Digital transformation is a key objective for many banks, yet numerous hurdles prevent them from achieving this goal. The financial sector has been inundated with data from various sources, while technical debt from outdated systems slows the pace of innovation.
Publicis Sapient’s Global Banking Benchmark study highlights these challenges, revealing that despite a strong desire to advance AI capabilities, financial institutions struggle to move past these barriers.
The need to modernize is undeniable, but banks face complex challenges involving legacy technology, regulatory constraints, and operational inefficiencies. As data continues to pile up, banks must find ways to simplify systems, manage their information, and allocate budgets effectively to remain competitive.
Can banks keep up? Data overload and legacy systems are stifling progress
Banks face growing complexity as they attempt to embrace digital transformation while dealing with overwhelming amounts of data and entrenched technical debt. Data from Publicis Sapient’s Global Banking Benchmark study, gathered from 1,000 senior executives, shows that financial institutions are eager to evolve but are struggling under the weight of legacy systems and escalating costs.
Despite high hopes for AI integration, a number of banks remain mired in old technology and processes that hinder progress.
Legacy systems are one of the major obstacles standing in the way of digital transformation. Outdated infrastructure not only slows the modernization process but also racks up technical debt, which banks must address before moving forward.
Publicis Sapient’s study reveals that one-third of senior executives point to budget constraints as a key barrier, making it difficult for banks to invest in the technology needed to transform.
The financial burden of maintaining legacy systems, combined with regulatory compliance demands and the cost of new innovations, leaves many banks at a standstill. Without addressing these financial challenges, banks will continue to struggle in their efforts to modernize.
Data silos are the silent killer of banking’s digital dream
While banks possess vast amounts of customer data, this wealth of information often exists in silos, fragmented across various business lines. Separation makes it difficult for institutions to use the data fully, hindering their efforts to innovate. A lack of integrated data systems limits the ability to make strategic decisions, improve customer experiences, or implement new AI-driven solutions.
Banks need to consolidate and simplify their data across departments to unlock its full potential. Until they can overcome the challenge of siloed information, their efforts to transform digitally will remain disjointed and less effective.
The unstructured data tsunami that could change banking forever
The volume and diversity of unstructured data are skyrocketing, providing banks with new opportunities and challenges. Information no longer comes just in the form of spreadsheets and numbers. Data arrives as tweets, pictures, videos, chats, and other non-traditional formats that require sophisticated systems to process and analyze.
According to David Donovan, EVP of Publicis Sapient, the rapid growth of unstructured data demands an architecture capable of managing these diverse inputs. Tweets, images, and chats hold valuable insights, but extracting actionable data requires new tools and processes.
As banks prepare for the future, they must invest in solutions that can handle this unstructured data deluge.
Generative AI is bringing new life into old banking systems
Generative AI tools offer a promising solution for modernizing COBOL applications and addressing legacy system challenges. With many banks still relying on outdated systems to manage core functions, technical debt continues to grow, further stalling progress.
According to Michael Abbott of Accenture, generative AI solutions can help ease this burden by automating the modernization of COBOL applications. Ai helps banks transition away from legacy systems, reducing the time and cost associated with manual updates. As technical debt diminishes, banks can focus their resources on new technologies and innovations.
AI and data, the power duo
Data and AI are the two pillars of banking modernization, with both set to dominate the industry’s transformation efforts over the next three years. Publicis Sapient highlights the importance of harnessing these technologies to drive systemic changes throughout the banking enterprise.
AI is the key to unlocking banking’s full digital potential
AI offers the ability to turn massive amounts of data into strategic assets. As AI technology advances, it provides financial institutions with tools to enhance decision-making, improve customer interactions, and automate key processes. However, integrating AI requires a holistic approach that touches every business unit, from operations to customer service.
When embedding AI deeply within their operations, banks can tap into the transformative potential that data offers.
75,000 databases? Fragmented data is slowing down banking
Banks face a daunting challenge in managing data across their operations. The typical investment bank operates with an average of 75,000 individual databases, leading to duplication, inefficiencies, and fragmentation of information.
A fragmented approach makes it difficult for banks to gain a unified view of their data, leading to missed opportunities for optimization and innovation.
In order to address this, banks must simplify their database architecture and reduce redundancy. Simplifying data management will help them act faster on insights, offer better customer service, and improve their overall operational efficiency.
Banks are betting big on the cloud to fuel the AI revolution
As banks seek to modernize and scale AI capabilities, cloud technology is a key investment. Migrating to the cloud allows banks to handle larger volumes of data, improve processing power, and lead to faster decision-making. Without cloud infrastructure, banks will struggle to keep up with the demands of AI-driven services.
When making the best use of their data in the cloud, banks can gain faster insights, improve customer experiences, and reduce operational complexity.
Nearly two in five executives in the Publicis Sapient study said that cloud infrastructure investments are a top priority for their institution over the next three years. Cloud platforms provide a flexible, scalable environment for banks to integrate their fragmented data and leverage AI more effectively.
JPMorgan Chase is setting an AI benchmark for the entire banking industry
JPMorgan Chase has taken the lead in AI adoption, positioning itself as a model for the rest of the banking industry. The bank is pouring $17 billion into technology in 2024, a 10% increase from the $15.5 billion spent in 2023.
As part of this strategy, JPMorgan has implemented its LLM Suite AI assistant, now available to 140,000 employees.
JPMorgan’s aggressive investment in AI is driven by its belief in the technology’s ability to transform banking operations. The deployment of the LLM Suite AI assistant is part of a broader plan to integrate AI across all aspects of the bank’s operations, from customer service to fraud detection.
Through the prioritization of AI, JPMorgan is setting the stage for more efficient and personalized services.
Banks are using new data and AI to get ahead
Banks are sitting on a treasure trove of client data, ranging from salary information to mortgage payments and credit card usage. This gives them unparalleled insight into their customers’ financial habits. With AI, banks can analyze this data to offer more personalized services, refine risk assessments, and develop more targeted products.
Banks know more about their customers than perhaps any other industry. This depth of knowledge, covering earnings, spending habits, and even daily movements, gives financial institutions a competitive edge. When using AI to process and analyze this data, banks can predict customer needs, offer tailored financial advice, and streamline services.
Investments in AI and machine learning are accelerating as banks seek to automate processes and improve customer engagement. With 29% of digital customer experience budgets going toward AI and machine learning, banks are betting that these technologies will improve both operational efficiency and customer satisfaction.
Outdated tech can’t handle the future
Legacy technology in banks is struggling to keep pace with the demands of modern data and AI usage. These outdated systems cannot handle the growing volume and complexity of data generated by today’s banking activities. As the pressure to innovate builds, banks are reaching the limits of what their legacy systems can process.
Legacy systems are rapidly becoming a bottleneck for banks that need to process ever-increasing amounts of data. Without modernization, these systems will continue to slow down banks’ ability to innovate and deliver real-time services.
As AI and machine learning become more integral to banking, the limitations of legacy systems will only become more apparent.
Machine learning is banking’s secret weapon for better customer service
Machine learning provides banks with tools to improve the customer experience in ways that weren’t possible before. When analyzing vast amounts of customer data in real time, machine learning can offer personalized recommendations, detect fraud more effectively, and automate service requests.
Improvements lead to a smoother and more responsive customer experience, which is becoming a key differentiator in the competitive banking landscape.
Key takeaways
The path to digital transformation in banking is complex, with data overload and technical debt at the forefront of challenges. Modernization efforts depend on overcoming fragmented data systems, outdated technology, and regulatory uncertainties, while also harnessing the power of AI and cloud technologies.
Investment in cloud infrastructure and AI-driven tools is key to simplifying operations and improving customer service. As the financial sector works through these hurdles, the focus remains on unlocking the full potential of data to drive strategic growth and maintain a competitive edge.
