Artificial Intelligence, which includes Machine Learning and Traditional Statistical Modeling techniques, has long played a role in the Financial Services industry. Recently, banking and financial institutions have stepped up their efforts to leverage Machine Learning. Enabling them to detect patterns in vast datasets that predict behaviors like fraud, customer churn, recommended actions, and more. Despite these advancements, the Financial Services industry still trails behind Big Tech and Digitally Native organizations when it comes to GenAI. This gap is primarily due to challenges like regulatory change, data fragmentation, and a culture that prioritizes risk aversion over innovation.
The Emergence of GenAI
GenAI has brought new capabilities that have captivated the entire Financial Services sector. Alongside enhancing efficiencies and reducing costs, GenAI has the potential to transform user experiences. It can parse extensive knowledge bases and facilitate natural language-based interactions through digital assistants.
These digital assistants can manage complex queries and significantly improve customer interactions. However, banks and financial institutions must tackle legal, compliance, privacy, and security concerns to truly unlock the power of these advanced tools.
Taking A Structured Approach to Innovation
While no one-size-fits-all solution exists for these challenges, leading organizations demonstrate the effectiveness of a time-tested strategy: Structured Innovation. This approach includes several crucial steps:
- Education: Stakeholders enthusiastic about GenAI must fully understand its capabilities, limitations, and impacts.
- Risk Management Integration: Integrating legal, compliance, security, and risk management teams early is crucial and helps shape the innovation pathway. Addressing risk management from the outset often determines whether innovation reaches operationalization or ends up abandoned.
- Value-Driven Initiatives: Organizations should prioritize projects based on their potential business value. This requires a clear understanding of current business processes and their costs, a gap many organizations still face.
- Transparent Governance and Agile Execution: Maintain clear priorities and governance. By adopting flexible, startup-inspired execution strategies, organizations can pivot or terminate projects as necessary, creating a culture that values learning rather than fearing failure.
- Co-Development with End Users: Engaging end users throughout development ensures solutions meet practical needs and address adoption hurdles from the start.
- Piloting and Operationalization: Following a successful proof of concept, the pilot phase must lead to operationalization with strict engineering rigor and intelligent automation to guarantee scalability and sustainability.
- Ongoing Evaluation and Adaptation: Organizations should continuously monitor performance, updating or replacing components—like LLMs in GenAI solutions—as more suitable models emerge.
The McKinsey Global Institute (MGI) reports that GenAI could add between $200 billion and $340 billion in value across the banking sector. To capture these benefits, banks must quickly adapt their innovation processes and address their existing challenges to be future-ready for applications of AI in Finance.
Navigating GenAI from the Perspective of Data & Technology
Traditionally, banks and financial institutions spent significant effort setting guidelines and building architectures and processes to create structured data pipelines for Machine Learning models. Now, intelligent AI broadens that data scope to include unstructured data from internal and external documents. This shift requires new architectures, processes, and even talent.
Success Hinges on GenAI Adoption
Success in GenAI depends on adoption, which banks will not achieve without well-defined roadmaps for integrating GenAI solutions into their existing and future tools and digital workflows.
Given the growing importance of applications of AI in Finance, many organizations across sectors are appointing Chief AI Officers to steer strategy and orchestrate execution. For those with a more decentralized model or without this role, partnering with an experienced team of Intelligent AI experts can accelerate progress.
In most cases, Financial Services organizations turn to trusted, intelligent automation partners who operate across the technology stack, from hybrid cloud infrastructure to digital workflows and data engineering to AI and GenAI. These partners help guide and accelerate their journeys.
As the new era of Agentic AI unfolds, how will your organization embrace it?
If any of these challenges resonate with you and your organization, or if you want to learn more about what’s happening with applications of AI in Finance, we’d love to connect.
