Global Data Analytics in Banking Market By Deployment Type (On-Premises, Cloud) By Type (Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics) By Solution (Risk Management (Credit Risk Assessment, Fraud Detection and Management, Stress Testing, others), Customer Analytics, Portfolio Management Analytics, Trading Analytics) By End User (Sell Side Firms, Buy Side Firms) By Company, By Region, Forecast & Opportunities, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 13.29 Billion
CAGR (2026-2031)	19.52%
Fastest Growing Segment	Cloud
Largest Market	North America
Market Size (2031)	USD 38.74 Billion

Market Overview

The Global Data Analytics in Banking Market will grow from USD 13.29 Billion in 2025 to USD 38.74 Billion by 2031 at a 19.52% CAGR. Data analytics in banking is the systematic computational analysis of financial records to uncover patterns, correlations, and trends that inform strategic decision-making. The market is primarily driven by the critical need for enhanced risk management frameworks and the increasing demand for personalized customer experiences, which require institutions to process vast volumes of transactional information rapidly. Additionally, stringent regulatory compliance mandates compel financial institutions to adopt precise analytical measures to ensure transparency and prevent financial crimes, acting as a fundamental catalyst for widespread industry adoption.

Despite these drivers, a significant challenge impeding market expansion is the difficulty of integrating modern analytical tools with fragmented legacy IT infrastructure, which often leads to data silos and governance issues. This operational gap is highlighted by the industry's struggle to formalize data usage; according to the American Bankers Association, in 2024, 71 percent of bank marketers reported that their institutions did not have a written or documented customer data strategy. Such deficiencies in strategic planning can hinder banks from fully leveraging their data assets, thereby slowing the overall maturity of the global analytics market.

Key Market Drivers

The surge in Artificial Intelligence (AI) and Machine Learning (ML) integration acts as a primary engine for the market, enabling institutions to transition from retrospective analysis to predictive intelligence. Banks utilize these technologies to process unstructured datasets, allowing for automated credit scoring and algorithmic product recommendations. This technological shift is evidenced by the industry's aggressive adoption rates; according to NVIDIA, February 2024, in the 'State of AI in Financial Services: 2024 Trends' report, 91 percent of financial services companies were assessing or using AI to drive innovation and operational resilience. Such widespread integration necessitates robust analytics platforms capable of handling complex models, thereby fueling market growth as financial entities strive to maintain competitive advantages through data-driven foresight.

Simultaneously, the rising demand for real-time fraud detection compels banks to deploy modern analytical solutions that can identify anomalies within milliseconds. As transaction volumes escalate, traditional rule-based systems are proving insufficient against evolving cyber threats, necessitating the use of behavioral analytics and pattern recognition. The effectiveness of these defensive measures is measurable; according to Visa, March 2024, in the 'Spring 2024 Threats Report', the company’s analytics capabilities helped block $40 billion in fraudulent activity during the previous year. To support these security measures and broader digital infrastructure, massive capital is being directed toward technological fortification. According to JPMorgan Chase, in 2024, the bank allocated approximately $17 billion to technology, underscoring the critical role of data-centric investment in the modern banking landscape.

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Key Market Challenges

The significant challenge impeding market expansion is the difficulty of integrating modern analytical tools with fragmented legacy IT infrastructure, which creates substantial data silos and governance voids. Financial institutions often rely on aged core systems that cannot efficiently communicate with newer, data-intensive applications, making it nearly impossible to aggregate the real-time, unified datasets required for advanced analytics. This architectural disconnect prevents banks from seamlessly accessing the transactional information needed for critical functions such as risk modeling and personalized customer targeting. Consequently, the inability to establish a cohesive data environment limits the scalability of analytics initiatives, forcing institutions to rely on manual, error-prone processes that negate the efficiency and speed promised by modern analytical solutions.

This technical barrier directly hampers market growth by elevating the operational risk and expense associated with digital transformation projects. The complexity of layering sophisticated analytics on top of incompatible legacy frameworks often leads to prolonged implementation timelines and ballooning costs, deterring institutions from fully committing to necessary upgrades. According to the Conference of State Bank Supervisors, in 2024, nearly 80 percent of community bankers identified technology implementation and costs as a top internal risk to their organizations. This statistic highlights the widespread hesitation across the industry to undertake the complex structural overhauls required for data integration. As banks delay these critical technology updates to avoid disruption and financial exposure, the broader adoption of global data analytics stalls, preventing the market from reaching its full potential.

Key Market Trends

The expansion of open banking and API-driven data ecosystems is fundamentally reshaping the market by transitioning financial institutions from closed, proprietary data silos to collaborative, interoperable networks. This trend allows banks to securely share customer-permissioned data with third-party providers, fostering the development of innovative financial products and streamlined payment services that extend beyond traditional banking interfaces. The acceleration of this ecosystem is evident in the rapid uptake among commercial entities seeking efficiency. According to Mastercard, December 2024, in the 'Open banking: The trust imperative' white paper, 85 percent of B2B respondents reported currently using open banking solutions to enhance their financial operations. This high adoption rate underscores the market's shift toward platform-based models where data fluidity drives competitive differentiation and new revenue streams.

The integration of generative AI for hyper-personalization represents a critical evolution in how banks utilize data, moving beyond static predictive scores to dynamic, conversational customer engagement. Unlike traditional analytics that categorize users into broad segments, generative models analyze individual transaction histories and behavioral nuances to construct bespoke financial advice and automated, human-like interactions in real time. This technological commitment is intensifying as institutions recognize the necessity of AI for operational excellence and customer retention. According to NTT DATA, February 2025, in the 'Intelligent Banking in the Age of AI' report, 58 percent of banking organizations have fully embraced the transformative potential of generative AI, viewing it as a critical tool for improving productivity and efficiency. Such widespread implementation highlights the sector's focus on leveraging advanced algorithms to deliver the tailored, responsive experiences modern consumers demand.

Segmental Insights

The Cloud deployment segment is characterized as the fastest-growing area within the Global Data Analytics in Banking Market. This rapid expansion is primarily driven by the industry's strategic shift from rigid, capital-intensive on-premise infrastructures to flexible, cost-efficient cloud models. Financial institutions are prioritizing cloud adoption to manage the massive data volumes required for real-time risk assessment and customer personalization. Furthermore, as global regulatory bodies increasingly validate cloud security frameworks, historical compliance barriers are diminishing, allowing banks to leverage cloud platforms for superior operational agility and advanced data processing capabilities.

Regional Insights

North America holds the leading position in the global data analytics in banking market due to the significant concentration of major technology firms and financial institutions within the United States. The region benefits from substantial capital investment in digital infrastructure and the early integration of analytics for improved risk management. Additionally, stringent reporting standards enforced by authorities such as the Federal Reserve compel banks to utilize analytics for compliance and fraud detection. This focus on operational efficiency and customer retention through data-driven strategies ensures North America remains the dominant region in the global market.

Recent Developments

• In October 2025, Snowflake unveiled Cortex AI for Financial Services, a comprehensive suite of AI capabilities tailored for the banking and financial sector. The new solution was designed to help financial institutions unify their data ecosystems and securely deploy AI models and agents for critical tasks such as market analysis, fraud detection, and claims management. By offering rigorous security and compliance controls required in regulated industries, the platform enabled companies to leverage both proprietary and industry-specific data to generate deeper insights, reduce operational costs, and accelerate decision-making processes.
• In July 2025, NatWest Group announced a five-year collaboration with Amazon Web Services and Accenture to accelerate its bank-wide data and AI transformation. The initiative focused on consolidating various data streams into a single, unified platform to enable more personalized and engaging customer services. By leveraging advanced cloud infrastructure and machine learning capabilities, the partnership aimed to modernize the bank's digital foundation, improve operational performance, and foster a data-driven culture. The agreement also included provisions for enhancing fraud detection and security measures through superior data quality and automated banking processes.
• In July 2024, JPMorgan Chase launched a proprietary generative artificial intelligence product named LLM Suite, designed to function as a research analyst for its workforce. The bank rolled out the tool to approximately 50,000 employees in its asset and wealth management division to assist with tasks such as writing, idea generation, and document summarization. Developed in-house to ensure strict adherence to regulatory requirements and data security, the platform leveraged large language models to streamline information flow and enhance productivity across the firm’s global operations, serving as a significant step in the bank's adoption of AI.
• In February 2024, The Bank of New York Mellon formed a strategic partnership with Microsoft to transform capital markets and the broader financial services industry using cloud and artificial intelligence technologies. The collaboration involved migrating the bank's data and analytics workloads to Microsoft Azure to develop a leading data management solution for buy-side and sell-side clients. By integrating the bank's extensive financial data capabilities with the cloud platform, the alliance aimed to help financial institutions navigate complex market dynamics, enhance risk management, and optimize operational efficiency through improved precision and agility.

Key Market Players

• International Business Machines Corporation
• SAP SE
• Oracle Corporation
• Microsoft Corporation
• Google LLC
• Amazon Web Services, Inc.
• TIBCO Software, Inc.
• Hewlett Packard Enterprise Co.
• SiSense, Inc.
• Mu Sigma, Inc.
• Dell, Inc.
• Alteryx Inc.
• Teradata Corporation
• Wipro Ltd.
• SAS Institute, Inc.

By Deployment Type	By Type	By Solution	By End User	By Region
On-Premises Cloud	Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics	Risk Management (Credit Risk Assessment, Fraud Detection and Management, Stress Testing, others) Customer Analytics Portfolio Management Analytics Trading Analytics	Sell Side Firms Buy Side Firms	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Data Analytics in Banking Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Data Analytics in Banking Market, By Deployment Type:

• On-Premises
• Cloud

• Data Analytics in Banking Market, By Type:

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

• Data Analytics in Banking Market, By Solution:

• Risk Management (Credit Risk Assessment, Fraud Detection and Management, Stress Testing, others)
• Customer Analytics
• Portfolio Management Analytics
• Trading Analytics

• Data Analytics in Banking Market, By End User:

• Sell Side Firms
• Buy Side Firms

• Data Analytics in Banking Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE