Research Methodology – Predictive Analytics in Banking Market
At Straits Research, we adopt a rigorous 360° research approach that integrates both primary and secondary research methodologies. This ensures accuracy, reliability, and actionable insights for stakeholders. Our methodology for the Predictive Analytics in Banking Market comprises the following key stages:
Market Indicator & Macro-Factor Analysis
Our baseline thesis for the Predictive Analytics in Banking Market is developed by integrating key market indicators and macroeconomic variables. These include:
1 Factors considered while calculating market size and share
- Current number of banks implementing predictive analytics
- Rate of adoption of predictive analytics in the banking industry
- Annual budget allocated for predictive analytics by individual banks
- Revenue generated by predictive analytics solution providers in the banking sector
- Market penetration rate of predictive analytics in the banking sector
- Regional demographics including banking outreach and technology adoption
- Number of active customers in the banking sector
- Growth rate of digital banking solutions
2 Key Market Indicators
- Global and regional GDP growth rates
- Number of banks and other financial institutions
- Advancements in big data analytics and artificial intelligence
- Credit risk management trends in the banking sector
- Operating profit margins for banks
- Banking regulations and policies related to data handling and privacy
- Financial technology (FinTech) investment trends
- Adoption rates of digital banking services
3 Growth Trends
- Increasing adoption of predictive analytics for risk management in banking
- Growth in digital banking boosting the demand for predictive analytics
- Rising investment in FinTech driving the expansion of predictive analytics in the banking market
- Increasing use of AI and ML for predictive analytics in the banking sector
- Growth of personalised banking services through use of predictive analytics
- Increasing trend of using predictive analytics for customer segmentation
- Rising usage of predictive analytics for fraud detection in banking
- Growth in data volume due to digitalisation, driving the demand for predictive analytics in banking
Secondary Research
Our secondary research forms the foundation of market understanding and scope definition. We collect and analyze information from multiple reliable sources to map the overall ecosystem of the Predictive Analytics in Banking Market. Key inputs include:
- Annual reports, investor presentations, SEC filings
- Company press releases and product launch announcements
- Public executive interviews and earnings calls
- Strategy briefings and M&A updates
- Country-level industry associations and trade bodies
- Government dossiers, policy frameworks, and official releases
- Whitepapers, working papers, and public R&D initiatives
- Relevant Associations for the Predictive Analytics in Banking Market
- Broker reports and financial analyst coverage
- Paid databases (Hoovers, Factiva, Refinitiv, Reuters, Statista, etc.)
- Import/export trade data and tariff databases
- Sector-specific journals, magazines, and news portals
- Global macroeconomic indicators and their cascading effect on the industry
- Demand–supply outlook and value chain analysis
- Consumer behaviour, adoption rates, and commercialization trends
Primary Research
To validate and enrich our secondary findings, we conduct extensive primary research with industry stakeholders across the value chain. This ensures we capture both qualitative insights and quantitative validation. Our primary research includes:
- Key Opinion Leader (KOL) Engagements
- Structured interviews with executives, product managers, and domain experts
- Paid and barter-based interviews across manufacturers, distributors, and end-users
- Discussions with stakeholders to validate demand-supply gaps
- Group discussions on emerging technologies, regulatory shifts, and adoption barriers
- Cross-verification of market sizing and forecasts with industry insiders
- Capturing business perspectives on growth opportunities and restraints
Data Triangulation & Forecasting
The final step of our research involves data triangulation ensuring accuracy through cross-verification of:
- Demand-side analysis (consumption patterns, adoption trends, customer spending)
- Supply-side analysis (production, capacity, distribution, and market availability)
- Macroeconomic & microeconomic impact factors
- Time-series analysis
- Regression and correlation studies
- Baseline modeling
- Expert validation at each stage
Outcome
The outcome is a comprehensive and validated market model that captures:
- Market sizing (historical, current, forecast)
- Growth drivers and restraints
- Opportunity mapping and investment hotspots
- Competitive positioning and strategic insights
Available for purchase with detailed segment data, forecasts, and regional insights.
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