은행 업계용 예측 분석 시장 예측 - 분석 유형, 데이터 소스, 용도, 도입 형태, 최종사용자, 지역별 분석(-2034년)

According to Stratistics MRC, the Global Predictive Analytics for Banking Market is accounted for $23.04 billion in 2026 and is expected to reach $74.51 billion by 2034 growing at a CAGR of 15.8% during the forecast period. Predictive Analytics for Banking uses advanced analytics, machine learning, and statistical models to forecast customer behavior, financial risks, and market trends. Banks use these tools for credit scoring, fraud detection, customer retention, and revenue optimization. By analyzing historical and real-time data, predictive analytics enables proactive decision-making and personalized financial services. Increasing digitalization, data availability, and competition in the banking sector are driving the adoption of predictive analytics to improve efficiency, profitability, and customer experience.

Market Dynamics:

Driver:

Rising demand for data-driven decisions

Predictive analytics empowers institutions to move beyond intuition and base decisions on quantifiable insights. This demand is particularly strong in areas such as credit risk assessment, fraud detection, and customer engagement. By leveraging predictive models, banks can optimize operations and improve profitability. The growing complexity of financial ecosystems makes reliance on data-driven decisions indispensable. As a result, rising demand for actionable insights is a key driver of market growth.

Restraint:

Data silos limiting analytics effectiveness

Information stored in silos across departments reduces the accuracy and efficiency of analytics. Integrating disparate datasets requires significant investment in infrastructure and governance. These challenges often delay implementation and limit scalability. Smaller institutions, in particular, face difficulties in overcoming siloed architectures. Consequently, data silos remain a major restraint on the full potential of predictive analytics in banking.

Opportunity:

AI-enhanced customer behavior predictions

AI-driven models present a strong opportunity for banks to predict customer behavior with greater precision. By analyzing transaction histories, lifestyle patterns, and digital interactions, institutions can tailor services to individual needs. This personalization enhances customer loyalty and drives cross-selling opportunities. Predictive analytics also supports proactive engagement, such as anticipating loan requirements or investment preferences. The integration of AI into customer analytics creates new revenue streams for banks. As adoption accelerates, AI-enhanced behavior prediction will be a major growth lever for the market.

Threat:

Inaccurate predictions affecting outcomes

Models trained on incomplete or biased data can produce misleading results. Such errors may lead to poor lending decisions, ineffective fraud detection, or misguided customer strategies. In regulated industries like banking, these inaccuracies can result in compliance issues and financial losses. Overreliance on flawed predictions undermines trust in analytics systems. Without robust validation, inaccurate outcomes remain a persistent threat to market credibility.

Covid-19 Impact:

The Covid-19 pandemic reshaped banking priorities, accelerating digital adoption and risk management needs. Predictive analytics became vital in modeling customer defaults, liquidity risks, and transaction anomalies during the crisis. Institutions relied on data-driven tools to navigate uncertainty and maintain resilience. At the same time, budget constraints slowed new investments in some regions. The pandemic highlighted both the necessity and challenges of predictive analytics in volatile environments. Overall, Covid-19 acted as a catalyst for long-term adoption despite short-term hurdles.

The transaction data segment is expected to be the largest during the forecast period

The transaction data segment is expected to account for the largest market share during the forecast period as it forms the backbone of predictive analytics in banking. Transaction-level insights provide critical visibility into customer spending, creditworthiness, and fraud risks. Banks increasingly rely on this data to design personalized products and strengthen risk frameworks. Regulatory support for transparent data usage further reinforces its dominance. Continuous innovation in analytics tools enhances the utility of transaction datasets.

The personalized banking services segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the personalized banking services segment is predicted to witness the highest growth rate due to rising demand for tailored financial experiences. Customers expect banks to anticipate their needs and deliver customized solutions. Predictive analytics enables hyper-personalization by analyzing behavior patterns and preferences. The surge in digital banking platforms amplifies this trend. Institutions that invest in personalization gain a competitive edge in customer retention.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to its advanced financial infrastructure and strong adoption of analytics technologies. The presence of leading banks and fintech innovators reinforces regional dominance. Regulatory frameworks encourage transparency and data-driven practices. High consumer demand for digital banking services further accelerates adoption. Investments in AI and big data platforms strengthen predictive capabilities.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR driven by rapid digital transformation and expanding financial ecosystems. Countries such as India, China, and Singapore are spearheading innovation in predictive analytics for banking. Rising mobile penetration and digital payment adoption create fertile ground for analytics platforms. Government-backed initiatives supporting fintech growth further accelerate adoption. The region's diverse customer base encourages innovation in personalized banking services.

Key players in the market

Some of the key players in Predictive Analytics for Banking Market include IBM Corporation, Oracle Corporation, SAP SE, Microsoft Corporation, Google LLC, Amazon Web Services (AWS), SAS Institute Inc., FICO, Moody's Analytics, FIS Global, Fiserv, Inc., Temenos AG, Finastra, Accenture plc, Cognizant Technology Solutions, Tata Consultancy Services (TCS), Infosys Limited and Wipro Limited.

Key Developments:

In January 2026, Oracle Corporation and Microsoft expanded their Multi-cloud Partnership. This alliance allows banks to run Oracle Financial Services Analytics Cloud directly on Azure infrastructure, enabling seamless predictive modeling across siloed data sets without moving the underlying data.

In May 2025, FICO Launched the FICO(R) Platform Q2 '25 Release. This major product update introduced Focused Sequence Models (FSMs), which allow banks to ingest entire transaction histories to detect sophisticated "voice clone" fraud and predict total loss exposure with 45% faster execution speeds.

Analytics Types Covered:

• Customer Behavior Analytics
• Credit Risk Prediction
• Fraud Prediction
• Revenue & Profit Forecasting
• Churn Prediction
• Other Analytics Types

Data Sources Covered:

• Transaction Data
• Customer Data
• Market & Economic Data
• Digital Interaction Data
• Other Data Sources

Applications Covered:

• Customer Segmentation & Targeting
• Risk & Compliance Management
• Personalized Banking Services
• Cross-Selling & Upselling
• Other Applications

Deployment Modes Covered:

• Cloud-Based Solutions
• On-Premises Solutions

End Users Covered:

• Retail Banks
• Commercial Banks
• Investment Banks
• Neobanks
• Other End Users

Regions Covered:

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Spain
  • Netherlands
  • Belgium
  • Sweden
  • Switzerland
  • Poland
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Thailand
  • Malaysia
  • Singapore
  • Vietnam
  • Rest of Asia Pacific
• South America
  • Brazil
  • Argentina
  • Colombia
  • Chile
  • Peru
  • Rest of South America
• Rest of the World (RoW)
  • Middle East
• Saudi Arabia
• United Arab Emirates
• Qatar
• Israel
• Rest of Middle East
  • Africa
• South Africa
• Egypt
• Morocco
• Rest of Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

• 1.1 Market Snapshot and Key Highlights
• 1.2 Growth Drivers, Challenges, and Opportunities
• 1.3 Competitive Landscape Overview
• 1.4 Strategic Insights and Recommendations

2 Research Framework

• 2.1 Study Objectives and Scope
• 2.2 Stakeholder Analysis
• 2.3 Research Assumptions and Limitations
• 2.4 Research Methodology
  • 2.4.1 Data Collection (Primary and Secondary)
  • 2.4.2 Data Modeling and Estimation Techniques
  • 2.4.3 Data Validation and Triangulation
  • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

• 3.1 Market Definition and Structure
• 3.2 Key Market Drivers
• 3.3 Market Restraints and Challenges
• 3.4 Growth Opportunities and Investment Hotspots
• 3.5 Industry Threats and Risk Assessment
• 3.6 Technology and Innovation Landscape
• 3.7 Emerging and High-Growth Markets
• 3.8 Regulatory and Policy Environment
• 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

• 4.1 Porter's Five Forces Analysis
  • 4.1.1 Supplier Bargaining Power
  • 4.1.2 Buyer Bargaining Power
  • 4.1.3 Threat of Substitutes
  • 4.1.4 Threat of New Entrants
  • 4.1.5 Competitive Rivalry
• 4.2 Market Share Analysis of Key Players
• 4.3 Product Benchmarking and Performance Comparison

5 Global Predictive Analytics for Banking Market, By Analytics Type

• 5.1 Customer Behavior Analytics
• 5.2 Credit Risk Prediction
• 5.3 Fraud Prediction
• 5.4 Revenue & Profit Forecasting
• 5.5 Churn Prediction
• 5.6 Other Analytics Types

6 Global Predictive Analytics for Banking Market, By Data Source

• 6.1 Transaction Data
• 6.2 Customer Data
• 6.3 Market & Economic Data
• 6.4 Digital Interaction Data
• 6.5 Other Data Sources

7 Global Predictive Analytics for Banking Market, By Application

• 7.1 Customer Segmentation & Targeting
• 7.2 Risk & Compliance Management
• 7.3 Personalized Banking Services
• 7.4 Cross-Selling & Upselling
• 7.5 Other Applications

8 Global Predictive Analytics for Banking Market, By Deployment Mode

• 8.1 Cloud-Based Solutions
• 8.2 On-Premises Solutions

9 Global Predictive Analytics for Banking Market, By End User

• 9.1 Retail Banks
• 9.2 Commercial Banks
• 9.3 Investment Banks
• 9.4 Neobanks
• 9.5 Other End Users

10 Global Predictive Analytics for Banking Market, By Geography

• 10.1 North America
  • 10.1.1 United States
  • 10.1.2 Canada
  • 10.1.3 Mexico
• 10.2 Europe
  • 10.2.1 United Kingdom
  • 10.2.2 Germany
  • 10.2.3 France
  • 10.2.4 Italy
  • 10.2.5 Spain
  • 10.2.6 Netherlands
  • 10.2.7 Belgium
  • 10.2.8 Sweden
  • 10.2.9 Switzerland
  • 10.2.10 Poland
  • 10.2.11 Rest of Europe
• 10.3 Asia Pacific
  • 10.3.1 China
  • 10.3.2 Japan
  • 10.3.3 India
  • 10.3.4 South Korea
  • 10.3.5 Australia
  • 10.3.6 Indonesia
  • 10.3.7 Thailand
  • 10.3.8 Malaysia
  • 10.3.9 Singapore
  • 10.3.10 Vietnam
  • 10.3.11 Rest of Asia Pacific
• 10.4 South America
  • 10.4.1 Brazil
  • 10.4.2 Argentina
  • 10.4.3 Colombia
  • 10.4.4 Chile
  • 10.4.5 Peru
  • 10.4.6 Rest of South America
• 10.5 Rest of the World (RoW)
  • 10.5.1 Middle East
    • 10.5.1.1 Saudi Arabia
    • 10.5.1.2 United Arab Emirates
    • 10.5.1.3 Qatar
    • 10.5.1.4 Israel
    • 10.5.1.5 Rest of Middle East
  • 10.5.2 Africa
    • 10.5.2.1 South Africa
    • 10.5.2.2 Egypt
    • 10.5.2.3 Morocco
    • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

• 11.1 Industry Value Network and Supply Chain Assessment
• 11.2 White-Space and Opportunity Mapping
• 11.3 Product Evolution and Market Life Cycle Analysis
• 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

• 12.1 Mergers and Acquisitions
• 12.2 Partnerships, Alliances, and Joint Ventures
• 12.3 New Product Launches and Certifications
• 12.4 Capacity Expansion and Investments
• 12.5 Other Strategic Initiatives

13 Company Profiles

• 13.1 IBM Corporation
• 13.2 Oracle Corporation
• 13.3 SAP SE
• 13.4 Microsoft Corporation
• 13.5 Google LLC
• 13.6 Amazon Web Services (AWS)
• 13.7 SAS Institute Inc.
• 13.8 FICO (Fair Isaac Corporation)
• 13.9 Moody's Analytics
• 13.10 FIS Global
• 13.11 Fiserv, Inc.
• 13.12 Temenos AG
• 13.13 Finastra
• 13.14 Accenture plc
• 13.15 Cognizant Technology Solutions
• 13.16 Tata Consultancy Services (TCS)
• 13.17 Infosys Limited
• 13.18 Wipro Limited