重新构想技术领导力：BFSI未来的SoftServe视角

REPORT AUTHORTeam Ecosystm December 2024 Executive Summary The financial services industry is at the forefront of technological innovation. However, the push for digitaltransformation brings challenges such as technical debt, governance, and evolving regulations.Overcoming these requires AI-optimised infrastructure, accessible data, and business resiliency. Thisconvergence presents financial institutions with an opportunity to reshape their strategies in a rapidlyevolving digital world. However, questions remain about the future of technology in finance. How can organisations mitigate therisks of emerging technologies while leveraging them for competitive advantage? What strategies canreduce the burden of technical debt and maximise operational efficiency? As governance evolves from aregulatory necessity to a strategic enabler, how can institutions harness it to build trust and driveinnovation? These were some of the critical themes explored at the recent Insights Forum roundtable hosted bySoftServe and moderated by Ecosystm during the Singapore Fintech Festival. This closed-door sessionbrought together banking, data, and technology leaders to discuss the opportunities and challengesshaping the next wave of technology adoption in BFSI. Here are the key takeaways from the discussion. Incorporating Innovation in theBusiness Strategy1 Financial institutions today have a unique opportunity to be innovative todrive growth and enhance customer experiences. By integratingtechnology seamlessly into their operations, they can streamlineprocesses, reduce costs, and create personalised offerings. GenAIpresents a powerful tool for financial institutions to automate tasks,improve decision-making, and generate creative content. However, torealise the potential of AI, organisations have to invest in data quality andestablish robust governance frameworks. "AI does not feel like another hypecycle – the intensity of theconversations, especially on GenAI,makes it feel very real andimpactful. But there are risks.Understanding and addressingthose risks will make our AIjourneys sustainable.” ROUNDTABLE PARTICIPANT Organisations that have seen early success with GenAI report a shift inmindset, focusing on broader business objectives and breaking down silosto foster a culture of innovation. A strategic approach enables them to break their AI journey into clear steps: identifying andprioritising high-value use cases; running quick, iterative POCs to test feasibility; and scalingrapidly through a defined AI roadmap supported by technology, data, governance, andchange management strategies. This holistic approach not only facilitates the adoption of GenAI but also ensures that innovation drivesboth short-term efficiency and long-term growth. Viewing technology as an enabler of business evolutionrather than just a tool for optimisation, creates sustainable competitive advantages that meet the evolvingdemands of their customers and markets. Overcoming the OperationalHurdles of Scaling AI2 Operationalising AI in financial institutions requires overcoming key challenges, particularly in scalingtechnology while managing resource limitations. Leveraging AI for exception-based workflows or pay-as-you-go software models can optimise costs and expand offerings. However, the real challenge lies inintegrating these technologies into existing systems without overextending resources. The journey from POC to full-scale GenAI deployment demands not only technology but alsoorganisational maturity and collaboration. Often limited engineering capacity makes it hard to allocateresources to GenAI. Its success also requires alignment between engineers and content owners tovalidate outputs. Organisations face additional hurdles with siloed, inconsistent data.Effective data governance and preparation – such as creating securelanding zones – are critical to reducing access costs and enablingsuccessful AI deployment. Strategic partnerships can provide an edge butmust be accompanied by rigorous data validation. “Scalability also adds to thecomplexity – while organisationscan afford localised resourcesduring exploration, scaling AIacross thousands of employeesrequires shared infrastructure,which might not align with currentregulatory frameworks.” Beyond technology, AI adoption requires understanding thecosts, risks, and ROI of operationalising models. It requiresinvestments in infrastructure and clear goal setting to managecomplexity. Balancing innovation with compliance is also key tomainstream AI adoption. ROUNDTABLEPARTICIPANT Building Trust in AI:The Role of Data Governance Governance is a central priority for financial services organisations as they navigate compliancechallenges and treat data as a critical asset. Many are grappling with high data preparation costs and theoperational readiness required to effectively use AI. As a result, there is a strong focus on strengtheningsecurity, governance, and control measures. Some organisa