Panel, Conference Presentation
Transforming Finance: Tech Is Everywhere
- Banks are expected to accelerate the adoption of cloud and AI technologies to enable radical operating model changes, transforming digital capabilities from optional features into critical business continuity measures driven by 60% of customers showing increased willingness to buy digital services compared to two years ago.
- Emerging business models such as "bank as a service" aim to leverage existing capital and technology to convert cost centers into revenue centers by providing payment or clearing services to non-banks, while banks increasingly participate in shared data ecosystems to move beyond the "not invented here" mentality.
- Digital platform usage and wallet adoption among older demographics are predicted to persist post-pandemic, though cash remains dominant with digital payments representing only 15% of all transactions and 75% of store transactions in India.
- Future financial services firms will utilize technology, automation, and AI to reshape cost curves and manage difficult economic conditions characterized by low interest rates and increased loan losses, alongside a focus on employee-facing digital tools to support remote work productivity and industrialization needs.
- Technology and data will be deployed to lower verification costs for serving harder-to-reach populations and to drive financial inclusion, with specific applications including AI-driven reductions in drug discovery costs and the creation of open platforms allowing banks to select fintech providers for services like fraud detection and flood insurance.
- Investment criteria will prioritize companies using machine learning and data to remove friction and open markets, provided the business model aligns with the interests of all stakeholders, while funding programs such as SoftBank Vision Fund's "Engage" initiative will target companies founded by minority groups.
- Expectations for model stability, robustness, and explainability of "black box" algorithms will rise, necessitating disparate impact assessments, collective understanding of algorithm outputs, and regulatory focus on explainability before immediate model approval in credit underwriting and adverse action notifications.
- Risks involving security and fraud prevention will intensify, requiring evolution to combat ransomware and biased algorithmic learning, with potential regulatory divergence where fintechs face lower standards regarding privacy and bias compared to large established institutions.
- Social impact initiatives, including global hackathons and solutions for rapid loan delivery, will be pursued to address system inequities, with success dependent on diverse leadership generating empathy for data governance and a workforce maintaining expectations for work-life balance and empathetic management.
- The long-term outlook suggests that the fundamental shift in consumer behavior and the compressed speed of technology adoption will solve daily needs, while diverse inputs into algorithm development will be critical for creating unbiased systems that represent humanity.