BFSIにおけるロボティック・プロセス・オートメーション市場- 世界の産業規模、シェア、動向、機会、予測、タイプ別、展開別、組織別、用途別、地域別、競合別セグメント、2018年～2028年

BFSIにおけるロボティック・プロセス・オートメーションの世界市場規模は、2022年に8億7,200万米ドルとなり、2028年までのCAGRは38%で、予測期間中に力強い成長が予測されています。

銀行・金融サービス・保険（BFSI）分野における世界のロボティック・プロセス・オートメーション（RPA）市場は、大きな変革期を迎えています。RPA技術は、ルールに基づいた反復作業を自動化することで、効率化、コスト削減、精度向上を実現し、金融機関の業務に革命をもたらしています。データの正確性とコンプライアンスが最重要視される業界において、RPAはバックオフィス業務、顧客サービス、規制レポートの合理化において極めて重要な役割を果たしています。データ入力、口座照合、不正検知などのプロセスを自動化することで、BFSI組織は人的資源をより戦略的で付加価値の高い活動に振り向けることができます。

さらにRPAは、急速に進化する金融業界において重要な要素であるオペレーションの俊敏性を促進します。市場の変化、顧客の要求、規制の更新に迅速に対応し、競争力を高めることができます。さらに、RPAの24時間稼働能力は、中断のないサービスを保証し、リアルタイムの取引処理と顧客サポートを促進します。

さらに、BFSIセクターでは、コンプライアンスとリスク管理のためにRPAを採用する動きが加速しています。RPAボットは規制プロトコルに細心の注意を払って従うことができるため、コンプライアンス違反とそれに伴う罰則のリスクを低減することができます。全体として、BFSIにおける世界のRPA市場は、金融機関が業務の合理化、顧客体験の向上、ますますダイナミックでデータ主導型の業界における規制遵守の確保を実現する自動化の変革の可能性を認識しているため、堅調な成長を遂げています。

市場促進要因

目次

第1章 概要

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 BFSIにおけるロボティック・プロセス・オートメーションの世界市場に対するCOVID-19の影響

第5章 顧客の声

第6章 BFSIにおけるロボティック・プロセス・オートメーションの世界市場概要

第7章 BFSIにおけるロボティック・プロセス・オートメーションの世界市場BFSIにおけるロボットプロセス自動化の世界市場展望

• 市場規模と予測
  • 金額別
• 市場シェアと予測
  • タイプ別（ソフトウェア、サービス）
  • 展開別（クラウド、オンプレミス）
  • 組織別（中小企業、大企業）
  • 用途別（銀行、金融サービス、保険）
  • 地域別（北米、欧州、南米、中東・アフリカ、アジア太平洋地域）
• 企業別（2022年）
• 市場マップ

第8章 北米のBFSIにおけるロボティック・プロセス・オートメーション市場展望

• 市場規模・予測
  • 金額別
• 市場シェアと予測
  • タイプ別
  • 展開別
  • 組織別
  • アプリケーション別
  • 国別
• 北米国別分析
  • 米国
  • カナダ
  • メキシコ

第9章 欧州：BFSIにおけるロボティック・プロセス・オートメーション市場の展望

• 市場規模と予測
  • 金額別
• 市場シェアと予測
  • タイプ別
  • 展開別
  • 組織別
  • アプリケーション別
  • 国別
• 欧州国別分析
  • ドイツ
  • フランス
  • 英国
  • イタリア
  • スペイン
  • ベルギー

第10章 南米BFSIにおけるロボティック・プロセス・オートメーション市場の展望

• 市場規模・予測
  • 金額別
• 市場シェアと予測
  • タイプ別
  • 展開別
  • 組織別
  • アプリケーション別
  • 国別
• 南米国別分析
  • ブラジル
  • コロンビア
  • アルゼンチン
  • チリ
  • ペルー

第11章 中東・アフリカのBFSIにおけるロボティック・プロセス・オートメーション市場展望

• 市場規模・予測
  • 金額別
• 市場シェアと予測
  • タイプ別
  • 展開別
  • 組織別
  • アプリケーション別
  • 国別
• 中東&アフリカ：国別分析
  • サウジアラビア
  • アラブ首長国連邦
  • 南アフリカ
  • トルコ
  • イスラエル

第12章 アジア太平洋地域のBFSIにおけるロボティック・プロセス・オートメーション市場の展望

• 市場規模と予測
  • タイプ別
  • デプロイメント別
  • 組織別
  • アプリケーション別
  • 国別
• アジア太平洋地域国別分析
  • 中国
  • インド
  • 日本
  • 韓国
  • オーストラリア
  • インドネシア
  • ベトナム

第13章 市場力学

• 促進要因
• 課題

第14章 市場の動向と開拓

第15章 企業プロファイル企業プロファイル

• Antworks
• Atos SE
• Automation Anywhere, Inc.
• Blue Prism Limited
• EdgeVerve Systems Ltd.
• FPT Software
• IBM
• Kofax Inc.
• Microsoft（Softomotive）
• NICE
• Nintex UK Ltd.（Kryon Systems）
• Pegasystems Inc.
• Protiviti Inc.
• UiPath

第16章 戦略的提言

第17章 調査会社について・免責事項

Global Robotic Process Automation in BFSI Market has valued at USD 872 Million in 2022 and is anticipated to project robust growth in the forecast period with a CAGR of 38% through 2028. The Global Robotic Process Automation (RPA) market within the Banking, Financial Services, and Insurance (BFSI) sector is undergoing a profound transformation. RPA technology is revolutionizing how financial institutions operate by automating repetitive, rule-based tasks, enabling efficiency gains, cost reduction, and enhanced accuracy. In an industry where data accuracy and compliance are paramount, RPA plays a pivotal role in streamlining back-office operations, customer service, and regulatory reporting. By automating processes such as data entry, account reconciliation, and fraud detection, BFSI organizations can redirect human resources towards more strategic, value-added activities.

Moreover, RPA fosters operational agility, a critical factor in the rapidly evolving financial landscape. It empowers institutions to adapt swiftly to market changes, customer demands, and regulatory updates, thereby gaining a competitive edge. Additionally, RPA's ability to work around the clock ensures uninterrupted service, facilitating real-time transaction processing and customer support.

Furthermore, the BFSI sector is increasingly embracing RPA for compliance and risk management. RPA bots can meticulously follow regulatory protocols, reducing the risk of non-compliance and associated penalties. Overall, the Global RPA in BFSI market is witnessing robust growth as financial institutions recognize the transformative potential of automation to streamline operations, enhance customer experiences, and ensure regulatory adherence in an increasingly dynamic and data-driven industry.

Key Market Drivers

Enhanced Operational Efficiency

AI-driven Robotic Process Automation (RPA) is transforming the Banking, Financial Services, and Insurance (BFSI) sector by automating repetitive and time-consuming tasks, thereby enabling organizations to focus on more complex and critical areas. By leveraging AI algorithms, RPA systems can analyze large volumes of transaction data, identify patterns, and automate routine processes. This technological advancement brings about significant improvements in operational efficiency and accelerates workflows within the BFSI domain. One of the key benefits of AI-driven RPA in the BFSI sector is its ability to automate tasks such as data entry, account reconciliation, and fraud detection. By automating these processes, financial institutions can free up human resources to concentrate on more strategic tasks, while also reducing the potential for human error. This not only enhances productivity but also ensures greater accuracy in critical financial operations. Furthermore, AI-driven RPA systems continuously learn from their interactions, allowing them to adapt and improve over time. This means that as the technology interacts with various data sources and performs tasks, it becomes more proficient and effective. By constantly learning and updating its processes, AI-driven RPA ensures that organizations can stay up to date with the latest industry trends and regulations, thereby maintaining compliance and efficiency.

The ability of AI-driven RPA to analyze large volumes of transaction data is particularly valuable in the BFSI sector. By leveraging AI algorithms, RPA systems can identify patterns and anomalies in financial transactions, enabling organizations to detect potential fraud or suspicious activities in real-time. This proactive approach to fraud detection helps financial institutions mitigate risks and protect their customers' assets. In addition to fraud detection, AI-driven RPA can also assist in regulatory compliance. By automating processes related to compliance monitoring and reporting, financial institutions can ensure that they adhere to the ever-changing regulatory landscape. This not only saves time and effort but also reduces the risk of non-compliance and associated penalties.

Overall, AI-driven RPA is revolutionizing the BFSI sector by streamlining operations, improving efficiency, and reducing the potential for errors. By automating repetitive tasks and leveraging AI algorithms, financial institutions can allocate their resources more strategically, focus on critical areas, and provide better services to their customers. As technology continues to advance, AI-driven RPA is expected to play an increasingly vital role in shaping the future of the BFSI sector.

Cost Reduction and Compliance

The banking, financial services, and insurance (BFSI) industry is confronted with strict regulations and an increasing demand for cost control. In this context, AI-enabled Robotic Process Automation (RPA) plays a crucial role in achieving these objectives. By implementing AI-driven RPA, financial institutions can automate regulatory compliance checks and routine operational tasks, leading to significant reductions in operational costs while ensuring adherence to industry regulations. The utilization of AI algorithms enables efficient identification of discrepancies, continuous monitoring of transactions, and generation of compliance reports, thereby saving valuable time and resources. This dual advantage of cost reduction and compliance is fueling the widespread adoption of AI-driven RPA across the BFSI sector.

The BFSI industry operates in a highly regulated environment, with numerous compliance requirements imposed by regulatory bodies. These regulations are designed to safeguard the interests of customers, maintain the stability of financial systems, and prevent fraudulent activities. However, ensuring compliance with these regulations can be a complex and resource-intensive process. AI-enabled RPA offers a solution by automating the compliance checks, thereby reducing the burden on human resources and minimizing the risk of errors. By leveraging AI algorithms, financial institutions can efficiently analyze vast amounts of data, identify potential compliance issues, and take appropriate actions in a timely manner. This not only enhances the accuracy and effectiveness of compliance processes but also frees up human employees to focus on more strategic and value-added tasks.

In addition to compliance, cost control is a critical concern for the BFSI industry. Financial institutions are constantly seeking ways to optimize their operational expenses without compromising on the quality of services provided. AI-driven RPA provides a viable solution by automating routine operational tasks, such as data entry, reconciliation, and report generation. By automating these tasks, financial institutions can achieve significant cost savings by reducing the need for manual labor and minimizing the occurrence of errors. Moreover, AI algorithms can continuously learn and improve over time, leading to enhanced operational efficiency and further cost reductions.

The combination of cost reduction and compliance benefits offered by AI-driven RPA has led to its widespread adoption across the BFSI sector. Financial institutions are increasingly recognizing the potential of AI technologies to streamline their operations, improve efficiency, and ensure regulatory compliance. The implementation of AI-driven RPA not only enables financial institutions to achieve cost savings and regulatory compliance but also enhances their overall competitiveness in the market. As the BFSI industry continues to evolve, AI-enabled RPA is expected to play an increasingly pivotal role in driving operational excellence and meeting the evolving needs of customers and regulators alike.

Enhanced Customer Experience

AI-enabled Robotic Process Automation (RPA) has revolutionized the customer experience in the Banking, Financial Services, and Insurance (BFSI) sector by significantly expediting critical processes such as loan approvals, account management, and customer inquiries. By leveraging automated chatbots and virtual assistants, financial institutions can now provide customers with real-time support, streamline query resolution, and offer personalized services, leading to improved customer satisfaction, strengthened loyalty, and enhanced trust in the financial services industry. One of the key benefits of AI-enabled RPA in the BFSI sector is the ability to expedite loan approvals. Traditionally, loan approval processes have been time-consuming and cumbersome, often requiring customers to submit numerous documents and wait for extended periods for a decision. However, with the implementation of AI-powered automation, financial institutions can now analyze vast amounts of customer data, assess creditworthiness, and make informed decisions in a fraction of the time. This not only reduces the waiting time for customers but also improves the overall efficiency of the loan approval process.

Furthermore, AI-enabled RPA has transformed account management in the BFSI sector. By automating routine tasks such as balance inquiries, transaction history, and fund transfers, financial institutions can provide customers with instant access to their account information. This real-time availability of data empowers customers to manage their finances more effectively and make informed decisions. Additionally, AI-powered virtual assistants can proactively identify potential issues, such as unusual account activity or low balances, and notify customers, thereby enhancing security and preventing fraudulent activities. Moreover, AI-powered chatbots and virtual assistants have revolutionized customer inquiries in the BFSI sector. These intelligent systems can understand and respond to customer queries in real-time, providing accurate and personalized information. By leveraging natural language processing and machine learning algorithms, chatbots can handle a wide range of customer inquiries, from basic account information to complex financial advice. This not only reduces the burden on customer service representatives but also ensures consistent and efficient customer support, regardless of the time or day. Overall, the integration of AI-enabled RPA in the BFSI sector has significantly enhanced the customer experience. By expediting processes such as loan approvals, account management, and customer inquiries, financial institutions can provide customers with real-time support, streamline query resolution, and offer personalized services. These advancements not only improve customer satisfaction but also strengthen customer loyalty and trust in the financial services industry. As AI technology continues to evolve, we can expect further innovations that will revolutionize the way customers interact with financial institutions, ultimately leading to a more seamless and personalized banking experience.

Improved Risk Management

Risk assessment and management play a crucial role in the Banking, Financial Services, and Insurance (BFSI) sector, and the integration of AI-driven Robotic Process Automation (RPA) is revolutionizing these processes. RPA solutions leverage the power of artificial intelligence to analyze vast amounts of data and identify patterns that may indicate potential risks. This proactive approach enables BFSI institutions to mitigate risks before they escalate. By employing AI algorithms, RPA systems can detect anomalies and evaluate transactional data to identify fraudulent activities, thereby triggering alerts for further investigation. This proactive risk management approach empowers BFSI institutions to safeguard their assets, prevent fraud, and uphold the integrity of their financial operations. The BFSI sector deals with a wide range of risks, including credit risk, market risk, operational risk, and compliance risk. Traditionally, risk assessment and management in this sector have relied on manual processes, which are time-consuming and prone to human error. However, with the advent of AI-driven RPA, these processes have become more efficient and effective.

RPA solutions can analyze vast datasets in real-time, enabling them to identify potential risks and patterns that may go unnoticed by human analysts. By continuously monitoring transactions and financial activities, RPA systems can quickly detect any suspicious behavior or deviations from normal patterns. This allows BFSI institutions to take immediate action and prevent potential risks from materializing. One of the key advantages of AI-driven RPA in risk management is its ability to identify fraudulent activities. By analyzing transactional data and comparing it against historical patterns, RPA systems can identify anomalies that may indicate fraudulent behavior. This can include unusual transaction amounts, suspicious account activities, or patterns that deviate from the norm. When such anomalies are detected, the RPA system can automatically trigger alerts, prompting further investigation by human analysts. Furthermore, AI algorithms can continuously learn and adapt to new risks and fraud patterns, making RPA solutions even more effective over time. By leveraging machine learning techniques, RPA systems can improve their accuracy in detecting risks and fraud, thereby enhancing the overall risk management capabilities of BFSI institutions.

Key Market Challenges

Lack of Awareness and Understanding

A significant challenge confronting the Global Robotic Process Automation (RPA) in the BFSI market is the limited awareness and understanding among financial institutions regarding the transformative potential of RPA solutions. Many organizations, particularly smaller banks and financial firms, may not fully comprehend how RPA can optimize their operational processes, enhance compliance, and drive cost savings. This lack of awareness can hinder the adoption of RPA technology, leaving organizations with suboptimal operational efficiency and underutilized automation potential. To address this challenge, comprehensive awareness campaigns and educational initiatives are crucial. These initiatives should emphasize how RPA can streamline routine tasks, mitigate risks, and improve customer service within the BFSI sector. Real-world case studies and practical examples showcasing the tangible benefits of RPA can serve as compelling tools to deepen understanding and drive adoption.

Complexity and Integration Challenges

The implementation and management of RPA solutions can be intricate, especially for BFSI organizations with limited IT resources or experience in automation technologies. Configuring RPA systems effectively and integrating them with existing processes and legacy systems can present technical complexities. Compatibility issues during integration can lead to delays and hinder RPA's ability to deliver optimal performance. To overcome these challenges, it is imperative to simplify the deployment and management of RPA solutions. User-friendly interfaces and intuitive configuration options should be provided to streamline setup and customization. Additionally, BFSI organizations should have access to comprehensive support and guidance, including documentation, tutorials, and expert assistance to facilitate seamless integration and resolve any technical hurdles. Simplifying the implementation process ensures that RPA technology can augment BFSI operations without disruptions.

Mitigating False Positives and Performance Optimization

While RPA solutions excel in automating tasks, they face the challenge of false positives - instances where legitimate operations are mistakenly flagged as issues. These false positives can disrupt workflows and create inefficiencies in BFSI processes. Additionally, the performance of RPA systems can be a concern, especially when handling a high volume of transactions and tasks. Striking the right balance between rigorous automation and minimizing false positives while optimizing performance is paramount. To address this challenge, continuous refinement of RPA algorithms and technologies is essential. Enhanced algorithms can reduce false positives by accurately distinguishing between genuine anomalies and non-issues. The optimization of RPA systems can minimize processing delays and ensure the efficient execution of tasks, even during peak workloads. Ongoing improvements in these aspects empower BFSI organizations to harness the benefits of RPA while maintaining smooth and efficient operations, ultimately enhancing their overall efficiency and service quality.

Key Market Trends

Rise in Sophisticated Cyber Threats

The global market for Robotic Process Automation (RPA) in the BFSI sector is witnessing a surge in sophisticated cyber threats targeting financial institutions. Malicious actors are continually evolving their tactics to exploit vulnerabilities, infiltrate banking systems, and gain unauthorized access to sensitive financial data. Consequently, there is a growing demand for advanced RPA solutions that can effectively detect and counter these sophisticated threats. To address this demand, RPA solution providers are intensifying their efforts to develop intelligent and adaptive RPA systems equipped with the capability to analyze transaction patterns, identify anomalies, and provide real-time protection against emerging threats. These advanced RPA solutions leverage cutting-edge technologies such as artificial intelligence and machine learning to bolster their threat detection capabilities. By processing substantial volumes of financial data, these solutions can swiftly identify irregularities and deviations from expected behavior, enabling financial organizations to respond proactively to evolving threats. The objective is to equip financial institutions with proactive defense mechanisms that can adapt to the ever-changing threat landscape. As RPA technologies advance continuously, financial organizations can benefit from robust security measures that safeguard their operations, protect customer assets, and ensure compliance with stringent financial regulations. By investing in intelligent and adaptive RPA solutions, financial institutions can maintain a vigilant stance against cyber adversaries and safeguard the integrity and confidentiality of their financial operations.

Shift towards Cloud-based RPA Solutions

The global market is witnessing a notable shift towards cloud-based Robotic Process Automation (RPA) solutions within the BFSI sector. This shift is primarily driven by the increasing adoption of cloud computing and the migration of financial processes to cloud-based platforms. Financial institutions are actively seeking RPA solutions that seamlessly integrate with their cloud infrastructure to ensure comprehensive automation and security for their operations.

Cloud-based RPA solutions offer distinct advantages. Firstly, they provide scalability, enabling financial organizations to dynamically allocate resources in response to changing workloads. This scalability ensures that RPA systems can efficiently handle fluctuations in transaction volumes and automate financial processes during peak demand periods.

Moreover, cloud-based RPA solutions deliver flexibility. They can be readily deployed and managed across diverse cloud environments, allowing financial institutions the freedom to select the cloud platform that best aligns with their operational requirements. This flexibility ensures seamless integration with existing cloud infrastructure and accommodates the specific needs of each organization.

Integration of Artificial Intelligence and Machine Learning

The integration of artificial intelligence (AI) and machine learning (ML) technologies into RPA solutions is a prominent trend in the BFSI market. AI and ML algorithms possess the capacity to analyze vast datasets, discern patterns, and detect anomalies in real-time, enabling RPA systems to adapt and effectively respond to evolving challenges. These advanced technologies enhance the precision and efficiency of RPA solutions, reducing instances of false positives and false negatives. RPA solution providers are making substantial investments in AI and ML capabilities to elevate threat detection, automate financial operations, and proactively defend against emerging risks. By harnessing the potential of AI and ML, financial institutions can streamline processes, enhance compliance, and elevate customer service while effectively safeguarding against evolving threats.

Segmental Insights

Application Insights

The banking segment holds the largest market share of more than 59.6% in 2022. Lack of skilled resources, high personnel costs, and the need to increase productivity are the key factors driving the adoption of RPA in the banking sector. Furthermore, the rising market pressure is impelling banks to seek different opportunities to reduce operational costs, maximize efficiency, and accelerate their productivity gains. In addition to enabling easy review of documents, owing to its accessibility, robotic process automation in banks has helped create transparency in an organization where every transaction can be recorded, categorized, and stored.

The financial services and insurance segment is expected to witness the fastest growth at a CAGR of 41.1%. Identifying and automating various processes in the financial services sector is becoming a key priority of robotic process automation vendors. RPA solutions have helped financial services providers to assign employees to value-added tasks to enhance organizational value. The structured legacy systems in BFSI organizations are hard to integrate with the advanced RPA solutions, and modernizing legacy systems becomes a challenge in terms of data accessibility and data migration. In such cases, RPA services, which enable seamless transition, are used. robotic process automation helps organizations automate a time-consuming task so that an employee can focus on customer services by realigning organizational resources.

Deployment Insights

The on-premise segment accounted for a market share of 54.4% in 2022. The BFSI industry handles extremely private and sensitive client information. To have better control over the security of sensitive data, some organizations prefer to maintain it on-premise. With on-premise RPA deployments, BFSI organizations can store data inside of their own infrastructure while still adhering to stringent industry standards and privacy legislation. For organizations with strict security standards and data governance procedures, this tendency is especially pertinent.

The cloud segment is expected to expand at the highest CAGR of 42.1%. RPA technologies in the cloud provide BFSI organizations with a cost-effective alternative. By utilizing the cloud, businesses can grow their RPA installations as necessary and avoid making large upfront infrastructure costs. Platforms for RPA in the cloud often include flexible price structures that let businesses only pay for the resources they use. With the help of this trend, BFSI companies may save expenses while increasing automation efficiency.

Type Insights

The service segment accounted for the largest market share of over 62.3% in 2022 and is likely to dominate the market over the forecast period. The services segment is further bifurcated into consulting, implementation, and training. To cope with the intense industrial competition, service providers are focusing on the enhancement of advisory, training, and consultancy services. These enhancements facilitate cost reduction of various BFSI processes by minimizing the efforts of a Full-Time Employee (FTE). The RPA services specifically focus on the identification of automation opportunities in different organizational processes. Moreover, the service providers also help in planning the development of a suitable solution, choosing the right vendors, and proofing concepts of the solutions to check their real-time usability. All such activities enable precise deployment of the RPA platform, thereby strengthening the core of automation within a BFSI organization. RPA solutions help reduce compliance costs and the overall risks occurring due to information deficits & other operational gaps.

Regional Insights

The North American regional market dominated the robotic process automation in BFSI market in 2022 and accounted for 37.2% market share and is anticipated to retain its position over the forecast period. It is estimated that a U.S. bank with USD 10+ billion assets spends an average of USD 50 million per year on KYC compliance, CDD, and onboarding. The increasing cost of KYC and AML compliance, steep fines, and regulatory scrutiny for non-compliance are necessitating banks and financial institutions to consider new technologies such as automation to identify & prevent theft, financial fraud, money laundering, and terrorist financing, as well as eliminate manual tasks, improve compliance with government regulations, and reduce costs of compliance. Moreover, the growing need for process management and automation solutions in the BFSI sector is anticipated to boost regional market growth.

Asia Pacific is anticipated to rise as the fastest-developing regional market at a CAGR of 42.0% over the forecast period. An increasing number of financial institutions in the Asia Pacific have identified processes suitable for RPA. Furthermore, BFSI organizations are using RPA and AI in areas such as back-office operations for compliance automation & process streamlining and front-end operations for customer service enhancement. Moreover, an increasing number of businesses in the Asia Pacific region are adopting robotic process automation solutions to increase production through faster execution of business processes, bolster overall financial performance, and maintain a competitive edge.

Key Market Players

Antworks

Atos SE

Automation Anywhere, Inc.

Blue Prism Limited

EdgeVerve Systems Ltd.

FPT Software

IBM

Kofax Inc.

Microsoft (Softomotive)

NICE

Nintex UK Ltd. (Kryon Systems)

Pegasystems Inc.

Protiviti Inc.

UiPath

Report Scope:

In this report, the Global Robotic Process Automation in BFSI Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Robotic Process Automation in BFSI Market, By Type:

• Solution
• Services

Robotic Process Automation in BFSI Market, By Deployment:

• Cloud
• On-premise

Robotic Process Automation in BFSI Market, By Organization:

• SMEs
• Large Enterprises

Robotic Process Automation in BFSI Market, By Application:

• Banking
• Financial Services & Insurance

Robotic Process Automation in BFSI Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Belgium
• Asia-Pacific
• China
• India
• Japan
• Australia
• South Korea
• Indonesia
• Vietnam
• South America
• Brazil
• Argentina
• Colombia
• Chile
• Peru
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE
• Turkey
• Israel

Competitive Landscape

• Company Profiles: Detailed analysis of the major companies present in the Global Robotic Process Automation in BFSI Market.

Available Customizations:

• Global Robotic Process Automation in BFSI market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Formulation of the Scope
• 2.4. Assumptions and Limitations
• 2.5. Sources of Research
  • 2.5.1. Secondary Research
  • 2.5.2. Primary Research
• 2.6. Approach for the Market Study
  • 2.6.1. The Bottom-Up Approach
  • 2.6.2. The Top-Down Approach
• 2.7. Methodology Followed for Calculation of Market Size & Market Shares
• 2.8. Forecasting Methodology
  • 2.8.1. Data Triangulation & Validation

3. Executive Summary

4. Impact of COVID-19 on Global Robotic Process Automation in BFSI Market

5. Voice of Customer

6. Global Robotic Process Automation in BFSI Market Overview

7. Global Robotic Process Automation in BFSI Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Type (Software, Services)
  • 7.2.2. By Deployment (Cloud, On-Premise)
  • 7.2.3. By Organization (SMEs, Large Enterprises)
  • 7.2.4. By Application (Banking, Financial Services & Insurance)
  • 7.2.5. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
• 7.3. By Company (2022)
• 7.4. Market Map

8. North America Robotic Process Automation in BFSI Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Type
  • 8.2.2. By Deployment
  • 8.2.3. By Organization
  • 8.2.4. By Application
  • 8.2.5. By Country
• 8.3. North America: Country Analysis
  • 8.3.1. United States Robotic Process Automation in BFSI Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Type
      • 8.3.1.2.2. By Deployment
      • 8.3.1.2.3. By Organization
      • 8.3.1.2.4. By Application
  • 8.3.2. Canada Robotic Process Automation in BFSI Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Type
      • 8.3.2.2.2. By Deployment
      • 8.3.2.2.3. By Organization
      • 8.3.2.2.4. By Application
  • 8.3.3. Mexico Robotic Process Automation in BFSI Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Type
      • 8.3.3.2.2. By Deployment
      • 8.3.3.2.3. By Organization
      • 8.3.3.2.4. By Application

9. Europe Robotic Process Automation in BFSI Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Type
  • 9.2.2. By Deployment
  • 9.2.3. By Organization
  • 9.2.4. By Application
  • 9.2.5. By Country
• 9.3. Europe: Country Analysis
  • 9.3.1. Germany Robotic Process Automation in BFSI Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Type
      • 9.3.1.2.2. By Deployment
      • 9.3.1.2.3. By Organization
      • 9.3.1.2.4. By Application
  • 9.3.2. France Robotic Process Automation in BFSI Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Type
      • 9.3.2.2.2. By Deployment
      • 9.3.2.2.3. By Organization
      • 9.3.2.2.4. By Application
  • 9.3.3. United Kingdom Robotic Process Automation in BFSI Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Type
      • 9.3.3.2.2. By Deployment
      • 9.3.3.2.3. By Organization
      • 9.3.3.2.4. By Application
  • 9.3.4. Italy Robotic Process Automation in BFSI Market Outlook
    • 9.3.4.1. Market Size & Forecast
      • 9.3.4.1.1. By Value
    • 9.3.4.2. Market Share & Forecast
      • 9.3.4.2.1. By Type
      • 9.3.4.2.2. By Deployment
      • 9.3.4.2.3. By Organization
      • 9.3.4.2.4. By Application
  • 9.3.5. Spain Robotic Process Automation in BFSI Market Outlook
    • 9.3.5.1. Market Size & Forecast
      • 9.3.5.1.1. By Value
    • 9.3.5.2. Market Share & Forecast
      • 9.3.5.2.1. By Type
      • 9.3.5.2.2. By Deployment
      • 9.3.5.2.3. By Organization
      • 9.3.5.2.4. By Application
  • 9.3.6. Belgium Robotic Process Automation in BFSI Market Outlook
    • 9.3.6.1. Market Size & Forecast
      • 9.3.6.1.1. By Value
    • 9.3.6.2. Market Share & Forecast
      • 9.3.6.2.1. By Type
      • 9.3.6.2.2. By Deployment
      • 9.3.6.2.3. By Organization
      • 9.3.6.2.4. By Application

10. South America Robotic Process Automation in BFSI Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Type
  • 10.2.2. By Deployment
  • 10.2.3. By Organization
  • 10.2.4. By Application
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Robotic Process Automation in BFSI Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Type
      • 10.3.1.2.2. By Deployment
      • 10.3.1.2.3. By Organization
      • 10.3.1.2.4. By Application
  • 10.3.2. Colombia Robotic Process Automation in BFSI Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Type
      • 10.3.2.2.2. By Deployment
      • 10.3.2.2.3. By Organization
      • 10.3.2.2.4. By Application
  • 10.3.3. Argentina Robotic Process Automation in BFSI Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Type
      • 10.3.3.2.2. By Deployment
      • 10.3.3.2.3. By Organization
      • 10.3.3.2.4. By Application
  • 10.3.4. Chile Robotic Process Automation in BFSI Market Outlook
    • 10.3.4.1. Market Size & Forecast
      • 10.3.4.1.1. By Value
    • 10.3.4.2. Market Share & Forecast
      • 10.3.4.2.1. By Type
      • 10.3.4.2.2. By Deployment
      • 10.3.4.2.3. By Organization
      • 10.3.4.2.4. By Application
  • 10.3.5. Peru Robotic Process Automation in BFSI Market Outlook
    • 10.3.5.1. Market Size & Forecast
      • 10.3.5.1.1. By Value
    • 10.3.5.2. Market Share & Forecast
      • 10.3.5.2.1. By Type
      • 10.3.5.2.2. By Deployment
      • 10.3.5.2.3. By Organization
      • 10.3.5.2.4. By Application

11. Middle East & Africa Robotic Process Automation in BFSI Market Outlook

• 11.1. Market Size & Forecast
  • 11.1.1. By Value
• 11.2. Market Share & Forecast
  • 11.2.1. By Type
  • 11.2.2. By Deployment
  • 11.2.3. By Organization
  • 11.2.4. By Application
  • 11.2.5. By Country
• 11.3. Middle East & Africa: Country Analysis
  • 11.3.1. Saudi Arabia Robotic Process Automation in BFSI Market Outlook
    • 11.3.1.1. Market Size & Forecast
      • 11.3.1.1.1. By Value
    • 11.3.1.2. Market Share & Forecast
      • 11.3.1.2.1. By Type
      • 11.3.1.2.2. By Deployment
      • 11.3.1.2.3. By Organization
      • 11.3.1.2.4. By Application
  • 11.3.2. UAE Robotic Process Automation in BFSI Market Outlook
    • 11.3.2.1. Market Size & Forecast
      • 11.3.2.1.1. By Value
    • 11.3.2.2. Market Share & Forecast
      • 11.3.2.2.1. By Type
      • 11.3.2.2.2. By Deployment
      • 11.3.2.2.3. By Organization
      • 11.3.2.2.4. By Application
  • 11.3.3. South Africa Robotic Process Automation in BFSI Market Outlook
    • 11.3.3.1. Market Size & Forecast
      • 11.3.3.1.1. By Value
    • 11.3.3.2. Market Share & Forecast
      • 11.3.3.2.1. By Type
      • 11.3.3.2.2. By Deployment
      • 11.3.3.2.3. By Organization
      • 11.3.3.2.4. By Application
  • 11.3.4. Turkey Robotic Process Automation in BFSI Market Outlook
    • 11.3.4.1. Market Size & Forecast
      • 11.3.4.1.1. By Value
    • 11.3.4.2. Market Share & Forecast
      • 11.3.4.2.1. By Type
      • 11.3.4.2.2. By Deployment
      • 11.3.4.2.3. By Organization
      • 11.3.4.2.4. By Application
  • 11.3.5. Israel Robotic Process Automation in BFSI Market Outlook
    • 11.3.5.1. Market Size & Forecast
      • 11.3.5.1.1. By Value
    • 11.3.5.2. Market Share & Forecast
      • 11.3.5.2.1. By Type
      • 11.3.5.2.2. By Deployment
      • 11.3.5.2.3. By Organization
      • 11.3.5.2.4. By Application

12. Asia Pacific Robotic Process Automation in BFSI Market Outlook

• 12.1. Market Size & Forecast
  • 12.1.1. By Type
  • 12.1.2. By Deployment
  • 12.1.3. By Organization
  • 12.1.4. By Application
  • 12.1.5. By Country
• 12.2. Asia-Pacific: Country Analysis
  • 12.2.1. China Robotic Process Automation in BFSI Market Outlook
    • 12.2.1.1. Market Size & Forecast
      • 12.2.1.1.1. By Value
    • 12.2.1.2. Market Share & Forecast
      • 12.2.1.2.1. By Type
      • 12.2.1.2.2. By Deployment
      • 12.2.1.2.3. By Organization
      • 12.2.1.2.4. By Application
  • 12.2.2. India Robotic Process Automation in BFSI Market Outlook
    • 12.2.2.1. Market Size & Forecast
      • 12.2.2.1.1. By Value
    • 12.2.2.2. Market Share & Forecast
      • 12.2.2.2.1. By Type
      • 12.2.2.2.2. By Deployment
      • 12.2.2.2.3. By Organization
      • 12.2.2.2.4. By Application
  • 12.2.3. Japan Robotic Process Automation in BFSI Market Outlook
    • 12.2.3.1. Market Size & Forecast
      • 12.2.3.1.1. By Value
    • 12.2.3.2. Market Share & Forecast
      • 12.2.3.2.1. By Type
      • 12.2.3.2.2. By Deployment
      • 12.2.3.2.3. By Organization
      • 12.2.3.2.4. By Application
  • 12.2.4. South Korea Robotic Process Automation in BFSI Market Outlook
    • 12.2.4.1. Market Size & Forecast
      • 12.2.4.1.1. By Value
    • 12.2.4.2. Market Share & Forecast
      • 12.2.4.2.1. By Type
      • 12.2.4.2.2. By Deployment
      • 12.2.4.2.3. By Organization
      • 12.2.4.2.4. By Application
  • 12.2.5. Australia Robotic Process Automation in BFSI Market Outlook
    • 12.2.5.1. Market Size & Forecast
      • 12.2.5.1.1. By Value
    • 12.2.5.2. Market Share & Forecast
      • 12.2.5.2.1. By Type
      • 12.2.5.2.2. By Deployment
      • 12.2.5.2.3. By Organization
      • 12.2.5.2.4. By Application
  • 12.2.6. Indonesia Robotic Process Automation in BFSI Market Outlook
    • 12.2.6.1. Market Size & Forecast
      • 12.2.6.1.1. By Value
    • 12.2.6.2. Market Share & Forecast
      • 12.2.6.2.1. By Type
      • 12.2.6.2.2. By Deployment
      • 12.2.6.2.3. By Organization
      • 12.2.6.2.4. By Application
  • 12.2.7. Vietnam Robotic Process Automation in BFSI Market Outlook
    • 12.2.7.1. Market Size & Forecast
      • 12.2.7.1.1. By Value
    • 12.2.7.2. Market Share & Forecast
      • 12.2.7.2.1. By Type
      • 12.2.7.2.2. By Deployment
      • 12.2.7.2.3. By Organization
      • 12.2.7.2.4. By Application

13. Market Dynamics

• 13.1. Drivers
• 13.2. Challenges

14. Market Trends and Developments

15. Company Profiles

• 15.1. Antworks
  • 15.1.1. Business Overview
  • 15.1.2. Key Revenue and Financials
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel/Key Contact Person
  • 15.1.5. Key Product/Services Offered
• 15.2. Atos SE
  • 15.2.1. Business Overview
  • 15.2.2. Key Revenue and Financials
  • 15.2.3. Recent Developments
  • 15.2.4. Key Personnel/Key Contact Person
  • 15.2.5. Key Product/Services Offered
• 15.3. Automation Anywhere, Inc.
  • 15.3.1. Business Overview
  • 15.3.2. Key Revenue and Financials
  • 15.3.3. Recent Developments
  • 15.3.4. Key Personnel/Key Contact Person
  • 15.3.5. Key Product/Services Offered
• 15.4. Blue Prism Limited
  • 15.4.1. Business Overview
  • 15.4.2. Key Revenue and Financials
  • 15.4.3. Recent Developments
  • 15.4.4. Key Personnel/Key Contact Person
  • 15.4.5. Key Product/Services Offered
• 15.5. EdgeVerve Systems Ltd.
  • 15.5.1. Business Overview
  • 15.5.2. Key Revenue and Financials
  • 15.5.3. Recent Developments
  • 15.5.4. Key Personnel/Key Contact Person
  • 15.5.5. Key Product/Services Offered
• 15.6. FPT Software
  • 15.6.1. Business Overview
  • 15.6.2. Key Revenue and Financials
  • 15.6.3. Recent Developments
  • 15.6.4. Key Personnel/Key Contact Person
  • 15.6.5. Key Product/Services Offered
• 15.7. IBM
  • 15.7.1. Business Overview
  • 15.7.2. Key Revenue and Financials
  • 15.7.3. Recent Developments
  • 15.7.4. Key Personnel/Key Contact Person
  • 15.7.5. Key Product/Services Offered
• 15.8. Kofax Inc.
  • 15.8.1. Business Overview
  • 15.8.2. Key Revenue and Financials
  • 15.8.3. Recent Developments
  • 15.8.4. Key Personnel/Key Contact Person
  • 15.8.5. Key Product/Services Offered
• 15.9. Microsoft (Softomotive)
  • 15.9.1. Business Overview
  • 15.9.2. Key Revenue and Financials
  • 15.9.3. Recent Developments
  • 15.9.4. Key Personnel/Key Contact Person
  • 15.9.5. Key Product/Services Offered
• 15.10. NICE
  • 15.10.1. Business Overview
  • 15.10.2. Key Revenue and Financials
  • 15.10.3. Recent Developments
  • 15.10.4. Key Personnel/Key Contact Person
  • 15.10.5. Key Product/Services Offered
• 15.11. Nintex UK Ltd. (Kryon Systems)
  • 15.11.1. Business Overview
  • 15.11.2. Key Revenue and Financials
  • 15.11.3. Recent Developments
  • 15.11.4. Key Personnel/Key Contact Person
  • 15.11.5. Key Product/Services Offered
• 15.12. Pegasystems Inc.
  • 15.12.1. Business Overview
  • 15.12.2. Key Revenue and Financials
  • 15.12.3. Recent Developments
  • 15.12.4. Key Personnel/Key Contact Person
  • 15.12.5. Key Product/Services Offered
• 15.13. Protiviti Inc.
  • 15.13.1. Business Overview
  • 15.13.2. Key Revenue and Financials
  • 15.13.3. Recent Developments
  • 15.13.4. Key Personnel/Key Contact Person
  • 15.13.5. Key Product/Services Offered
• 15.14. UiPath
  • 15.14.1. Business Overview
  • 15.14.2. Key Revenue and Financials
  • 15.14.3. Recent Developments
  • 15.14.4. Key Personnel/Key Contact Person
  • 15.14.5. Key Product/Services Offered

16. Strategic Recommendations

17. About Us & Disclaimer