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市場調查報告書
商品編碼
2035285

2034年銀行業市場預測－全球分析（依分析類型、資料來源、應用程式、部署模式、最終使用者和地區分類）

Predictive Analytics for Banking Market Forecasts to 2034 - Global Analysis By Analytics Type, Data Source, Application, Deployment Mode, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球銀行業預測分析市場規模將達到 230.4 億美元，在預測期內以 15.8% 的複合年成長率成長，到 2034 年將達到 745.1 億美元。

銀行預測分析利用進階分析、機器學習和統計模型來預測客戶行為、財務風險和市場趨勢。銀行使用這些工具進行信用評分、詐欺偵測、客戶維繫和收入最佳化。透過分析歷史數據和即時數據，預測分析能夠幫助銀行做出前瞻性決策並提供個人化金融服務。銀行業數位化進程的推進、數據可用性的提高以及日益激烈的競爭，正在推動銀行採用預測分析來提升效率、盈利和客戶體驗。

對數據驅動決策的需求日益成長

預測分析使金融機構能夠擺脫對直覺的依賴，轉而基於可量化的洞察做出決策。這種需求在信用風險評估、詐欺偵測和客戶參與等領域尤為明顯。透過利用預測模型，銀行可以最佳化營運並提高盈利。隨著金融生態系統日益複雜，依賴數據驅動的決策至關重要。因此，對可執行洞察日益成長的需求成為市場成長的主要驅動力。

資料孤島會阻礙分析的有效性。

跨部門資訊孤島式儲存會降低分析的準確性和效率。整合分散的資料集需要對基礎設施和管治進行大量投資。這些挑戰往往導致應用延遲和擴充性受限。小規模的金融機構尤其難以克服資訊孤島式架構。因此，資料孤島仍是銀行業充分發揮預測分析潛力的主要限制因素。

人工智慧驅動的客戶行為預測

人工智慧模型為銀行提供了一個強大的契機，使其能夠更精準地預測客戶行為。透過分析交易歷史、生活方式模式和數位化互動，金融機構可以提供量身定做的個人化服務。這種個人化服務能夠提升客戶忠誠度，並創造交叉銷售機會。預測分析還支援主動式客戶參與，例如預測貸款需求和投資偏好。將人工智慧融入客戶分析，能為銀行創造新的收入來源。隨著人工智慧行為預測技術的普及，它將成為市場成長的主要驅動力。

不準確的預測會影響結果

基於不完整或偏差資料訓練的模型可能會產生誤導結果。此類錯誤會導致不恰當的貸款決策、無效的詐欺偵測或錯誤的客戶策略。在銀行業等受監管行業，此類不準確之處可能導致合規問題和財務損失。過度依賴有缺陷的預測會削弱人們對分析系統的信心。缺乏強而有力的檢驗，不準確的結果將持續威脅市場信心。

新冠疫情的影響：

新冠疫情改變了銀行業的工作重點，加速了數位轉型和風險管理的必要性。危機期間，預測分析對於建立客戶違約、流動性風險和交易異常模型至關重要。金融機構依靠數據驅動工具來應對不確定性並保持韌性。同時，預算限制減緩了部分地區的新投資。疫情凸顯了在動盪環境中應用預測分析的必要性與挑戰。總體而言，儘管存在短期障礙，但新冠疫情加速了預測分析的長期應用。

在預測期內，交易資料區段預計將佔據最大的市場佔有率。

預計在預測期內，交易資料區段將佔據最大的市場佔有率，因為它構成了銀行業預測分析的基礎。交易層面的洞察能夠提供關於客戶支出、信用狀況和詐欺風險的關鍵資訊。銀行越來越依賴這些數據來設計個人化產品並加強其風險管理系統。監管機構對透明數據使用的支持進一步增強了其優勢。分析工具的持續創新正在提升交易資料集的效用。

在預測期內，個人化銀行服務領域預計將呈現最高的複合年成長率。

在預測期內，個人化銀行服務領域預計將呈現最高的成長率，這主要得益於客戶對客製化金融體驗日益成長的需求。客戶期望銀行能夠預見他們的需求並提供個人化的解決方案。預測分析透過分析行為模式和偏好，實現了高度個人化。數位銀行平台的普及進一步加速了這一趨勢。投資個人化服務的金融機構在客戶維繫將獲得競爭優勢。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其先進的金融基礎設施和對分析技術的廣泛應用。主要銀行和金融科技創新者的存在進一步鞏固了該地區的領先地位。法律規範促進了透明度和數據驅動型實踐。消費者對數位銀行服務的高需求正在加速其普及。對人工智慧和巨量資料平台的投資正在提升預測能力。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的數位轉型和不斷擴展的金融生態系統。印度、中國和新加坡等國家在銀行業預測分析領域處於創新主導。行動網際網路普及率的提高和數位支付的日益普及為分析平台創造了有利環境。政府主導的金融科技發展支持措施進一步加速了其應用。該地區多元化的基本客群正在推動個人化銀行服務的創新。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球銀行業預測分析市場：依分析類型分類

  • 客戶行為分析
  • 信用風險預測
  • 詐欺預測
  • 營收和利潤預測
  • 取消預測
  • 其他分析類型

第6章：全球銀行業預測分析市場：依資料來源分類

  • 交易數據
  • 客戶數據
  • 市場和經濟數據
  • 數位互動數據
  • 其他數據來源

第7章：全球銀行業預測分析市場：依應用領域分類

  • 客戶區隔與目標定位
  • 風險與合規管理
  • 個人化銀行服務
  • 交叉銷售和提升銷售
  • 其他用途

第8章：全球銀行業預測分析市場：依部署模式分類

  • 基於雲端的解決方案
  • 本地部署解決方案

第9章：全球銀行業預測分析市場：依最終用戶分類

  • 零售銀行
  • 商業銀行
  • 投資銀行
  • 新銀行
  • 其他最終用戶

第10章：全球銀行業預測分析市場：依地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • IBM Corporation
  • Oracle Corporation
  • SAP SE
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services（AWS）
  • SAS Institute Inc.
  • FICO（Fair Isaac Corporation）
  • Moody's Analytics
  • FIS Global
  • Fiserv, Inc.
  • Temenos AG
  • Finastra
  • Accenture plc
  • Cognizant Technology Solutions
  • Tata Consultancy Services（TCS）
  • Infosys Limited
  • Wipro Limited
Product Code: SMRC35549

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

List of Tables

  • Table 1 Global Predictive Analytics for Banking Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Predictive Analytics for Banking Market, By Analytics Type (2023-2034) ($MN)
  • Table 3 Global Predictive Analytics for Banking Market, By Customer Behavior Analytics (2023-2034) ($MN)
  • Table 4 Global Predictive Analytics for Banking Market, By Credit Risk Prediction (2023-2034) ($MN)
  • Table 5 Global Predictive Analytics for Banking Market, By Fraud Prediction (2023-2034) ($MN)
  • Table 6 Global Predictive Analytics for Banking Market, By Revenue & Profit Forecasting (2023-2034) ($MN)
  • Table 7 Global Predictive Analytics for Banking Market, By Churn Prediction (2023-2034) ($MN)
  • Table 8 Global Predictive Analytics for Banking Market, By Other Analytics Types (2023-2034) ($MN)
  • Table 9 Global Predictive Analytics for Banking Market, By Data Source (2023-2034) ($MN)
  • Table 10 Global Predictive Analytics for Banking Market, By Transaction Data (2023-2034) ($MN)
  • Table 11 Global Predictive Analytics for Banking Market, By Customer Data (2023-2034) ($MN)
  • Table 12 Global Predictive Analytics for Banking Market, By Market & Economic Data (2023-2034) ($MN)
  • Table 13 Global Predictive Analytics for Banking Market, By Digital Interaction Data (2023-2034) ($MN)
  • Table 14 Global Predictive Analytics for Banking Market, By Other Data Sources (2023-2034) ($MN)
  • Table 15 Global Predictive Analytics for Banking Market, By Application (2023-2034) ($MN)
  • Table 16 Global Predictive Analytics for Banking Market, By Customer Segmentation & Targeting (2023-2034) ($MN)
  • Table 17 Global Predictive Analytics for Banking Market, By Risk & Compliance Management (2023-2034) ($MN)
  • Table 18 Global Predictive Analytics for Banking Market, By Personalized Banking Services (2023-2034) ($MN)
  • Table 19 Global Predictive Analytics for Banking Market, By Cross-Selling & Upselling (2023-2034) ($MN)
  • Table 20 Global Predictive Analytics for Banking Market, By Other Applications (2023-2034) ($MN)
  • Table 21 Global Predictive Analytics for Banking Market, By Deployment Mode (2023-2034) ($MN)
  • Table 22 Global Predictive Analytics for Banking Market, By Cloud-Based Solutions (2023-2034) ($MN)
  • Table 23 Global Predictive Analytics for Banking Market, By On-Premises Solutions (2023-2034) ($MN)
  • Table 24 Global Predictive Analytics for Banking Market, By End User (2023-2034) ($MN)
  • Table 25 Global Predictive Analytics for Banking Market, By Retail Banks (2023-2034) ($MN)
  • Table 26 Global Predictive Analytics for Banking Market, By Commercial Banks (2023-2034) ($MN)
  • Table 27 Global Predictive Analytics for Banking Market, By Investment Banks (2023-2034) ($MN)
  • Table 28 Global Predictive Analytics for Banking Market, By Neobanks (2023-2034) ($MN)
  • Table 29 Global Predictive Analytics for Banking Market, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.