Banking on advanced AI adoption
Financial institutions have historically been early adopters of technology. From traditional data analytics to the AI of today, the banking, financial services, and insurance (BFSI) industries consistently been early adopters of new methods and solutions to their existing processes. Now, with the vast improvements in AI’s predictive abilities and the rise of generative and agentic AI, many businesses in BFSI must make the leap into further digital transformation. As it stands, many organizations are burdened by large, complex legacy systems and strict regulatory requirements, which can slow modernization efforts. Before organizations can fully deploy advanced AI and agent-based solutions, they need clean and contextualized data.
In an episode of the Industry Forward Podcast, host Kate Eby interviews Dylan Tancill, Vice President of Sales and Global Head of BFSI at Siemens Digital Industries Software, about the current state of AI adoption in BFSI. Successfully moving AI from pilot to production depends on creating a foundation of trusted and contextualized data.
Ensuring AI-readiness in BFSI
While many financial institutions have been technology innovators for decades, many of them still have large, complex legacy infrastructures. Integrating modern AI solutions such as generative and agentic can prove difficult due to BFSI platforms having siloed and fragmented data. What’s more, these systems are often expensive to maintain and trickier to update.
Ensuring clean, organized data is the crux of the issue. Knowledge graphs combined with AI agents has the power to help analyze large data sets continuously and identify actionable insights more efficiently and accurately. Generally, AI is mostly accurate. And in BFSI, mostly accurate AI poses an issue because inaccuracy can lead to financial losses, regulatory penalties, operational failures and reputational damage. Creating an AI-ready data foundation by curating the data and then connecting it with context will help move AI from pilots to production.
In a similar vein, any new AI systems incorporated into existing infrastructures must meet regulatory compliance. BFSI operates in one of the most heavily regulated environments in the world, and besides meeting requirements, businesses need to guarantee that agentic AI solutions are transparent and auditable while reducing the incorrect outputs or hallucinations and managing the privacy and security of customer data. This goes back to the importance of having high quality, curated data.
Additionally, many organizations in the industry are under pressure to reduce technology costs while investing in AI. Enterprises are evaluating how to optimize their technology stacks and reduce operational expenses all while keeping up with competitors. From ongoing system maintenance costs to managing storage expenses and compute requirements, BFSI as a whole must be sure to balance these with investing in AI projects.
Towards an AI-ready future
Already, current AI use cases have shown promising results when it comes to financial crime detection and research. Today, AI in BFSI can detect potential fraud and surveil insider trading activities as well as help employees access insights from large volumes of structured, semi-structured, and unstructured data—effectively reducing research times from hours to minutes or seconds.
And these use cases are only poised to grow as BFSI continues to invest in AI. Despite being aggressive adopters of technology, BFSI is seeing roadblocks to integrating newer AI solutions due to complex infrastructures and unclean data. Successful AI adoption in the industry is not primarily about deploying the newest AI models. Instead, success depends on creating a foundation of trusted, contextualized, governed data and implementing strong guardrails, helping to move AI initiatives from pilot projects into production.
To learn how your company can become ready for the next big thing in AI, consider tuning in to our Industry Forward podcast.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
