The banking industry is undergoing a rapid transformation as artificial intelligence (AI) becomes more widely adopted and integrated into various aspects of financial services. From customer service to risk management, AI is enabling banks to improve efficiency, reduce costs, enhance customer satisfaction, and gain a competitive edge.
To learn more about how AI is changing the banking landscape, Bank Automation News spoke with Dustin Hubbard, the president of Archway Software, a SaaS company that provides digital innovation solutions for financial institutions. Hubbard shared his insights on the current and future applications of AI in banking, the challenges and opportunities of implementing AI, and the best practices for ensuring safety and compliance.
AI use cases in banking
Hubbard said that AI can be applied to various domains within banking, such as:
- Conversational AI: This refers to the use of natural language processing (NLP) and natural language understanding (NLU) to create chatbots, voice assistants, and virtual agents that can interact with customers and employees through natural language. Conversational AI can help banks provide 24/7 service, answer common queries, resolve issues, offer personalized recommendations, and cross-sell products.
- Predictive analytics: This refers to the use of machine learning (ML) and data mining to analyze historical and real-time data and generate predictions, insights, and recommendations. Predictive analytics can help banks optimize pricing, marketing, sales, credit scoring, fraud detection, risk management, and customer retention.
- Robotic process automation (RPA): This refers to the use of software robots or bots to automate repetitive, rule-based tasks that are usually performed by humans. RPA can help banks improve operational efficiency, accuracy, speed, and scalability.
- Computer vision: This refers to the use of image processing and deep learning to enable machines to understand and analyze visual information. Computer vision can help banks enhance security, identity verification, document processing, and customer experience.
Implementing AI with safety and compliance
Hubbard said that implementing AI in banking requires careful planning, testing, monitoring, and governance to ensure that the technology is safe, reliable, ethical, and compliant with regulations. He suggested some best practices for banks to follow when adopting AI:
- Define clear goals and use cases: Banks should identify the specific problems they want to solve with AI and the expected outcomes and benefits. They should also evaluate the feasibility, viability, and desirability of each use case and prioritize them accordingly.
- Choose the right partners and platforms: Banks should partner with reputable and experienced vendors and providers that can offer end-to-end solutions for AI development, deployment, integration, maintenance, and support. They should also select platforms that are scalable, secure, flexible, and compatible with their existing systems and processes.
- Build trust and transparency: Banks should ensure that their AI systems are explainable, interpretable, auditable, and accountable. They should also communicate clearly with their customers and employees about how they use AI and how it affects them. They should also seek feedback and consent from their stakeholders and respect their privacy and preferences.
- Establish ethical principles and guidelines: Banks should adhere to ethical standards and principles when designing, developing, deploying, and using AI. They should also follow relevant laws and regulations regarding data protection, consumer protection, anti-discrimination, anti-money laundering, etc.
- Create a culture of learning and innovation: Banks should foster a culture that encourages learning, experimentation, collaboration, and innovation among their employees. They should also provide training and education on AI skills and literacy for their staff.
The future of AI in banking
Hubbard said that he expects AI to become more pervasive and sophisticated in the banking industry in the future. He said that some of the trends he anticipates are:
- Hyper-personalization: Banks will use AI to create more personalized and tailored experiences for their customers based on their preferences, behaviors, needs, goals, and life stages. They will also use AI to segment their customers more granularly and offer them more relevant products and services.
- Augmented intelligence: Banks will use AI to augment rather than replace human intelligence and capabilities. They will use AI to assist their employees in making better decisions faster by providing them with data-driven insights and recommendations. They will also use AI to automate mundane tasks while empowering their employees to focus on more creative and strategic work.
- Open banking: Banks will use AI to leverage the opportunities of open banking ecosystems where they can share data and offer services across different platforms and providers. They will use AI to integrate with third-party applications such as fintechs or big techs that can offer complementary or alternative solutions for their customers.
- Social responsibility: Banks will use AI to enhance their social responsibility by supporting environmental sustainability initiatives such as green finance or carbon footprint reduction. They will also use AI to promote financial inclusion by reaching out to underserved segments such as low-income or unbanked populations.
Hubbard concluded by saying that AI is a powerful and transformative technology that can help banks achieve their business objectives and create value for their customers and society. He said that banks should embrace AI as a strategic partner and a team member that can help them innovate and grow.
