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Banks Are Confident - and Cautious - About Technology Innovation

Fred Fuller
Endava

Amid economic disruption, fintech competition, and other headwinds in recent years, banks have had to quickly adjust to the demands of the market. This adaptation is often reliant on having the right technology infrastructure in place.

Industry attitudes toward the situation tend to be positive, with 80% of retail banking leaders saying that their technology is ahead of their competitors. However, they still recognize areas where improvements can be made. This finding comes from Endava's new Retail Banking Report, which looks at banks' current and future strategies to meet customer demand and explores their plans to address external factors affecting the industry.

The main takeaway from the report is that retail banks are prioritizing AI, data analytics, payments technology, and core system upgrades to improve the experience of their internal and external systems.

Additional insights from the Retail Banking report include:

Customer centricity: 85% of financial institutions prioritize improving the customer experience, recognizing its importance for acquisition and retention. More than 70% are doing this by increasing digital capabilities and payment offerings.

AI investment: 50% of banks are investing in AI within the next year, making it the top category of those evaluated. The financial sector is excited about the potential of AI to create new efficiencies across internal infrastructure and customer-facing products, with applications such as fraud detection, customer service, data analysis, and investment management.

Data analytics: Close behind AI, banks are focused on data analytics, with 45% of respondents indicating they are investing in this area. Leaders continue to see the value of data to improve customer service, strengthen security and risk management, personalize products, and attract new customers.

Payments upgrades: Upgrading payment gateways and adopting new payment rails are top priorities, with over 75% of organizations ranking them as high-priority initiatives. Upgraded payments technology allows banks to offer customers instant money transfers and timely bill payments, which fosters loyalty and reduces attrition. Additionally, it gives them the ability to increase revenue from current payment volumes.

Core modernization: To accommodate these technology priorities, banks are focusing on updating their core banking system. 75% of those surveyed feel they need to modernize their cores, with large numbers embracing cloud-based solutions. The core system is the backbone of a financial institution, impacting everything from application performance management to in-person customer experience. The benefits of a modern core often include lower operating costs, wider range of products, increased efficiency, enhanced security, and improved retention.

Financial institutions currently operate in a demanding and volatile marketplace requiring them to adapt quickly to shifts in consumer preference and external pressures. It's clear from the report findings that banks of the future will capture sustainable market share by focusing on customer preferences and ensuring alignment between their back-end systems and front-end, client-facing operations.

To ensure they are keeping up with the changing market demands, leaders can leverage technology to quickly roll out new offerings like AI assistants and real-time payments. When a bank creates a better user experience, they're encouraging customers to take advantage of more of their products, creating a more profitable and loyal customer base. The organizations that will succeed are those who can use technology to meet these rapidly evolving consumer demands, while demonstrating ongoing resilience and adaptability.

Fred Fuller is EVP, Global Head of Banking and Capital Markets, at Endava

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Banks Are Confident - and Cautious - About Technology Innovation

Fred Fuller
Endava

Amid economic disruption, fintech competition, and other headwinds in recent years, banks have had to quickly adjust to the demands of the market. This adaptation is often reliant on having the right technology infrastructure in place.

Industry attitudes toward the situation tend to be positive, with 80% of retail banking leaders saying that their technology is ahead of their competitors. However, they still recognize areas where improvements can be made. This finding comes from Endava's new Retail Banking Report, which looks at banks' current and future strategies to meet customer demand and explores their plans to address external factors affecting the industry.

The main takeaway from the report is that retail banks are prioritizing AI, data analytics, payments technology, and core system upgrades to improve the experience of their internal and external systems.

Additional insights from the Retail Banking report include:

Customer centricity: 85% of financial institutions prioritize improving the customer experience, recognizing its importance for acquisition and retention. More than 70% are doing this by increasing digital capabilities and payment offerings.

AI investment: 50% of banks are investing in AI within the next year, making it the top category of those evaluated. The financial sector is excited about the potential of AI to create new efficiencies across internal infrastructure and customer-facing products, with applications such as fraud detection, customer service, data analysis, and investment management.

Data analytics: Close behind AI, banks are focused on data analytics, with 45% of respondents indicating they are investing in this area. Leaders continue to see the value of data to improve customer service, strengthen security and risk management, personalize products, and attract new customers.

Payments upgrades: Upgrading payment gateways and adopting new payment rails are top priorities, with over 75% of organizations ranking them as high-priority initiatives. Upgraded payments technology allows banks to offer customers instant money transfers and timely bill payments, which fosters loyalty and reduces attrition. Additionally, it gives them the ability to increase revenue from current payment volumes.

Core modernization: To accommodate these technology priorities, banks are focusing on updating their core banking system. 75% of those surveyed feel they need to modernize their cores, with large numbers embracing cloud-based solutions. The core system is the backbone of a financial institution, impacting everything from application performance management to in-person customer experience. The benefits of a modern core often include lower operating costs, wider range of products, increased efficiency, enhanced security, and improved retention.

Financial institutions currently operate in a demanding and volatile marketplace requiring them to adapt quickly to shifts in consumer preference and external pressures. It's clear from the report findings that banks of the future will capture sustainable market share by focusing on customer preferences and ensuring alignment between their back-end systems and front-end, client-facing operations.

To ensure they are keeping up with the changing market demands, leaders can leverage technology to quickly roll out new offerings like AI assistants and real-time payments. When a bank creates a better user experience, they're encouraging customers to take advantage of more of their products, creating a more profitable and loyal customer base. The organizations that will succeed are those who can use technology to meet these rapidly evolving consumer demands, while demonstrating ongoing resilience and adaptability.

Fred Fuller is EVP, Global Head of Banking and Capital Markets, at Endava

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Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...