Artificial Intelligence in Banking Market Size, Growth & Trends Report by 2033

Research Methodology – Artificial Intelligence 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 Artificial Intelligence in Banking Market comprises the following key stages:

Market Indicator & Macro-Factor Analysis

Our baseline thesis for the Artificial Intelligence in Banking Market is developed by integrating key market indicators and macroeconomic variables. These include:

Factors considered while calculating market size and share

The total number of banks investing or planning to invest in AI technology.
Revenue generated by the AI implementation in the banking sector.
Startups or companies providing AI-based solutions focusing on the banking sector.
Projected rate of adoption of AI technologies in banks.
Global spending on AI by the banking industry.
The extent of AI applications in the banking sector.

Key Market Indicators

Year-over-year growth in AI investment by banks.
Geographical distribution of AI adoption in banking.
Number of AI-related patents filed by banks.
Usage of different AI applications such as chatbots, automated customer support, fraud detection, etc. in banking.
The ratio of banks that have implemented AI to the total number of banks.
Market share of leading companies providing AI solutions to the banking sector.

Growth Trends

Increase in the use of AI for customer segmentation.
Growth in AI usage for personalized marketing and recommendation systems.
Trends in automating banking operations using AI.
Increase in the use of AI for risk management and fraud detection.
Adoption of AI for better decision-making processes in banking.
Rise in AI investments and M&A activities in the banking sector.

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 Artificial Intelligence in Banking Market. Key inputs include:

Company-Level Information

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

Industry and Government Sources

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 Artificial Intelligence in Banking Market

Market Intelligence Sources

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

Macro & Consumer Insights

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:

Expert Insights & KOL Engagements

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

Focused Discussions & Panels

Discussions with stakeholders to validate demand-supply gaps
Group discussions on emerging technologies, regulatory shifts, and adoption barriers

Data Validation & Business POV

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

Forecasting is carried out using proprietary models that combine: