AI in BFSI Market witness remarkable growth 45% CAGR Forecast 2020- 2026


The Artificial Intelligence in BFSI Market may surpass USD 80 billion by the year 2026.Growing demand for virtual assistance coupled with surging adoption of cloud-based services across BFSI enterprises could strengthen the industry outlook.

The Artificial Intelligence (AI) in BFSI Market is poised to garner sizeable growth on account of rapid integration of fintech innovations and technology in banking process. Currently, the BFSI sector is going through a digitization phase to automate repetitive tasks. This have paved the way for numerous innovative AI-based solutions that intend to simplify overall banking operations.

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Banking institutions are constantly looking to adopt advanced solutions like blockchain, big data, biometrics, and cloud computing to simplify the process of analyzing, reporting, & collecting huge volumes of data as well as to attain meaningful insights to improve banking operations. For the record, AI-based technologies are combined with machine learning algorithms to streamline the process of analyzing and collecting data in banks. These solutions offer in-depth analysis from the user data and allows banks to take needed decisions, thus gaining higher ROI while enabling superior operational efficiency.

In terms of application, the AI in BFSI market is bifurcated into customer service, back-office/operation, risk management, financial advisory, and compliance & security. Out of these, the customer service segment is anticipated to witness remarkable growth, controlling almost 45% of the global AI in BFSI market share by 2026. Increasing competition across the BFSI sector along with the rising need to procure a large customer base has influenced enterprises to shift their focus on improving customer relationships.

Financial institutes and enterprises are adopting AI-backed applications to enhance their engagement with customers. Additionally, firms are using AI-based chatbots or virtual communication assistants to improve customer query handling, service delivery, assisting customers in banking procedures, and virtual assistance. Citing an instance, earlier in June 2020, RBL Bank added a slew of new features and enabled additional banking transactions to its AI-powered conversational Chatbot, RBL Cares, allowing it to offer superior real-time customer service assistance.

Customer Relationship Management (CRM) solutions have gained a prominent spot in the banking sector as it helps in better understanding customer’s need. CRM allows banks to easily manage their customers while quickly providing the right solutions for their queries. In the present digital environment, financial institutions need CRM solutions to increase their lead conversion, scale productivity, personalize customer journeys, and facilitate efficient communication.

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On the regional front, North America is considered to be a highly lucrative market for AI in BFSI industry. Studies claim that the regional market could hold at least 50% of the overall market share by 2026. Currently, the BFSI sector is struggling to operate effectively due to the COVID-19 outbreak. As a result, banking institutions are turning towards innovative solutions to continue their operation and drive businesses. Taking April 2020 for instance, Temenos released its explainable AI models for financial institutes, allowing them to offer rapid loans to retail customers and SMEs.

Browse key industry insights spread across 250 pages with 412 market data tables and 30 figures & charts from the report, Artificial Intelligence in BFSI Market in detail along with the table of contents:

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Report Content

Chapter 3.   AI in BFSI Industry Insights

3.1.  Introduction
3.2.  Industry segmentation
3.3.  Impact of COVID-19 outbreak
3.3.1. By region
3.3.1.1.    North America
3.3.1.2.    Europe
3.3.1.3.    Asia Pacific
3.3.1.4.    Latin America
3.3.1.5.    Middle East & Africa
3.3.2. Industry value chain
3.3.2.1.  Suppliers
3.3.2.2.    AI in BFSI technology providers
3.3.2.3.    Marketing & distribution channels
3.3.3. Competitive landscape
3.3.3.1.  Strategy
3.3.3.2.    Distribution network
3.3.3.3.    Business growth
3.4.  AI in BFSI industry ecosystem analysis
3.5.  Evolution of AI in BFSI technology
3.6.  Investment landscape
3.6.1. AI investment
3.6.1.1.  Americas
3.6.1.2.    EMEA
3.6.1.3.    Asia Pacific
3.7.  Regulatory landscape
3.7.1. Health Insurance Portability and Accountability Act (HIPAA)
3.7.2. Payment Card Industry Data Security Standard (PCI DSS)
3.7.3. North American Electric Reliability Corp. (NERC) Standards
3.7.4. Federal Information Security Management Act (FISMA)
3.7.5. The Gramma-Leach-Bliley Act (GLB) Act of 1999
3.7.6. Sarbanes-Oxley Act of 2002
3.7.7. General Data Protection Regulation (GDPR)
3.7.8. Alternative Investment Fund Managers Directive (AIFMD)
3.7.9. Anti-Money Laundering Directive 2015/849/EU (AMLD)
3.7.10. Dodd–Frank Wall Street Reform and Consumer Protection Act
3.7.11.    European Market Infrastructure Regulation (EMIR)
3.7.12.    Foreign Account Tax Compliance Act (FATCA)
3.7.13.    Markets in Financial Instruments Directive (MIFID)
3.8.  Use cases
3.8.1. Algorithmic trading
3.8.2. Anti-money laundering (AML) pattern detection
3.8.3. Customer service
3.8.4. Fraud detection
3.8.5. Insurance & loan underwriting
3.8.6. Portfolio management
3.9.  Industry impact forces
3.9.1. Growth drivers
3.9.1.1.  Exponentially growing digital data
3.9.1.2.    Rising investment in AI
3.9.1.3.    Increasing partnership between financial institutes and fintech companies
3.9.1.4.    Growing need to provide enhanced customer experience
3.9.2. Industry pitfalls & challenges
3.9.2.1.  Data safety & security
3.9.2.2.    Black box effect
3.10.    Growth potential analysis
3.11. Porter’s analysis
3.12.    PESTEL analysis

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