한글목차

세계 은행업용 예측 분석 시장 전망은 유망하며, 중소기업 시장과 대기업 시장에서 기회가 있을 것으로 보입니다. 세계 뱅킹 예측 분석 시장은 2025-2031년 20.6%의 연평균 복합 성장률(CAGR)을 보일 것으로 예측됩니다. 이 시장의 주요 촉진요인은 AI를 활용한 분석의 채택이 증가하고 있으며, 부정행위 감지 솔루션에 대한 요구가 증가하고 있다는 점입니다.

• Lucintel의 예측에 따르면, 유형별로는 고객 분석이 예측 기간 동안 가장 높은 성장세를 보일 것으로 예측됩니다.
• 용도별로는 중소기업이 높은 성장을 이룰 것으로 예측됩니다.
• 지역별로는 아시아태평양이 예측 기간 동안 가장 높은 성장을 보일 것으로 예측됩니다.

은행업용 예측 분석 시장의 새로운 트렌드

오늘날 은행업용 예측 분석 산업은 은행의 고객 이해, 리스크 처리, 업무 추진 방식을 재정의하는 다양한 주요 트렌드의 영향을 받고 있습니다. 이러한 추세는 최신 기술과 점점 더 방대해지는 데이터를 활용하고 있습니다.

• 실시간 예측 분석 : 즉각적인 대출 실행, 거래 중 사기 경고, 고객 참여 순간에 타겟팅된 제안 등 즉각적인 의사결정을 내리고 고객 경험을 개선하고 위험을 줄이기 위해 은행들은 실시간 예측 분석을 빠르게 도입하고 있습니다. 이러한 순간적인 성격은 반응과 고객의 기쁨을 높여줍니다.
• 신뢰와 투명성을 높이는 설명 가능한 AI: 보다 정교한 AI 모델의 적용이 증가함에 따라 예측이 어떻게 도출되었는지에 대한 통찰력을 제공할 수 있는 설명 가능한 AI에 대한 수요가 증가하고 있습니다. 이는 규제적 요구, 고객 신뢰, 은행 업무에서 자동화된 의사결정을 인간이 모니터링할 수 있는 능력에 필수적입니다.
• 공동 데이터 분석을 위한 통합 학습: 은행들은 데이터 프라이버시 문제와 규제 장벽을 극복하기 위해 통합 학습을 고려하고 있습니다. 통합 학습을 통해 여러 금융기관이 민감한 고객 데이터를 교환하지 않고도 AI 모델을 공동으로 학습할 수 있어 보다 종합적이고 강력한 예측 통찰력을 확보할 수 있습니다. 공동 학습 방식은 데이터 프라이버시를 보호합니다.
• 자연어 처리 내장: 은행들은 고객 서비스 콜, 소셜 미디어, 뉴스 피드와 같은 비정형 데이터를 분석하여 고객 태도, 위험 요소, 시장 개발 동향을 더 깊이 이해하고 예측력을 높이기 위해 자연어 처리(NLP)를 활용하는 사례가 증가하고 있습니다. 하는 사례가 늘고 있습니다. 이를 통해 기존과는 다른 소스에서 풍부한 정보를 얻을 수 있습니다.
• 개인화된 금융 웰니스를 위한 예측 분석 : 기존 은행 상품과는 별도로 개인화된 금융 웰니스 가이던스, 예산 관리 기능, 사전 예방적 제안을 제공하기 위해 예측 분석을 활용하는 새로운 트렌드가 등장하고 있습니다. 이는 트랜잭션 뱅킹의 범주를 넘어선다.

이러한 추세는 은행업용 예측 분석 시장을 보다 나은 의사결정을 촉진하고 전반적인 뱅킹 경험을 향상시키는 실시간, 투명성, 협업, 고객 중심의 솔루션으로 변화시키고 있습니다.

은행업용 예측 분석 시장의 최근 동향

오늘날 은행업용 예측 분석 산업은 정확성과 효율성을 극대화하고 데이터 활용의 윤리적 요소를 고려하기 위해 중요한 진화를 거듭하고 있습니다. 이러한 발전은 은행이 경쟁력을 확보하고 소비자의 신뢰를 얻는 데 도움이 되고 있습니다. 그 추진력은 책임감과 큰 영향력을 가진 AI로 향하고 있습니다.

• 모델 배포를 가속화하는 AutoML 플랫폼의 새로운 혁신 : AutoML 플랫폼이 비약적으로 발전하면서 은행이 적은 인력으로 더 빠르게 예측 모델을 개발할 수 있게 되었고, 많은 은행 기능에서 분석의 신속한 도입이 촉진되고 있습니다. 촉진하고 있습니다.
• 피처 엔지니어링과 선택에 대한 중요성: 은행들은 데이터에서 유용한 시그널을 추출하기 위한 고급 피처 엔지니어링 기법과 예측 모델의 정확도와 해석 가능성을 높이기 위한 고급 피처 선택 기법에 더 많은 자금을 투자하고 있습니다.
• 강력한 모델 모니터링 및 거버넌스 모델 개발: 변화하는 고객 데이터와 행동의 특성을 이해하고, 은행은 예측 모델의 성능을 지속적으로 모니터링할 수 있는 강력한 모델을 개발하고, 장기적으로 정확성을 유지할 수 있는 거버넌스를 구축하여 편향성을 제어하고 있습니다.
• 그래프 데이터베이스 통합을 통한 관계 분석 향상: 은행들은 부정행위 감지 및 신용 리스크 분석에서 보다 정확한 예측을 위해 고객 네트워크, 거래 패턴 등 데이터 내 복잡한 관계를 보다 정확하게 분석하기 위해 그래프 데이터베이스의 활용을 늘리고 있습니다.
• 프라이버시 보호 AI 기법 주목: 데이터 프라이버시 관련 법규가 강화되는 가운데, 은행들은 고객 데이터를 훼손하지 않고 예측 분석에 데이터를 활용하기 위해 차등 프라이버시, 동형 암호화 등 프라이버시 보호 AI 기법을 도입하고 통합하고 있습니다.

이러한 추세는 보다 정확하고 신뢰할 수 있는 모델의 신속한 배포, 복잡한 데이터 관계의 더 나은 이해, 윤리와 프라이버시를 중시하는 데이터 활용을 촉진함으로써 시장에서 은행의 예측 분석에 영향을 미치고 있습니다.

목차

제1장 주요 요약

제2장 시장 개요

• 배경과 분류
• 공급망

제3장 시장 동향과 예측 분석

• 거시경제 동향과 예측
• 업계 촉진요인과 과제
• PESTLE 분석
• 특허 분석
• 규제 환경

제4장 세계의 은행업용 예측 분석 시장 : 유형별

• 개요
• 매력 분석 : 유형별
• 고객 분석 : 동향과 예측(2019-2031년)
• 화이트 칼라 자동화 : 동향과 예측(2019-2031년)
• 신용점수 산정 : 동향과 예측(2019-2031년)
• 거래 분석 : 동향과 예측(2019-2031년)
• 기타 : 동향과 예측(2019-2031년)

제5장 세계의 은행업용 예측 분석 시장 : 용도별

• 개요
• 매력 분석 : 용도별
• 중소기업 : 동향과 예측(2019-2031년)
• 대기업 : 동향과 예측(2019-2031년)

제6장 지역 분석

• 개요
• 세계의 은행업용 예측 분석 시장 : 지역별

제7장 북미의 은행업용 예측 분석 시장

• 개요
• 북미의 은행업용 예측 분석 시장 : 유형별
• 북미의 은행업용 예측 분석 시장 : 용도별
• 미국의 은행업용 예측 분석 시장
• 멕시코의 은행업용 예측 분석 시장
• 캐나다의 은행업용 예측 분석 시장

제8장 유럽의 은행업용 예측 분석 시장

• 개요
• 유럽의 은행업용 예측 분석 시장 : 유형별
• 유럽의 은행업용 예측 분석 시장 : 용도별
• 독일의 은행업용 예측 분석 시장
• 프랑스의 은행업용 예측 분석 시장
• 스페인의 은행업용 예측 분석 시장
• 이탈리아의 은행업용 예측 분석 시장
• 영국의 은행업용 예측 분석 시장

제9장 아시아태평양의 은행업용 예측 분석 시장

• 개요
• 아시아태평양의 은행업용 예측 분석 시장 : 유형별
• 아시아태평양의 은행업용 예측 분석 시장 : 용도별
• 일본의 은행업용 예측 분석 시장
• 인도의 은행업용 예측 분석 시장
• 중국의 은행업용 예측 분석 시장
• 한국의 은행업용 예측 분석 시장
• 인도네시아의 은행업용 예측 분석 시장

제10장 기타 지역(ROW)의 은행업용 예측 분석 시장

• 개요
• 기타 지역(ROW)의 은행업용 예측 분석 시장 : 유형별
• 기타 지역(ROW)의 은행업용 예측 분석 시장 : 용도별
• 중동의 은행업용 예측 분석 시장
• 남미의 은행업용 예측 분석 시장
• 아프리카의 은행업용 예측 분석 시장

제11장 경쟁 분석

• 제품 포트폴리오 분석
• 운영 통합
• Porter의 Five Forces 분석
  • 경쟁 기업간 경쟁 관계
  • 바이어의 교섭력
  • 공급업체의 교섭력
  • 대체품의 위협
  • 신규 진출업체의 위협
• 시장 점유율 분석

제12장 기회와 전략 분석

• 밸류체인 분석
• 성장 기회 분석
  • 성장 기회 : 유형별
  • 성장 기회 : 용도별
• 세계 은행업용 예측 분석 시장의 새로운 동향
• 전략 분석
  • 신제품 개발
  • 인증 및 라이선싱
  • 인수합병(M&A), 계약, 제휴 및 합작투자(JV)

제13장 밸류체인 주요 기업 개요

• Competitive Analysis
• Accretive Technologies
• Angoss Software Corporation
• FICO
• HP
• IBM
• Information Builders
• KXEN
• Microsoft
• Oracle
• Salford Systems

제14장 부록

• 그림 리스트
• 표 리스트
• 분석 방법
• 면책사항
• 저작권
• 약어와 기술 단위
• Lucintel에 대해
• 문의

영문목차

The future of the global predictive analytics in banking market looks promising with opportunities in the small & medium enterprise and large enterprise markets. The global predictive analytics in banking market is expected to grow with a CAGR of 20.6% from 2025 to 2031. The major drivers for this market are the rising adoption of AI-driven analytics, and the growing need for fraud detection solutions.

• Lucintel forecasts that, within the type category, customer analytics is expected to witness the highest growth over the forecast period.
• Within the application category, small & medium enterprise is expected to witness higher growth.
• In terms of region, APAC is expected to witness the highest growth over the forecast period.

Emerging Trends in the Predictive Analytics in Banking Market

The predictive analytics banking industry is today influenced by a range of key trends that are redefining how banks understand customers, handle risk, and drive their operations. These trends tap into the latest technologies and increasingly large pools of data.

• Real-Time Predictive Analytics: Banks are fast embracing real-time predictive analytics in order to take instant decisions like instant loan disbursements, fraud warnings in the middle of a transaction, and targeted offerings at the moment of engagement, improving customer experience and lowering risk. This in-the-moment nature enhances response and customer delight.
• Explainable AI for Fostering Trust and Transparency: As more sophisticated AI models find increased application, there is increasing demand for explainable AI that gives insight into how predictions were arrived at. This is imperative for regulatory needs, customer trust, and the ability to exercise human oversight of automated decisions within banking.
• Federated Learning for Collaborative Data Analysis: Banks are considering federated learning to overcome data privacy issues and regulatory barriers. Federated learning enables multiple institutions to train AI models jointly without exchanging sensitive customer data, facilitating more comprehensive and robust predictive insights. The collaborative method preserves data privacy.
• Incorporation of Natural Language Processing: NLP is more and more used by banks to analyze unstructured data from non-traditional sources such as customer service calls, social media, and news feeds to develop a better understanding of customer attitudes, emerging risk, and market trends, boosting predictive power. This opens up rich information from non-traditional sources.
• Predictive Analytics for Personalized Financial Wellness: Aside from legacy banking products, there's a new trend of utilizing predictive analytics to provide personalized financial wellness guidance, budgeting capabilities, and proactive suggestions to empower customers to better manage their finances, creating deeper customer relationships and loyalty. This is beyond transactional banking.

These trends collectively are transforming the predictive analytics in banking market into more real-time, transparent, collaborative, and customer-centric solutions that facilitate better decision-making and improve the overall banking experience.

Recent Developments in the Predictive Analytics in Banking Market

The predictive analytics in banking industry today is undergoing key advancements aimed at maximizing accuracy, efficiency, as well as considering ethical factors of using data. The advancements help the banks achieve competitiveness and obtain trust from consumers. The push is towards AI with responsible as well as significant impact.

• Emerging Innovations in AutoML Platforms Facilitating Quick Deployment of Models: AutoML platforms are advancing by leaps and bounds, making it possible for banks to develop predictive models faster using less human effort, driving quick adoption of analytics across many bank functions.
• Greater Emphasis on Feature Engineering and Selection: Banks are putting more money into sophisticated feature engineering methods to draw useful signals out of their data and using advanced feature selection techniques to enhance the accuracy and interpretability of their predictive models.
• Development of Strong Model Monitoring and Governance Models: Understanding the ever-changing nature of customer data and behavior, banks are developing strong models for constant monitoring of their predictive models' performance and governance to control bias and sustain accuracy over time.
• Graph Database Integration for Improved Relationship Analysis: Banks are increasingly using graph databases to better analyze intricate relationships in their data, including customer networks and patterns of transactions, to make more precise predictions in fraud detection and credit risk analysis.
• Focus on Privacy-Preserving AI Methods: As increasing data privacy laws, banks are adopting and integrating privacy-preserving AI methods, including differential privacy and homomorphic encryption, to use data for predictive analytics without compromising customer data.

These trends are influencing the banking predictive analytics in market by facilitating quicker deployment of more accurate and trustworthy models, better understanding of intricate data relationships, and focus on ethics and privacy-driven use of data.

Strategic Growth Opportunities in the Predictive Analytics in Banking Market

The predictive analytics in banking market has significant strategic growth opportunities across different applications based on the prospect of optimizing revenues, lowering costs, and improving customer relationships. Data-driven insights can revolutionize different aspects of banking operations.

• Improved Customer Acquisition and Retention: Predictive analytics can detect potential high-value customers and forecast churn risk, allowing banks to execute targeted marketing campaigns and proactive retention initiatives, resulting in higher market share and customer loyalty.
• Better Credit Risk Evaluation and Loan Origination: Using advanced predictive models to evaluate creditworthiness, predict default probabilities, and automate loan origination processes can result in better lending decisions and lower credit losses.
• Proactive Fraud Detection and Prevention: Predictive analytics in real-time can recognize unusual patterns in transactions and foresee fraudulent activities more accurately, keeping financial losses by the bank as well as customers to a bare minimum.
• Personalized Product Recommendations and Cross-Selling: Using predictive models, banks can comprehend individual customers' needs and likes and recommend very relevant products as well as opportunities for cross-selling, thus maximizing revenue and satisfaction.
• Optimized Branch Operations and Resource Planning: Predictive analytics can predict customer traffic, transaction levels, and branch staffing requirements, allowing for optimized resource planning, lower operational expenses, and enhanced customer service efficiency.

These strategic growth prospects demonstrate the value creation potential of predictive analytics throughout the banking value chain, from customer acquisition and retention to risk management and operation optimization, ultimately leading to profitability and competitiveness enhancement.

Predictive Analytics in Banking Market Driver and Challenges

Banking predictive analytics market is driven by a strong synergy of forces highlighting the growing prominence of data-informed decision-making in finance as well as having major challenges capable of limiting widespread and efficient usage. To tackle this dynamic developing landscape, appreciating these drivers is imperative.

The factors responsible for driving the predictive analytics in banking market include:

1. Exponential Growth in Volume and Variety of Data: The huge volumes of data created through banking transactions and customer interactions present a fertile ground for leveraging predictive analytics to extract valuable insights.

2. Improvements in Artificial Intelligence and Machine Learning: Ongoing improvements in AI and ML algorithms make it possible to create more complex and accurate predictive models for numerous banking applications.

3. Growing Regulatory Attention to Risk Management and Compliance: Regulatory demands for strengthening risk management, fraud prevention, and meeting anti-money laundering requirements propel predictive analytics adoption in the interest of better oversight.

4. Rising Customer Expectations of Personalized Services: Customers now increasingly demand personal and relevant financial products and services, which can be effectively offered by banks using predictive analytics.

5. Competitive Pressure from FinTech's and Digital-Native Banks: The emergence of nimble fintech firms and neobanks that use data analytics adds to the pressure on traditional banks to gain similar capabilities in order to be competitive.

Challenges in the predictive analytics in banking market are:

1. Data Privacy and Security Concerns: The confidential nature of financial information calls for severe data privacy and security protocols that make data access and use more challenging for predictive analytics.

2. Legacy IT Infrastructure and Data Silos: Most conventional banks are plagued by legacy IT systems and isolated data silos, which prevent smooth integration and analysis of data to support effective predictive modeling.

3. Lack of Qualified Data Scientists and Analysts: Insufficient experts with the right skills in data science, machine learning, and banking domain knowledge can slow the creation and deployment of sophisticated analytics solutions.

Strong forces of data growth, technology breakthroughs, and regulatory requirements are driving predictive analytics adoption in the banking sector. But to benefit fully from predictive analytics' disruptive power, it is essential that banks overcome barriers to data privacy, legacy, and talent onboarding.

List of Predictive Analytics in Banking Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. With these strategies predictive analytics in banking companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the predictive analytics in banking companies profiled in this report include-

• Accretive Technologies
• Angoss Software Corporation
• FICO
• HP
• IBM
• Information Builders
• KXEN
• Microsoft
• Oracle
• Salford Systems

Predictive Analytics in Banking Market by Segment

The study includes a forecast for the global predictive analytics in banking market by type, application, and region.

Predictive Analytics in Banking Market by Type [Value from 2019 to 2031]:

• Customer Analytics
• White-Collar Automation
• Credit Scoring
• Trading Insight
• Others

Predictive Analytics in Banking Market by Application [Value from 2019 to 2031]:

• Small & Medium Enterprises
• Large Enterprises

Predictive Analytics in Banking Market by Region [Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Predictive Analytics in Banking Market

The global predictive analytics in banking industry is increasingly using predictive analytics to better understand customer behavior, streamline operations, and manage risks. Advances in artificial intelligence, machine learning, and big data technologies over the past few years are powering major trends in how banks in leading economies are applying predictive analytics to improve their competitive advantage and respond to changing market conditions.

• United States: Emphasis on fraud detection and custom individual experiences. The latest innovations involve advanced AI-driven systems for real-time fraud detection and the application of prediction models in providing highly customized products and services to enhance customer retention and acquisition in a competitive marketplace.
• China: Accelerating adoption in digital banking and credit scoring. China's banks are fast embracing predictive analytics, specifically digital banking platforms for risk assessment, credit scoring for an extensive unbanked population, and targeted marketing in their expansive digital ecosystems.
• Germany: Regulatory compliance and risk management focus. Current developments in Germany center on using predictive analytics for more effective risk management, such as credit risk measurement and anti-money laundering initiatives, while meeting strict data privacy rules and compliance measures.
• India: Expansion of digital lending and financial inclusion programs. India is experiencing greater application of predictive analytics to the growing space of digital lending to determine creditworthiness and extend financial inclusion to underpenetrated markets, frequently relying on alternative sources of data.
• Japan: Customer retention and operational effectiveness in a saturated market. New trends in Japan highlight the deployment of predictive analytics to enhance customer retention in an established banking industry and operational efficiency through forecasting and resource management.

Features of the Global Predictive Analytics in Banking Market

• Market Size Estimates: Predictive analytics in banking market size estimation in terms of value ($B).
• Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.
• Segmentation Analysis: Predictive analytics in banking market size by type, application, and region in terms of value ($B).
• Regional Analysis: Predictive analytics in banking market breakdown by North America, Europe, Asia Pacific, and Rest of the World.
• Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the predictive analytics in banking market.
• Strategic Analysis: This includes M&A, new product development, and competitive landscape of the predictive analytics in banking market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the predictive analytics in banking market by type (customer analytics, white-collar automation, credit scoring, trading insight, and others), application (small & medium enterprises and large enterprises), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Market Overview

• 2.1 Background and Classifications
• 2.2 Supply Chain

3. Market Trends & Forecast Analysis

• 3.1 Macroeconomic Trends and Forecasts
• 3.2 Industry Drivers and Challenges
• 3.3 PESTLE Analysis
• 3.4 Patent Analysis
• 3.5 Regulatory Environment

4. Global Predictive Analytics in Banking Market by Type

• 4.1 Overview
• 4.2 Attractiveness Analysis by Type
• 4.3 Customer Analytics: Trends and Forecast (2019-2031)
• 4.4 White-Collar Automation: Trends and Forecast (2019-2031)
• 4.5 Credit Scoring: Trends and Forecast (2019-2031)
• 4.6 Trading Insight: Trends and Forecast (2019-2031)
• 4.7 Others: Trends and Forecast (2019-2031)

5. Global Predictive Analytics in Banking Market by Application

• 5.1 Overview
• 5.2 Attractiveness Analysis by Application
• 5.3 Small & Medium Enterprises: Trends and Forecast (2019-2031)
• 5.4 Large Enterprises: Trends and Forecast (2019-2031)

6. Regional Analysis

• 6.1 Overview
• 6.2 Global Predictive Analytics in Banking Market by Region

7. North American Predictive Analytics in Banking Market

• 7.1 Overview
• 7.2 North American Predictive Analytics in Banking Market by Type
• 7.3 North American Predictive Analytics in Banking Market by Application
• 7.4 United States Predictive Analytics in Banking Market
• 7.5 Mexican Predictive Analytics in Banking Market
• 7.6 Canadian Predictive Analytics in Banking Market

8. European Predictive Analytics in Banking Market

• 8.1 Overview
• 8.2 European Predictive Analytics in Banking Market by Type
• 8.3 European Predictive Analytics in Banking Market by Application
• 8.4 German Predictive Analytics in Banking Market
• 8.5 French Predictive Analytics in Banking Market
• 8.6 Spanish Predictive Analytics in Banking Market
• 8.7 Italian Predictive Analytics in Banking Market
• 8.8 United Kingdom Predictive Analytics in Banking Market

9. APAC Predictive Analytics in Banking Market

• 9.1 Overview
• 9.2 APAC Predictive Analytics in Banking Market by Type
• 9.3 APAC Predictive Analytics in Banking Market by Application
• 9.4 Japanese Predictive Analytics in Banking Market
• 9.5 Indian Predictive Analytics in Banking Market
• 9.6 Chinese Predictive Analytics in Banking Market
• 9.7 South Korean Predictive Analytics in Banking Market
• 9.8 Indonesian Predictive Analytics in Banking Market

10. ROW Predictive Analytics in Banking Market

• 10.1 Overview
• 10.2 ROW Predictive Analytics in Banking Market by Type
• 10.3 ROW Predictive Analytics in Banking Market by Application
• 10.4 Middle Eastern Predictive Analytics in Banking Market
• 10.5 South American Predictive Analytics in Banking Market
• 10.6 African Predictive Analytics in Banking Market

11. Competitor Analysis

• 11.1 Product Portfolio Analysis
• 11.2 Operational Integration
• 11.3 Porter's Five Forces Analysis
  • Competitive Rivalry
  • Bargaining Power of Buyers
  • Bargaining Power of Suppliers
  • Threat of Substitutes
  • Threat of New Entrants
• 11.4 Market Share Analysis

12. Opportunities & Strategic Analysis

• 12.1 Value Chain Analysis
• 12.2 Growth Opportunity Analysis
  • 12.2.1 Growth Opportunities by Type
  • 12.2.2 Growth Opportunities by Application
• 12.3 Emerging Trends in the Global Predictive Analytics in Banking Market
• 12.4 Strategic Analysis
  • 12.4.1 New Product Development
  • 12.4.2 Certification and Licensing
  • 12.4.3 Mergers, Acquisitions, Agreements, Collaborations, and Joint Ventures

13. Company Profiles of the Leading Players Across the Value Chain

• 13.1 Competitive Analysis
• 13.2 Accretive Technologies
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.3 Angoss Software Corporation
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.4 FICO
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.5 HP
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.6 IBM
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.7 Information Builders
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.8 KXEN
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.9 Microsoft
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.10 Oracle
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.11 Salford Systems
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing

14. Appendix

• 14.1 List of Figures
• 14.2 List of Tables
• 14.3 Research Methodology
• 14.4 Disclaimer
• 14.5 Copyright
• 14.6 Abbreviations and Technical Units
• 14.7 About Us
• 14.8 Contact Us