[267 Pages Report] The global artificial intelligence in fintech market size was valued at US$ 10.1 Billion in 2021, expected to reach US$ 11.76 Billion in 2022, reflecting a Y-o-Y surge of 16.4%. From 2022 to 2032, demand is expected to grow at a compound annual growth rate (CAGR) of 16.5% to reach US$ 54 Billion.
Fintech, or financial technology, applies modern technology to financial services to improve or automate banking and investing activities. Artificial intelligence (AI) is widely used in financial organizations to detect and prevent fraud through digital banking channels.
Mobile banking, digital loans, insurance, credit scores, purchasing and selling operations, and asset management are all included. AI technology can determine a customer’s typical behavior by monitoring how they interact and considering their transactions.
“The global AI in Fintech market is expected to show a remarkable growth in the forecast period owing to the increasing emphasis on customer’s account safety against cyber and banking frauds.”
| Attributes | Details |
|---|---|
| Anticipated Base Year Value (2021) | US$ 10.1 Billion |
| Expected Market Value (2022) | US$ 11.76 Billion |
| Projected Forecast Value (2032) | US$ 54 Billion |
| Global Growth Rate (2022 to 2032) | 16.5% CAGR |
| Growth Rate of the North American Market (2022 to 2032) | 16.5% CAGR |
| Expected Market Value of APAC (2032) | US$ 16 Billion |
| Europe Market Expansion Rate (2022 to 2032) | 15.5% CAGR |
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The increasing digitization of the banking, financial services, and insurance (BFSI) industry across the globe is one of the crucial aspects propelling the growth of the market. AI in fintech is extensively used for operating virtual assistance, debt collection, sentiment and predictive analysis, reporting, and customer behavior analysis. It boosts efficiency, curtails the chances of human error, and can process huge volumes of data in a short interval.
Along with this, AI also helps in the automated and real-time examination of cash, credit, and investment accounts for evaluating the financial health of the individual and creating customized insights for forthcoming development.
Moreover, countless technological advancements, such as the incorporation of fintech solutions with machine learning (ML), neural networks, big data, and evolutionary algorithms, are representing other growth-inducing aspects. These technologies offer enhanced supervising of financial transactions, risk management, speech recognition, and secured network access to the banking institutions.
Artificial Intelligence (AI) and Machine Learning (ML) have furthered the financial sector. The adoption of AI has empowered the banks to process a large number of data sets and reach conclusions owing to their capability to analyze real-time patterns, helping with quick decision-making. They are cultivating effectiveness and at the same time working efficiently.
AI amplifies employee productivity by 59% in the financial sector. It has slashed loan defaults and has made businesses safer, all for a better customer experience. By 2032, financial corporations will be able to cut down costs by 22% saving nearly 1 trillion. Several fintech firms are incessantly researching the areas of AI that will be helpful for banks and their fraud detection processes, customer service, credit service, and loan decisions.
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The arrival of artificial intelligence (AI) investment in the financial services space has raised a new list of questionnaires about data security and transparency. These, among other shortcomings of AI in financial services, are particularly crucial to address as data management practices advance with the advent of new AI solutions.
One of the foremost tasks of AI in financial services is the amount of data gathered that contains sensitive and confidential data that requires additional security measures to be instigated. Fintech companies are working to propose an array of security options, and have robust data protection with certifications and regulations, and security guidelines to ensure your customer data is aptly handled.
Despite several advantages, fintech solutions leave the door open to several threats, which may shackle consumer protection and financial stability. Such as underestimation of creditworthiness, market risk in compliance, fraud detection, and cyber-attacks. These factors are likely to challenge the adoption of AI in the Fintech market.
The business analytics and reporting segment led the market in 2021 and accounted for over 32% of global revenue. Future Market Insight’s recently published analytical report depicts that the aforementioned segment is estimated to expand at a CAGR of 16% in the upcoming decade.
Business analytics and reporting aid in regulatory and compliance management and customer behavior analysis. The segment's rise can be attributed to various factors, including increased operational efficiency, more informed decision-making, and increased revenue.
Many companies use business analytics, AI, and big data to make better business decisions. As a result, the growth of this section of AI in the fintech business is spreading due to the significant developments in the fintech market.
Customer behavioural analytics will witness significant growth in the coming years. It examines all of the hazards related to customers. In addition to assisting with regulatory and compliance management, business analytics and reporting can assist in analyzing client behavior, which will drive demand for AI in the fintech market.
It can forecast a user's behavior by integrating multiple AI and machine learning algorithms through an interface, allowing users to obtain extensive insights into their data.
For instance, in March 2022, PwC, a corporate finance accounting service provider, stated that 83% of Indian financial services firms are driving growth in their business due to the implementation of customer behavioural analytics. Furthermore, with the help of AI, it quickly studies customer behavior and provides insights into their data.
The on-premise segment held the largest revenue share of 57.2% in 2021. On-premise deployment assists enterprises in installing software or services on a financial institution's premises or systems. The cloud segment will register a 17% CAGR from 2022 to 2032.
Growth is attributed to AI-based algorithms learning from historical data in a cloud environment, detecting current standards, and making recommendations. The cloud and AI can improve productivity, efficiency, and digital security in data handling and authenticity, and this automated technique eliminates human errors during data processing.
For instance, in January 2022, Temenos, a provider of enterprise software for banks and financial institutions, announced the industry's first AI-powered buy-now-pay-later banking solution on the temenos banking cloud.
The banking service will provide fintech and banks with new revenue potential through alternative credit products, help them attain new markets, and solidify their relationships with consumers and merchants. The cloud platform plays a vital role in aligning customers' right opportunities to match their financial profiles. Risk tolerance also helps the fintech firms make better predictions faster.
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North America dominated the market in 2021 and accounted for more than 40% share of the global revenue. This high share can be attributed to a strong emphasis on R&D-derived inventions in the developed economies of the U.S. and Canada. These regions have the most competitive and fastest developing AI technology in fintech. It is also fuelled by many start-ups and emerging enterprises providing AI services to the finance sector.
For instance, financial services giant Stripe has launched a new identity verification tool to help online businesses prevent fraud when accepting payments. Stripe Identity uses artificial intelligence (AI) and machine learning (ML) technology to securely verify the identities of users from over 30 countries.
A recently published report by Future Market Insights reveals that the North American market for AI in Fintech is projected to reach US$ 35 Billion by 2032 end, documenting a CAGR of 16.5% throughout the forecast period.
Asia Pacific is anticipated to register the fastest CAGR from 2022 to 2032. This growth can be attributed to the area’s rapid adoption of digital payments and increased penetration of internet services.
APAC has emerged as a potential market due to increased technical improvement. The quick expansion of domestic firms with supportive government measures creates numerous opportunities for the advancement of AI in the fintech business.
Furthermore, prominent players invest in the region’s new markets as part of their business strategy, adding to regional market growth. For instance, in April 2022, Finbots.AI, a Singapore-based, AI-powered firm, announced its investment in series A funding for USD 3million.
The funding would be further used for technical improvements, customer support, and product enhancement. The company would also scale its business by expanding its footprint in the rest of Asia.
As per the recently published report by Future Market Insights, the APAC AI in the Fintech market is estimated to reach US$ 16.5 Billion by 2032 from US$ 2.3 billion in 2021, registering a Y-o-Y growth rate of 19.6% throughout the conjecture period.
| North America | 16.5% |
|---|---|
| Europe | 15.5% |
| Asia Pacific | 19.6% |
| Latin America | 15.5% |
| Middle East & Africa | 14.8% |
Prominent AI in Fintech providers is reliant on partnerships, collaborations, acquisitions, and new software launches to stay afloat in the global market. Constant innovations to ensure a seamless client-customer relationship are the main focus of prominent market players.
| Report Attributes | Details |
|---|---|
| Growth Rate | CAGR of 16.5% from 2022 to 2032 |
| Market Value for 2022 | US$ 11.76 Billion |
| Market Value for 2032 | US$ 54 Billion |
| Base Year for Estimation | 2021 |
| Historical Data | 2017 to 2021 |
| Forecast Period | 2022 to 2032 |
| Quantitative Units | USD Million for Value |
| Report Coverage | Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends and Pricing Analysis |
| Segments Covered |
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| Regions Covered |
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| Key Countries Profiled |
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| Key Companies Profiled |
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| Report Customization & Pricing | Available upon Request |
As of 2021, Future Market Insights estimated that AI in the Fintech market reached US$ 10.1 Billion
By 2022, Future Market Insights expected demand for AI in Fintech to reach US$ 11.76 Billion
From 2015-to 2021, the AI in Fintech market grew at a 16% value CAGR
From 2022-to 2032, AI in Fintech demand is likely to surge at a 16.4% CAGR
By 2032, the market for AI in Fintech is likely to be valued at US$ 56 Billion
By 2032, Future Market Insights expects the U.S market for AI in Fintech to reach US$ 35 Billion
APAC is expected to register a 19.6% CAGR in the AI in the fintech industry
Business analytics and reporting Segment AI in fintech will be maximum, expanding at a 16% CAGR
On-Premise AI in Fintech will remain most preferred, expanding at a CAGR of 17% until 2032
1. Executive Summary 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. Value Chain Analysis 3.5.1. Profit Margin Analysis 3.5.2. Service Providers 3.6. PESTLE and Porter’s Analysis 3.7. Regulatory Landscape 3.7.1. By Key Regions 3.7.2. By Key Countries 3.8. Regional Parent Market Outlook 4. Global AI in Fintech Market Analysis 2017-2021 and Forecast, 2022-2032 4.1. Historical Market Size Value (US$ Mn) Analysis, 2017-2021 4.2. Current and Future Market Size Value (US$ Mn) Projections, 2022-2032 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Components 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ Mn) Analysis By Components, 2017-2021 5.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Components, 2022-2032 5.3.1. Solutions 5.3.2. Services 5.3.2.1. Managed Services 5.3.2.2. Professional Services 5.4. Y-o-Y Growth Trend Analysis By Components, 2017-2021 5.5. Absolute $ Opportunity Analysis By Components, 2022-2032 6. Global AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Deployment 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ Mn) Analysis By Deployment, 2017-2021 6.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Deployment, 2022-2032 6.3.1. Cloud 6.3.2. On-Premise 6.4. Y-o-Y Growth Trend Analysis By Deployment, 2017-2021 6.5. Absolute $ Opportunity Analysis By Deployment, 2022-2032 7. Global AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Application 7.1. Introduction / Key Findings 7.2. Historical Market Size Value (US$ Mn) Analysis By Application, 2017-2021 7.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Application, 2022-2032 7.3.1. Virtual Assistant 7.3.2. Business Analytics and Reporting 7.3.3. Customer Behavioral Analytics 7.3.4. Fraud Detection 7.3.5. Quantitative and Assest Management 7.3.6. Other Applications 7.4. Y-o-Y Growth Trend Analysis By Application, 2017-2021 7.5. Absolute $ Opportunity Analysis By Application, 2022-2032 8. Global AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Region 8.1. Introduction 8.2. Historical Market Size Value (US$ Mn) Analysis By Region, 2017-2021 8.3. Current Market Size Value (US$ Mn) Analysis and Forecast By Region, 2022-2032 8.3.1. North America 8.3.2. Latin America 8.3.3. Europe 8.3.4. Asia Pacific 8.3.5. MEA 8.4. Market Attractiveness Analysis By Region 9. North America AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Country 9.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 9.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 9.2.1. By Country 9.2.1.1. U.S. 9.2.1.2. Canada 9.2.2. By Components 9.2.3. By Deployment 9.2.4. By Application 9.3. Market Attractiveness Analysis 9.3.1. By Country 9.3.2. By Components 9.3.3. By Deployment 9.3.4. By Application 9.4. Key Takeaways 10. Latin America AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Country 10.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 10.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 10.2.1. By Country 10.2.1.1. Brazil 10.2.1.2. Mexico 10.2.1.3. Rest of Latin America 10.2.2. By Components 10.2.3. By Deployment 10.2.4. By Application 10.3. Market Attractiveness Analysis 10.3.1. By Country 10.3.2. By Components 10.3.3. By Deployment 10.3.4. By Application 10.4. Key Takeaways 11. Europe AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Country 11.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 11.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 11.2.1. By Country 11.2.1.1. Germany 11.2.1.2. U.K. 11.2.1.3. France 11.2.1.4. Spain 11.2.1.5. Italy 11.2.1.6. Russia 11.2.1.7. Rest of Europe 11.2.2. By Components 11.2.3. By Deployment 11.2.4. By Application 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By Components 11.3.3. By Deployment 11.3.4. By Application 11.4. Key Takeaways 12. Asia Pacific AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Country 12.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 12.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 12.2.1. By Country 12.2.1.1. China 12.2.1.2. Japan 12.2.1.3. India 12.2.1.4. South Korea 12.2.1.5. Australia 12.2.1.6. Rest of APAC 12.2.2. By Components 12.2.3. By Deployment 12.2.4. By Application 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By Components 12.3.3. By Deployment 12.3.4. By Application 12.4. Key Takeaways 13. MEA AI in Fintech Market Analysis 2017-2021 and Forecast 2022-2032, By Country 13.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 13.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 13.2.1. By Country 13.2.1.1. South Africa 13.2.1.2. Saudi Arabia 13.2.1.3. UAE 13.2.1.4. Israel 13.2.1.5. Rest of MEA 13.2.2. By Components 13.2.3. By Deployment 13.2.4. By Application 13.3. Market Attractiveness Analysis 13.3.1. By Country 13.3.2. By Components 13.3.3. By Deployment 13.3.4. By Application 13.4. Key Takeaways 14. Key Countries AI in Fintech Market Analysis 14.1. U.S. 14.1.1. Pricing Analysis 14.1.2. Market Share Analysis, 2021 14.1.2.1. By Components 14.1.2.2. By Deployment 14.1.2.3. By Application 14.2. Canada 14.2.1. Pricing Analysis 14.2.2. Market Share Analysis, 2021 14.2.2.1. By Components 14.2.2.2. By Deployment 14.2.2.3. By Application 14.3. Brazil 14.3.1. Pricing Analysis 14.3.2. Market Share Analysis, 2021 14.3.2.1. By Components 14.3.2.2. By Deployment 14.3.2.3. By Application 14.4. Mexico 14.4.1. Pricing Analysis 14.4.2. Market Share Analysis, 2021 14.4.2.1. By Components 14.4.2.2. By Deployment 14.4.2.3. By Application 14.5. Germany 14.5.1. Pricing Analysis 14.5.2. Market Share Analysis, 2021 14.5.2.1. By Components 14.5.2.2. By Deployment 14.5.2.3. By Application 14.6. U.K. 14.6.1. Pricing Analysis 14.6.2. Market Share Analysis, 2021 14.6.2.1. By Components 14.6.2.2. By Deployment 14.6.2.3. By Application 14.7. France 14.7.1. Pricing Analysis 14.7.2. Market Share Analysis, 2021 14.7.2.1. By Components 14.7.2.2. By Deployment 14.7.2.3. By Application 14.8. Spain 14.8.1. Pricing Analysis 14.8.2. Market Share Analysis, 2021 14.8.2.1. By Components 14.8.2.2. By Deployment 14.8.2.3. By Application 14.9. Italy 14.9.1. Pricing Analysis 14.9.2. Market Share Analysis, 2021 14.9.2.1. By Components 14.9.2.2. By Deployment 14.9.2.3. By Application 14.10. Russia 14.10.1. Pricing Analysis 14.10.2. Market Share Analysis, 2021 14.10.2.1. By Components 14.10.2.2. By Deployment 14.10.2.3. By Application 14.11. China 14.11.1. Pricing Analysis 14.11.2. Market Share Analysis, 2021 14.11.2.1. By Components 14.11.2.2. By Deployment 14.11.2.3. By Application 14.12. Japan 14.12.1. Pricing Analysis 14.12.2. Market Share Analysis, 2021 14.12.2.1. By Components 14.12.2.2. By Deployment 14.12.2.3. By Application 14.13. India 14.13.1. Pricing Analysis 14.13.2. Market Share Analysis, 2021 14.13.2.1. By Components 14.13.2.2. By Deployment 14.13.2.3. By Application 14.14. South Korea 14.14.1. Pricing Analysis 14.14.2. Market Share Analysis, 2021 14.14.2.1. By Components 14.14.2.2. By Deployment 14.14.2.3. By Application 14.15. Australia 14.15.1. Pricing Analysis 14.15.2. Market Share Analysis, 2021 14.15.2.1. By Components 14.15.2.2. By Deployment 14.15.2.3. By Application 14.16. South Africa 14.16.1. Pricing Analysis 14.16.2. Market Share Analysis, 2021 14.16.2.1. By Components 14.16.2.2. By Deployment 14.16.2.3. By Application 14.17. Saudi Arabia 14.17.1. Pricing Analysis 14.17.2. Market Share Analysis, 2021 14.17.2.1. By Components 14.17.2.2. By Deployment 14.17.2.3. By Application 14.18. UAE 14.18.1. Pricing Analysis 14.18.2. Market Share Analysis, 2021 14.18.2.1. By Components 14.18.2.2. By Deployment 14.18.2.3. By Application 14.19. Israel 14.19.1. Pricing Analysis 14.19.2. Market Share Analysis, 2021 14.19.2.1. By Components 14.19.2.2. By Deployment 14.19.2.3. By Application 15. Market Structure Analysis 15.1. Competition Dashboard 15.2. Competition Benchmarking 15.3. Market Share Analysis of Top Players 15.3.1. By Regional 15.3.2. By Components 15.3.3. By Deployment 15.3.4. By Application 16. Competition Analysis 16.1. Competition Deep Dive 16.1.1. Microsoft 16.1.1.1. Overview 16.1.1.2. Product Portfolio 16.1.1.3. Profitability by Market Segments 16.1.1.4. Sales Footprint 16.1.1.5. Strategy Overview 16.1.1.5.1. Marketing Strategy 16.1.1.5.2. Product Strategy 16.1.1.5.3. Channel Strategy 16.1.2. Google Llc 16.1.2.1. Overview 16.1.2.2. Product Portfolio 16.1.2.3. Profitability by Market Segments 16.1.2.4. Sales Footprint 16.1.2.5. Strategy Overview 16.1.2.5.1. Marketing Strategy 16.1.2.5.2. Product Strategy 16.1.2.5.3. Channel Strategy 16.1.3. Salesforce, inc. 16.1.3.1. Overview 16.1.3.2. Product Portfolio 16.1.3.3. Profitability by Market Segments 16.1.3.4. Sales Footprint 16.1.3.5. Strategy Overview 16.1.3.5.1. Marketing Strategy 16.1.3.5.2. Product Strategy 16.1.3.5.3. Channel Strategy 16.1.4. International Business Machines Corporation 16.1.4.1. Overview 16.1.4.2. Product Portfolio 16.1.4.3. Profitability by Market Segments 16.1.4.4. Sales Footprint 16.1.4.5. Strategy Overview 16.1.4.5.1. Marketing Strategy 16.1.4.5.2. Product Strategy 16.1.4.5.3. Channel Strategy 16.1.5. Amelia Us Llc 16.1.5.1. Overview 16.1.5.2. Product Portfolio 16.1.5.3. Profitability by Market Segments 16.1.5.4. Sales Footprint 16.1.5.5. Strategy Overview 16.1.5.5.1. Marketing Strategy 16.1.5.5.2. Product Strategy 16.1.5.5.3. Channel Strategy 16.1.6. Nuance Communications, Inc. 16.1.6.1. Overview 16.1.6.2. Product Portfolio 16.1.6.3. Profitability by Market Segments 16.1.6.4. Sales Footprint 16.1.6.5. Strategy Overview 16.1.6.5.1. Marketing Strategy 16.1.6.5.2. Product Strategy 16.1.6.5.3. Channel Strategy 16.1.7. Complyadvantage.Com 16.1.7.1. Overview 16.1.7.2. Product Portfolio 16.1.7.3. Profitability by Market Segments 16.1.7.4. Sales Footprint 16.1.7.5. Strategy Overview 16.1.7.5.1. Marketing Strategy 16.1.7.5.2. Product Strategy 16.1.7.5.3. Channel Strategy 16.1.8. Narrative Science 16.1.8.1. Overview 16.1.8.2. Product Portfolio 16.1.8.3. Profitability by Market Segments 16.1.8.4. Sales Footprint 16.1.8.5. Strategy Overview 16.1.8.5.1. Marketing Strategy 16.1.8.5.2. Product Strategy 16.1.8.5.3. Channel Strategy 16.1.9. Affirm, Inc. 16.1.9.1. Overview 16.1.9.2. Product Portfolio 16.1.9.3. Profitability by Market Segments 16.1.9.4. Sales Footprint 16.1.9.5. Strategy Overview 16.1.9.5.1. Marketing Strategy 16.1.9.5.2. Product Strategy 16.1.9.5.3. Channel Strategy 16.1.10. Upstart Network, Inc. 16.1.10.1. Overview 16.1.10.2. Product Portfolio 16.1.10.3. Profitability by Market Segments 16.1.10.4. Sales Footprint 16.1.10.5. Strategy Overview 16.1.10.5.1. Marketing Strategy 16.1.10.5.2. Product Strategy 16.1.10.5.3. Channel Strategy 16.1.11. Intel 16.1.11.1. Overview 16.1.11.2. Product Portfolio 16.1.11.3. Profitability by Market Segments 16.1.11.4. Sales Footprint 16.1.11.5. Strategy Overview 16.1.11.5.1. Marketing Strategy 16.1.11.5.2. Product Strategy 16.1.11.5.3. Channel Strategy 16.1.12. Instructure, Inc. 16.1.12.1. Overview 16.1.12.2. Product Portfolio 16.1.12.3. Profitability by Market Segments 16.1.12.4. Sales Footprint 16.1.12.5. Strategy Overview 16.1.12.5.1. Marketing Strategy 16.1.12.5.2. Product Strategy 16.1.12.5.3. Channel Strategy 17. Assumptions & Acronyms Used 18. Research Methodology
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