Follow the link below to discover unique insights about work, life, and perseverance from one of the greatest minds at Frontier Foundry.

youtu.be/4seH35LQoiw?...
(2/2)

3. Talent Flexibility: Remote work has fundamentally changed the talent market. You no longer compete with the major banking hubs for talent if you’re willing to hire the sharp compliance analyst who doesn’t want to relocate. Small banks can punch above their weight here. 💪(4/4)

2. Regulatory Trust: Smaller banks’ charter and regulatory relationships provide a foundation that FinTechs spend years and millions trying to establish or work around. 🤝 (3/4)

1. Relationship Context: Community banks actually know their customers. AI trained on real relationship context such as lending decisions, customer behavior patterns, and local market knowledge represents a valuable data moat big tech companies can’t replicate. 📊 (2/4)

To read more about how community banks can survive this rapid technological transition, follow the link below, and subscribe for free to receive more articles on how to take advantage of the technologies shaping today’s financial ecosystem. ⬇️ (5/5)

substack.frontierfoundry.com/p/emerging-t...

Our latest Substack post summarizes Meghji’s insights into a list of key emerging technologies and actionable recommendations for community banks to follow, as well as the crucial advantages they still have over larger competitors.💡(4/5)

Large national banks have already begun implementing these new technologies. To maintain their competitive advantage, community banks must implement them quickly, but high up-front costs and a lack of talent and resources often slows adoption. ⏱️ (3/5)

The banking sector’s current technological transformation creates both opportunity and risk for community banks. New technologies and systems, from AI to cryptocurrencies to real-time payments, are fundamentally changing the industry and community banks’ role in it. 🏦 (2/5)

To read his full thoughts on the financial and intelligence architectures that have enabled these interventions, how they’ve been used to financially influence foreign regimes, and what benefits and risks this strategy brings, read the link below. ⬇️

thescif.org/nsi-experts-... (2/2)

4. Integration with Existing Systems

The complex IT environments financial institutions operate in make integration challenging. Institutions must ensure that their AI solutions are compatible with existing systems and can be seamlessly integrated into their workflows. 🤝 (5/5)

3. Scalability Challenges

On-premise AI solutions may face scalability challenges as data volumes and processing requirements grow. Institutions must carefully plan their infrastructure so it can scale to meet future needs without compromising performance or security. 📈 (4/5)

2. Talent and Expertise

Deploying on-premise AI solutions requires specialized talent. Institutions must build multidisciplinary teams that can effectively manage these systems and keep them aligned with risk management, compliance, and business objectives. 👨‍💻👩‍💻 (3/5)

1. Infrastructure Costs

On-premise AI solutions often require investment in hardware, software, and infrastructure. Institutions must evaluate the total cost of ownership and ensure that they have the necessary resources to manage on-premise deployments effectively. ⚙️ (2/5)

While building their own custom models (as many national banks have done to great success) would be the best option, most small banks lack the resources and talent to do so.

Community banks need “right-sized AI” or they will fall further behind. (2/2)

substack.frontierfoundry.com/p/from-cloud...
(5/5)

To learn more about why financial institutions are choosing on-premise AI systems, follow the link below to read our latest Substack post on the topic, and subscribe for free to receive weekly articles on the advancement of AI and how its use is transforming industries. ⬇️ (4/5)

While the upfront costs of implementing local and edge solutions may be higher, the long-term benefits of these systems make it a much better investment. Cost efficiency, greater control over AI models, and reduced latency are a few of the many advantages of on-prem AI. 📈 (3/5)

Concerns over risk management and data privacy have driven a shift away from cloud-based systems. Organizations handling sensitive financial data become prime targets for cyberattacks, and moving to on-prem helps reduce risk and maintain regulatory compliance. 🛡️ (2/5)