Artificial Intelligence (AI) is taking over every sector, and banking is no exception. According to recent reports, the global artificial intelligence in banking market size, estimated at $19.87 billion in 2023, is expected to grow at a compound annual growth rate (CAGR) of 31.8% from 2024 to 2030. From AI-driven chatbots to personalized product recommendations, AI integration in banking is poised to transform the overall banking experience.
With promises to streamline operations, improve productivity, and enhance risk management, AI in banking marks a crucial shift in how banking products are developed and delivered.
Understanding Use Cases
Artificial intelligence opens doors to several benefits for BFSI organizations across various applications. Let’s look at five use cases where AI is poised to have the most significant impact:
- Credit assessment: Banks can integrate AI into their lending platforms to provide advanced financial analytics and credit assessment. AI models can analyze various data points, such as income, credit history, behavior, transactions, etc., and generate credit scores that predict a borrower’s likelihood of repaying a loan. They can also learn and adapt to new data sets, improving predictions based on changing customer and market conditions.
- Customer onboarding: AI can simplify customer onboarding and enhance trust and loyalty. AI models can quickly perform KYC checks, reducing the time spent in manual reviews and ensuring accuracy and compliance. They can automate data capture from complex documents while guiding consumers through onboarding, answering questions in real-time, and assisting in completing forms and uploading documents.
- Fraud detection: Banks can leverage AI to enhance risk management and fraud detection. AI algorithms can process and categorize historical data to detect irregular patterns or anomalous transactions. Self-learning AI systems can constantly incorporate new data and adjust to a changing fraud environment. This can enable banks to recognize new fraud types and ensure around-the-clock customer and banking data protection.
- Compliance management: AI also offers the potential to automate and strengthen compliance management. AI models can proactively monitor regulatory changes and evaluate their impact on business operations. By training models on the latest regulations, company policies, and guidelines, banking organizations can better understand outliers and take action to minimize the probability and impact of non-compliance.
- Market analysis: AI-powered platforms can utilize natural language processing to analyze keyword searches within banking apps, research reports, and news to discover changes and trends in financial markets. Banks can use this information to conceptualize new products and features that align with emerging needs and drive greater customer satisfaction.
Evaluating AI Capabilities for the BFSI Sector
The McKinsey Global Institute estimates that Generative AI alone could add between $200 billion and $340 billion in value annually, or 2.8 to 4.7 percent of total industry revenues, mainly through increased productivity.
As AI becomes an integral part of the BFSI ecosystem, new and innovative AI-enabled features are constantly underway. Here’s looking at the various AI capabilities in the BFSI sector:
- Service Management: In service management, AI can improve service experiences through a high level of personalization. AI models can constantly analyze past banking user interactions and preferences and provide personalized engagements based on their profiles. AI-enabled tools like Jira Service Management can automate routine ITSM tasks to
- Identify and prioritize issues, summarize details, and generate incident summaries for post-incident reviews.
- Scan resources such as knowledge articles to find solutions for everyday problems and accelerate issue resolution.
- Find trends and patterns in service delivery and allocate resources optimally.
- Customer Support: As timely customer support becomes imperative for the banking sector, AI innovations can pave the way for quick and contextual support outcomes. Using AI features of Confluence, for example, teams can:
- Enable human-like interactions via chatbots and virtual assistants to boost operational efficiency and speed.
- Create customer support automation rules describing the tasks they wish to automate and let Atlassian Intelligence handle all the heavy lifting.
- Streamline time-consuming and mundane customer support tasks, such as tracking customer service metrics or finding answers to common queries.
- Project Management and Delivery: Project management and delivery are tedious for banking apps. It requires a deep understanding of the banking industry’s technical and regulatory aspects, and AI tools help with that. With features to streamline project execution and delivery, AI-powered tools like Jira allow project managers to track sprint tasks, customer issues, and bugs effortlessly. Using Jira, they can:
- Map dependencies and identify potential roadblocks to ensure smooth progress.
- Build scrum boards, breaking large, complex projects into manageable pieces for faster shipments.
- Visualize dependencies and make fast and informed decisions, keeping team bandwidth in mind.
- Code Quality: The BFSI sector handles highly sensitive data, demanding adherence to strict regulatory standards. AI-first tools can help teams understand new and complex code, diagnose errors and exceptions, and get detailed explanations and suggestions on how to fix them. JetBrains AI Assistant, for instance, allows developers to ask questions in the chat and provides detailed explanations based on the project context.
With JetBrains, developers can:
- Leverage modern AI capabilities to translate code and work seamlessly across different languages.
- Use AI-driven refactoring prompts to optimize and clean up code, ensuring it remains efficient and maintainable.
- Delegate routine or repetitive tasks to the AI Assistant and focus on more creative, satisfying activities.
- CI/CD: Online banking platforms and apps must incorporate robust CI/CD pipelines to deliver updates continuously. From updating security features to ensuring accurate fund transfers, modern AI tools help speed up code development, improve operations, and secure software across each CI/CD pipeline stage.
GitLab Duo, for instance, has been steadily expanding its suite of AI capabilities. Now extending across the entire software development lifecycle, these capabilities allow developers to:
- Get code suggestions, spot issues, and automate code reviews.
- Receive instant summaries on detected vulnerabilities, their implications, in-depth solutions, and suggested mitigation.
- Leverage AI-assisted root cause analysis for CI/CD job failures and save time troubleshooting.
Challenges and Considerations
While embracing AI has become a business prerogative for the BFSI industry, adopting the technology comes with several challenges:
- Data privacy: Data privacy and security are often the most common concerns when adopting AI. Since large language models rely on vast amounts of data, banks must implement policies and procedures to protect customer data 24/7. They must also ensure compliance with regulations such as GDPR to build trust and transparency, improve data management, and mitigate emerging risks.
- Operating model: As banks look to make the most of AI, choosing a suitable operating model is extremely important. According to McKinsey, a high degree of centralization works best for BFSI organizations as it minimizes the chances of pilot use cases getting stuck in silos. Central oversight also makes scaling easy, allowing financial institutions to reap the most significant rewards. With a centralized approach, BFSI organizations can build a cohesive team that stays on top of the evolving gen AI landscape and make essential decisions on funding, architecture, risk management tactics, and more.
- Skills gap: Lack of qualified and certified AI resources is another challenge BFSI organizations face in implementation. Since practical deployment demands specialized AI and machine learning skills, banks, and other financial institutions must hire experts to manage and develop AI-driven solutions. An expert team of qualified personnel can provide much-needed assistance and guidance in understanding the current business landscape, unearthing gaps and challenges, and suggesting solutions that perfectly align with future goals and aspirations.
- Compliance: Given how heavily regulated the financial sector is, AI adoption must be done in compliance with various regulations. Banks must carefully navigate the regulatory landscape to ensure the ethical use of AI, maintain content accuracy and accountability, and avoid potential legal issues. They must also implement mechanisms to control user access, enhance fairness, and minimize bias.
- Integration: Introducing AI into the BFSI IT landscape also brings in the challenge of integration. Integrating large language models can be complex, especially for those with legacy systems in place. Banks must invest in modernizing their outdated applications and upgrading their infrastructure to enable seamless interoperability and data sharing between AI models and existing technologies.
Reimagine Banking with AI
As AI takes over the world, BFSI organizations stand to supercharge banking software development. From seamless service management to quick customer support, effective project management and delivery to improved code quality and CI/CD pipeline management, embrace the world of AI for more innovative, safer, and seamless banking experiences.