AI in Fintech Market Size, Share & Forecast by 2033

Research Methodology – AI in Fintech 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 AI in Fintech Market comprises the following key stages:

Market Indicator & Macro-Factor Analysis

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

Factors considered while calculating market size and share

Various technologies used in AI for Fintech, like Machine Learning, Deep Learning, and Natural Language Processing.
Different applications of AI in Fintech such as fraud detection, chatbots, risk assessment, credit scoring etc.
The revenue generation of the companies who are utilising AI in Fintech.
Investments made in AI for Fintech by various companies and institutions.
Geographic coverage: the presence of AI in Fintech in different regions across the world.
Market penetration: the extent to which the AI in Fintech has reached amongst the potential customer base.
Forecasts of market growth for AI in Fintech.

Key Market Indicators

The total number of companies involved in AI for Fintech.
The total amount of investments made in AI for Fintech.
The number of transactions processed using AI.
The number of patents filed in the field of AI for Fintech.
The number of customers using AI-based fintech services.
AI adoption rate among fintech companies.

Growth Trends

Growth in AI adoption across all banking institutions and financial service providers.
Increased number of partnerships between fintech companies and traditional financial institutions.
The rising trend of mobile banking and online transactions that use AI technologies.
Introduction of new AI-based technologies in Fintech, such as Robo-advisors and AI-assisted financial planning.
Increasing demand for AI-based fraud detection and prevention systems in Fintech.
Increasing use of AI in investment management, financial planning, and wealth management.

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 AI in Fintech 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 AI in Fintech 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: