Artificial Intelligence (AI) technologies are an integral part of the banking industry. In areas such as risk, fraud, and compliance, for example, the use of AI has been commonplace for years and continues to deepen. The success of these initiatives (and others), and the potential to unlock further benefits, is driving further investment in this area in 2024, with Generative AI attracting particular interest.
Financial tech analyst Celent created a report commissioned by MongoDB and Icon Solutions that dives into how AI is currently being used in the banking industry today, as well as some of the key use cases for AI adoption in payments to improve operational agility, automate workflows, and increase developer productivity.
Download Celent’s report: Harnessing the Benefits of AI in Payments to discover how you can make the most of your AI investments and unlock the limitless possibilities that AI holds for the future of payments.
Unlocking a range of workflow and product enhancements
AI technologies are used today to address a wide range of different workflows and customer-facing services from process automation and optimization in the middle and back office, to areas such as real-time risk and liquidity management, cashflow forecasting, and service personalization in the front office. Virtual assistants and bots have also become an important part of the customer support process.
In this blog, we'll cover some of the key findings from Celent’s Harnessing the Benefits of AI in Payments report and what this means for the banking and payments industry.
Advanced analytics, intelligent automation, and AI technologies lead the investment agenda in 2024
Over time, banks have steadily increased their investments in projects to make better and more efficient use of data. In part, this has been driven by the need to respond to rising customer expectations over the speed and quality of digital services, but it also reflects a growing understanding of the true value of account and transaction data. Most important of all, though, has been enabling the technologies required to deliver use cases supported by AI and advanced analytics.
It is no surprise to see that projects supported by data analytics and AI technologies are high on the agenda globally. Advanced analytics and machine learning investments are a leading technology priority for 33% of corporate banks, ranking higher than projects relating to robotics and automation (a focus for 31% of the market). Artificial intelligence and natural language processing (NLP) are not far behind and were highlighted as a priority by 28% of banks.
Many are also exploring Generative AI
While the excitement around genAI is understandable given the obvious potential, the conversation became more nuanced through the latter part of 2023. This is understandable given the complexities of applying large language models (LLMs) to potentially sensitive customer data, as well as broader regulatory concerns over the explainability (and potential auditability) of LLM outputs. That said, there are many areas in which genAI is already being used to support advisors and relationship managers and further innovation in areas such as this is expected. According to the report, 58% of banks are evaluating or testing Generative AI in some capacity while a further 23% have projects using this technology in their roadmap.
Emerging use cases for AI in payments and the potential revenue growth
A lack of developer capacity is one of the biggest challenges for banks when it comes to delivering payment product innovation. Banks believe the product enhancements they could not deliver in the past two years due to resource constraints would have supported a 5.3% growth in payments revenues. With this in mind and the revolutionary transformation with the integration of AI, financial institutions must consider how to free up developer resources to make the most of these opportunities.
As the payments industry continues to evolve, the integration of AI is poised to reshape the landscape, offering innovative solutions that prioritize security, efficiency, personalized user experience. The emerging use cases for AI in payments are a testament to its transformative potential in shaping the future of financial transactions.
Leveraging modern technologies to make the most of AI adoption
In the rapidly evolving landscape of AI, constant technological advancements and evolving customer needs necessitate strategic investments. To stay competitive, banks and payment providers should not only focus on current product enhancements but also future-proof their capabilities through payment infrastructure modernization. When adopting advanced technologies like AI and ML which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. For example, adding a new payment rail and a new customer access point could be very difficult. Establishing a robust data architecture with a modern data platform that enables banks to enrich the payments experience by consolidating and analyzing data in any format in real-time, driving value-added services and features to consumers. The following recommendations will help ensure financial services organizations can unlock the transformative potential of generative AI at scale while ensuring privacy and security concerns are adequately addressed:
Train AI/ML models on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. By unifying data from backend payment processing to customer interactions, banks can surface insights in real-time to create a seamless, connected, and personalized customer journey.
Future-proof with a flexible data schema capable of accommodating any data structure, format, or source. This flexibility facilitates seamless integration with different AI/ML platforms, allowing financial institutions to adapt to changes in the AI landscape without extensive modifications to the infrastructure.
Address security concerns with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies.
Launch and scale always-on and secure applications by integrating third-party services with APIs. MongoDB's flexible data model and ability to handle various types of data, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible.
The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich payment experiences by consolidating, ingesting, and acting on any payment data type instantly. MongoDB Atlas is designed to help financial services organizations overcome data challenges. It features a flexible document data model and seamless third-party integration capabilities that are necessary to create composable payment systems that scale effortlessly, are always-on, secure, and ACID compliant.
Stay ahead of the curve — download Celent’s report now and unlock the limitless possibilities that AI holds for the future of payments. If you prefer a visual exploration, a discussion featuring Celent, Icon Solutions, and MongoDB, register for our upcoming webinar, Using AI to Unlock New Opportunities in Payments with Celent, Icon Solutions, and MongoDB.
If you would like to discover more about building AI-enriched payment applications with MongoDB, take a look at the following resources:
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