate customer hold times.
• Underwriting: 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 and can be used to accurately assess borrowers’ risk and quickly make loan decisions.
• Collections and recovery: AI can drive efficiencies and create preemptive strategies to help customers and lenders alike. Banks can benefit by leveraging customer data to identify warning signals for possible delinquencies and defaults, predict why customers might miss payments, and offer customised solutions to catch up.
• Regulatory and risk assessment: Banks can create efficiencies—and save money—by leveraging AI to automate labour-intensive processes and automatically detect regulatory changes to ensure they remain in compliance.

Shifting to full-scale AI implementation in banking

Much like the evolution of cloud platforms in recent years, banks must move beyond the hype and consider the practical applications of AI. While there are proven examples of effective applications, many banks still consider AI to be experimental, with many of their pilot programs never moving into full-scale implementation. Banks must consider their artificial intelligence and machine learning approach and invest in an AI implementation journey for successful outcomes. Here are critical focus areas, across six steps, where banks may need to evolve their processes to be successful on their journey:

• Step 1: Develop an AI strategy: Shift from just using AI capabilities to being an AI firm and addressing the how of execution.
• Step 2: Define a use case–driven process: Focus on business value-driven use cases and investing in diverse AI capabilities instead of focusing on limited AI solutions.
• Step 3: Experiment with prototypes: Shift from providing a concept to laying a foundation and prepare for strategic alignment.
• Step 4: Build with confidence: Move from a reactive mindset to a proactive focus on risks and ethics and explore new partnerships while balancing convergence
• Step 5: Scale for enterprise deployment: Change the “nice-to-have” AI talent list to a “must-have” list and shift from rigid to adaptive technology and operating models that introduce nimbleness across the organisation.
• Step 6: Drive sustainable outcomes: Go beyond only implementing AI to discovering how to enhance capabilities and generate additional business value from deployed applications.
  

An AI-enabled future

The growing adoption of AI promises to have a lasting impact on the banking industry. Even though banks must still overcome significant operational and organisational challenges, they are making great strides forward in implementation and adoption. To realise the full benefits of AI, banks must stay the course today and continue to build the technological foundations and processes necessary to move forward into the future.