Global Machine Learning in Finance
Market Report
2024
Machine Learning in Finance market was valued at USD 7.52 billion in 2022 and is projected to reach USD 38.13 billion by 2030, registering a CAGR of 22.50% for the forecast period 2023-2030.
The base year for the calculation is 2023 and 2019 to 2023 will be historical period. The year 2024 will be estimated one while the forecasted data will be from year 2025 to 2031. When we deliver the report that time we updated report data till the purchase date.
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The global Machine Learning in Finance market was valued at USD 7.52 billion in 2022 and is projected to reach USD 38.13 billion by 2030, registering a CAGR of 22.50% for the forecast period 2023-2030.
Base Year | 2023 |
Historical Data Time Period | 2019-2023 |
Forecast Period | 2024-2031 |
Global Machine Learning in Finance Market Sales Revenue 2022 | $ 7.52 Billion |
Global Machine Learning in Finance Market Sales Revenue 2030 | $ 38.13 Billion |
Global Machine Learning in Finance Market Compound Annual Growth Rate (CAGR) for 2024 to 2031 | 22.5% |
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Market Split by Company Type |
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Market Split by Investment Type |
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List of Competitors |
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Regional Analysis |
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Country Analysis |
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Key Qualitative Information Covered |
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Machine Learning in Finance Market is Segmented as below. Particular segment of your interest can be provided without any additional cost. Download the Sample Pages!
Machine learning in finance is regarded as an essential part of many financial services and applications, such as managing assets, assessing risk, determining credit scores, and even authorizing loans. Machine learning, a branch of data science, enables computers to learn from experience and get better over time without having to be programmed. This has gained popularity in recent years due to the growth of Artificial intelligence and computer aided software. The automated work trend has increased the demand for the Machine Learning in Finance. Machine learning algorithms are employed in the financial sector to identify fraud, automate trading, and offer investors financial advising services. For instance, according to the Bank of England in 2022, Machine learning (ML) is now being used by more UK financial services companies. In total, 72% of the companies that responded to the study stated that they were utilizing or creating ML applications.
Machine learning is now being used in operations by a large number of top fintech and financial services organizations, which has improved workflow, decreased risk, and improved portfolio optimization. For instance, Machine learning is used by 70% of all financial services companies to detect fraud, improve credit scores, and forecast cash flow events.
The development of the machine learning in Finance industry is positively impacted by advancements in data collection technology among banks and financial organizations. Additionally, increasing financial businesses' investments in machine learning and customer demand for individualized financial services are two significant drivers of the machine learning in finance market's global expansion.
The rising need for data-driven insights and predictive analytics can be attributed for the machine learning (ML) industry's rapid expansion and adoption. The necessity of using the vast databases and find insightful patterns has become important as financial institutions try to navigate the complexity of a constantly shifting global economy. This increase in demand is being driven by the understanding that standard analytical techniques frequently fail to capture the details and complex relationships contained in financial data. The ability of ML algorithms to analyse enormous volumes of data at high speeds gives them the power to find hidden trends, correlations, and inconsistencies that are inaccessible to manual testing. In the financial markets, where a slight edge in anticipating market movements, asset price fluctuations, and risk exposures can result in significant gains or reduced losses, this skill is particularly important. Additionally, the use of ML in finance goes beyond trading and investing plans. Various fields, including risk management, fraud detection, customer service, and regulatory compliance, are affected. Financial organizations can more effectively analyze and manage risk by recognizing possible risks and modeling scenarios that allow for better decision-making by utilizing advanced algorithms. Systems that use machine learning to detect fraud are more accurate than those that use rule-based methods because they can identify unexpected patterns and behaviors that could be signs of fraud in real time. For instance, Customers who use its machine learning (ML)-based CPP Fraud Analytics software for credit card fraud detection and prevention experience increases in detection rates between 50% and 90% and decreases in investigation times for individual fraud cases of up to 70%.
The accessibility and quality of the data used to develop and employ machine learning (ML) models in the field of finance are directly related to these factors. The absence of high-quality and unbiased financial data is a significant barrier that frequently prevents the effectiveness of ML applications in finance. Lack of thorough and reliable information can compromise the effectiveness and dependability of ML models in a sector characterized by complexity, quick market changes, and a wide range of affecting factors. Financial data includes market prices, economic indicators, trade volumes, sentiment research, and much more. It is also extremely diverse. For ML algorithms to produce useful insights and precise forecasts, it is essential that this data be precise, current, and indicative of the larger financial scene. If the historical data is biased and provides half information the machine learning software might give biased result depending on the data which would also results in the wrong and ineffective trends.
The rapid and prevalent adoption of artificial intelligence (AI) is currently driving a revolutionary trend in the financial market. There is growing use of artificial intelligence (AI) to improve customer service and automate a variety of financial processes. For instance, AI has the ability to increase economic growth by 26% and financial services revenue by 34%. This change is radically changing how financial organizations engage with their customers, streamline their processes, and provide services. These smart systems are made to respond to consumer queries, offer immediate support, and make specific suggestions. These AI-driven interfaces can comprehend and reply to consumer inquiries in a human-like manner by utilizing natural language processing and machine learning capabilities, ensuring effective and immediate assistance. In addition to increasing client satisfaction, this allows employees to concentrate on more difficult responsibilities. Additionally, AI is transforming the automation of a variety of financial functions, from common administrative procedures to sophisticated data analysis. Automating repetitive back-office tasks with greater accuracy and speed minimizes errors and lowers operational expenses. Examples include data input, document processing, and reconciliation. For instance, according to Accenture, Finance professionals might be relieved of repetitive tasks that take up 60–75% of staff time by automating 80% of financial operations. Massive amounts of financial data can be processed quickly by AI algorithms, which can then identify patterns, trends, and anomalies that can help with risk assessment and decision-making.
The pandemic had a significant impact on the Machine Learning in Finance Market. The need for more flexible and data-driven approaches was emphasized by limitations in conventional financial systems. This increased the need for machine learning (ML) tools that could quickly assess and adjust to rapidly shifting market conditions. As market volatility and unpredictability increased, financial institutions resorted to ML algorithms for risk assessment and management. They were better able to navigate volatile market conditions due to these algorithms, which enabled them to make better judgments in real time. The pandemic also highlighted the significance of digital transformation, encouraging financial companies to adopt tech-driven solutions. As remote work and digital communication increased, new approaches to operations and customer service were required. With the rise of ML-powered chatbots, virtual assistants, and automated customer care systems, service was maintained while putting a lower burden on human resources.
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Many companies are involved in mergers and acquisition as well as product launches to boost their financial sector by integrating machine learning and artificial intelligence. For instance, in December 2022, NVIDIA and Deutsche Bank partnership to integrate AI into the financial services industry Accelerating the application of AI to enhance financial services is the objective of a multi-year innovation partnership. Companies working on a variety of applications, including speech AI, intelligent avatars, and fraud detection. Using NVIDIA AI Enterprise software, Deutsche Bank and NVIDIA will create apps that better risk management, productivity, and customer service. The partnership will increase the bank's internal AI center of excellence.
May 2023
In order to further develop advanced analytics solutions customized to the particular requirements of different industries, SAS, the market leader in analytics, will invest $1 billion over the next three years. With this investment, SAS will keep assisting businesses that use AI, machine learning, and advanced analytics to manage risk, prevent fraud, provide better customer services.
(Source-www.sas.com/en_za/news/press-releases/2023/may/one-billion-investment-ai-industry-solutions.html)
June 2023
As part of its data and artificial intelligence (AI) transformation process, Banco Bilbao Vizcaya Argentaria, S.A. (BBVA), a leader in global banking, announced that it will employ Amazon Web Services (AWS) to supply advanced analytics and data services in the cloud. BBVA will utilize AWS to leverage analytics and machine learning to revolutionize its internal operations, enhance risk management, drive growth, and offer innovative options for its clients as part of its transformation into a data- and AI-driven business. In order to establish a secure depository for BBVA's operations and customer data, the bank will use a wide range of AWS analytics and AI capabilities across all of its operations. Additionally, it will develop a new data platform that will be deployed globally.
(Source-press.aboutamazon.com/2023/6/bbva-selects-aws-to-accelerate-its-data-driven-transformation)
February 2020
Large financial firms may automate complicated reports quickly using the New NLG Platform, which also provides a significant return on investment. Financial institutions can speed up their digital transition with the use of Augmented Analyst. A resilient, market-leading, patented NLG engine, together with expanded NLU and machine learning capabilities, are used by Augmented Analyst to extract insight from structured data and convert it into narratives for reports that are easily understood.
Top Companies Market Share in Machine Learning in Finance Industry: (In no particular order of Rank)
If any Company(ies) of your interest has/have not been disclosed in the above list then please let us know the same so that we will check the data availability in our database and provide you the confirmation or include it in the final deliverables.
North America accounted for the largest share in Machine learning in Finance Market due to several factors. The region's well-established financial ecosystem, which includes important global financial hubs like New York and Silicon Valley and has fostered the merging of finance and technology, is one important contributing aspect. Due to their close proximity, financial institutions and digital startups may easily collaborate and share expertise, which has sped up the adoption of ML solutions in the finance industry. Additionally, North America has a strong ecosystem for research and development, with superior academic institutions, research facilities, and tech firms committed to advancing ML technology. Modern algorithms, models, and tools created especially for financial applications are the result of this intellectual capital. The region's dominance in the sector has been further enhanced by the accessibility of the best professionals in data science, machine learning, and finance, which has encouraged innovation and accelerated the creation of advanced ML-driven financial products and services.
Having access to such a large volume of high-quality financial data has also been a significant advantage. Data is the essence of ML algorithms, and the complex financial markets in North America produce enormous amounts of diverse information. Due to the quantity of data, ML models can be trained and tested thoroughly, leading to predictions and judgments that are more reliable and precise.
The current report Scope analyzes Machine Learning in Finance Market on 5 major region Split (In case you wish to acquire a specific region edition (more granular data) or any country Edition data then please write us on info@cognitivemarketresearch.com
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Global Machine Learning in Finance Market Report 2024 Edition talks about crucial market insights with the help of segments and sub-segments analysis. In this section, we reveal an in-depth analysis of the key factors influencing Machine Learning in Finance Industry growth. Machine Learning in Finance market has been segmented with the help of its Type, Application Company Type, and others. Machine Learning in Finance market analysis helps to understand key industry segments, and their global, regional, and country-level insights. Furthermore, this analysis also provides information pertaining to segments that are going to be most lucrative in the near future and their expected growth rate and future market opportunities. The report also provides detailed insights into factors responsible for the positive or negative growth of each industry segment.
Due to its constantly proven effectiveness in addressing the most critical problems faced by financial institutions, supervised learning has accounted for largest share in the machine learning (ML) market within the banking sector. This dominance can be traced to the fundamental characteristics of supervised learning algorithms, which depend on labelled data to provide reliable predictions and judgments. Supervised learning techniques are particularly effective in the financial sector, where there is plenty of historical data and it is possible to assign reliable labels. These tasks include risk assessment, fraud detection, credit scoring, and portfolio optimization. Financial firms largely rely on precise risk assessment to decide which loans to provide. In order to estimate the possibility of default, supervised learning models can examine past data including borrower information, credit scores, and repayment history. This enables lenders to limit potential losses. Another essential area where supervised learning excels is fraud detection. Algorithms can be taught to recognize unusual patterns and alert potentially fraudulent activity in real-time by training on labelled datasets of legitimate and fraudulent transactions, enhancing security safeguards. Supervised learning is also beneficial for credit scoring, an essential financial function. In order determine credit scores, models can examine a person's financial history and other relevant information. This helps lenders choose appropriate loan terms. The benefits of supervised learning also extend to portfolio management, where algorithms may assess past asset performance to enhance investment selection and efficiently allocate resources.
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This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends and opportunities for each application of Machine Learning in Finance from 2019 to 2031. This will also help to analyze the demand for Machine Learning in Finance across different end-use industries. Our research team will also help acquire additional data such as Value Chain, Patent analysis, Company Evaluation Quadrant (Matrix), and much more confidential analysis and data insights.
Some of the key Application of Machine Learning in Finance are:
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I am working as a Senior Analyst with Cognitive Market Research in the areas of Industry Survey and Market Research/Analytics. I collaborate with CXOs, VPs, and other senior executives to understand their data needs and provide solutions and business strategy building. I fulfill data requirements with thorough research and analytics. ME and My research team provide intelligence on industry stats, market structure, company profiles/Competition Analysis, Forecast Mapping, and Recently started providing the COVID-19 Impact Analysis.
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The Global Machine Learning in Finance Market is witnessing significant growth in the near future.
In 2023, the Supervised Learning segment accounted for noticeable share of global Machine Learning in Finance Market and is projected to experience significant growth in the near future.
The Algorithmic Trading and Quantitative Analysis segment is expected to expand at the significant CAGR retaining position throughout the forecast period.
Some of the key companies The key players included in the report are IBM Watson , Amazon Web Services (AWS) and others are focusing on its strategy building model to strengthen its product portfolio and expand its business in the global market.
Please note, we have not disclose, all the sources consulted/referred during a market study due to confidentiality and paid service concern. However, rest assured that upon purchasing the service or paid report version, we will release the comprehensive list of sources along with the complete report and we also provide the data support where you can intract with the team of analysts who worked on the report.
Use cases of machine learning in banking & finance
https://www.epa.gov/regulatory-information-sector/construction-sector-naics-23
https://www.usace.army.mil/Missions/Civil-Works/Engineering-and-Construction/
https://www.abs.gov.au/statistics/industry/building-and-construction
https://business.gov.au/planning/industry-information/construction-industry
https://www.usitc.gov/research_and_analysis/tradeshifts/2021/footwear
Disclaimer:
Type | Supervised Learning, Unsupervised Learning, Reinforcement Learning |
Application | Algorithmic Trading and Quantitative Analysis, Credit Scoring and Risk Management, Fraud Detection and Prevention, Robo-Advisors and Wealth Management, Customer Interaction, Asset and Portfolio Management, Payment and Transaction Processing, Corporate Financial Analysis, Others |
Company Type | Banks and Financial Institutions, Hedge Funds and Asset Management Firms, Insurance Companies, FinTech Startups, Credit Rating Agencies, Academic and Research Institutions, Others |
Investment Type | Equity and Stock, Fixed Income and Bond, Foreign Exchange (Forex) Trading, Real Estate Investment, Mutual Funds and Exchange-Traded Funds (ETFs), Others |
List of Competitors | The key players included in the report are IBM Watson, Microsoft Azure, Amazon Web Services (AWS), Google Cloud, SAS, DataRobot, H2O.ai, NVIDIA, Yseop, Alpaca, Kensho |
This chapter will help you gain GLOBAL Market Analysis of Machine Learning in Finance. Further deep in this chapter, you will be able to review Global Machine Learning in Finance Market Split by various segments and Geographical Split.
Chapter 1 Global Market Analysis
Global Market has been segmented on the basis 5 major regions such as North America, Europe, Asia-Pacific, Middle East & Africa, and Latin America.
You can purchase only the Executive Summary of Global Market (2019 vs 2024 vs 2031)
Global Market Dynamics, Trends, Drivers, Restraints, Opportunities, Only Pointers will be deliverable
Chapter 2 North America Market Analysis
Purchase Add-Ons to Customize the Report Edition and get further granular data level data about the North America market. Add-On Cost: Request before placing the order, Respective Pricing will be informed after sending the inquiry
Qualitative Analysis for the North America Market: North America Machine Learning in Finance Market Trends North America Machine Learning in Finance Technological Road Map North America Machine Learning in Finance Market Drivers North America Machine Learning in Finance Market Restraints North America Machine Learning in Finance Market Opportunity Market Attractiveness Analysis COVID – 19 Impact Analysis PESTEL Analysis Porter’s Five Forces Analysis Product Life Cycle Industrial Chain Analysis
You can purchase only the Executive Summary North America Market (2019 vs 2024 vs 2031)
Chapter 3 Europe Market Analysis
You can purchase only the Executive Summary Europe Market (2019 vs 2024 vs 2031)
Chapter 4 Asia-Pacific Market Analysis
You can purchase only the Executive Summary Asia Pacific Market (2019 vs 2024 vs 2031)
Chapter 5 South America Market Analysis
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Chapter 6 Middle East and Africa Market Analysis
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Only Available with Corporate User License
Chapter 7 Top 10 Countries Analysis (Only Available with Corporate User License)
Competitor's Market Share and Revenue (Subject to Data Availability for Private Players)
Chapter 8 Competitor Analysis (Subject to Data Availability (Private Players))
(Subject to Data Availability (Private Players))
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Chapter 9 Qualitative Analysis (Subject to Data Availability)
Segmentation Type Analysis 2019 -2031, will provide market size split by Type. This Information is provided at Global Level, Regional Level and Top Countries Level The report with the segmentation perspective mentioned under this chapters will be delivered to you On Demand. So please let us know if you would like to receive this additional data as well. No additional cost will be applicable for the same.
Chapter 10 Market Split by Type Analysis 2019 -2031
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Chapter 11 Market Split by Application Analysis 2019 -2031
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Chapter 12 Market Split by Company Type Analysis 2019 -2031
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Chapter 13 Market Split by Investment Type Analysis 2019 -2031
This chapter helps you understand the Key Takeaways and Analyst Point of View of the global Machine Learning in Finance market
Chapter 14 Research Findings
Here the analyst will summarize the content of entire report and will share his view point on the current industry scenario and how the market is expected to perform in the near future. The points shared by the analyst are based on his/her detailed in-depth understanding of the market during the course of this report study. You will be provided exclusive rights to interact with the concerned analyst for unlimited time pre purchase as well as post purchase of the report.
Why Supervised Learning have a significant impact on Machine Learning in Finance market? |
What are the key factors affecting the Supervised Learning and Unsupervised Learning of Machine Learning in Finance Market? |
What is the CAGR/Growth Rate of Algorithmic Trading and Quantitative Analysis during the forecast period? |
By type, which segment accounted for largest share of the global Machine Learning in Finance Market? |
Which region is expected to dominate the global Machine Learning in Finance Market within the forecast period? |
Segmentation Level Customization |
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Region level Data Customization |
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Country level Data Customization |
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Company Level |
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Additional Data Analysis |
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Additional Qualitative Data |
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Additional Quantitative Data |
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