Artificial Intelligence in BFSI Market Top Key Participant: Google, Microsoft, IBM, Intel, AWS, SAP, Oracle, Salesforce, IPsoft, Palantir, Lexalytics, Inbenta technologies, Next IT, and Interaction

The excellent growth dynamics of the global artificial intelligence (AI) in BFSI market can be traced by the recent instance of Metromile, a leader in pay-per-mile car insurance in the U.S., launching its new AI-based smart claims assistant, AVA. The automated system reportedly utilizes machine learning capabilities to reconstruct the accident scene and promptly ascertains if claim details are true. Apparently, the claim is approved within seconds after the details are verified and the AI-based tool further notifies the car owner of the expedited claim. With the increasing adoption of high-grade technologies such as IoT and Big Data, the commercialization potential of AI in BFSI industry has observed a marked uptick in the past few years.

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Elaborating further, the unveiling of AVA signifies the transformative phase being witnessed across the worldwide insurance sector where prominent AI in BFSI industry players have been increasingly deploying advanced algorithms to enhance the underwriting process. This has, in turn, impelled the insurance segment of the AI in BFSI market which is forecast to register an outstanding y-o-y growth rate of over 38% during 2018-2024.

A brief insight of how banking sector has been shaping AI in BFSI industry trends

Over the past few years, numerous globally renowned banking institutions have been pouring in massive funds to develop cutting-edge AI applications that have invariably assisted them in optimizing performance, consolidating remuneration streams, and better serving their customers. In this regard, banks are increasingly forming partnerships with fintech corporations to integrate the latest technologies into banking products and services which has resulted in the development of advanced customer behavior analytics inventions, chatbots, and customer relationship management (CRM) solutions. Enumerated below is a succinct outline of how banks have been readily embracing AI-based solutions that has subsequently transformed the strategic and competitive landscape of AI in BFSI market:

• Embarking on its AI and automation journey in September 2017, State Bank of India unveiled its first AI-powered chat & voice assistant, named as SBI’s Intelligent Assistant or SIA. The chatbot reportedly aids customers with everyday banking tasks just like a bank representative and has a remarkable capacity to handle close to 10,000 enquiries per second or 864 million in a day. Arguably, the deployment of an AI-based solution of this scale is cited to be first of its kind in the banking segment of the global AI in BFSI market. With an astonishing customer strength of around 420 million, State Bank of India’s latest move towards integrating AI is being viewed as a major leap of faith for India’s banking sector.

• Regarded as one of the world’s largest banks, the U.S. based JPMorgan Chase has recently launched a digital platform that analyzes legal documents and extracts the relevant data. The AI-based invention, named as Contract Intelligence (COiN) platform, is an outcome of the initial implementation through machine learning technology and has an exceptional ability to revise approximately 12,000 annual sales agreements within minutes. The platform is being aptly termed as revolutionary across AI in BFSI industry given the fact that it effectively eliminates the need to manually review the said number of sales agreements which generally takes around 360,000 hours.

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Owing to the launch of intuitive, easy-to-use, and highly-efficient AI-based tools, the banking segment of the AI in BFSI market has been proliferating at an exceptional pace in the recent times. In fact, as per a research report compiled by Global Market Insights, Inc., the banking segment apportioned more than 50% of the total remuneration portfolio of AI in BFSI industry in the year 2017. With the unveiling of advanced AI technologies that provide real-time insights into every aspect of banking operations and leverage the abundance of data to gain a granular understanding of consumer behavior, the banking segment is slated to lead the end use spectrum of this business space over the ensuing years.

Partial Chapter of the Table of Content

Chapter 3. AI in BFSI Industry Insights

3.1. Introduction

3.2. Industry segmentation

3.3. Industry landscape, 2013-2024

3.4. Industry ecosystem analysis

3.5. Industry evolution

3.6. Market news

3.7. Investment landscape

3.7.1. AI investment

3.7.1.1. Americas

3.7.1.2. EMEA

3.7.1.3. Asia Pacific

3.7.2. Fintech investment

3.7.2.1. Americas

3.7.2.2. EMEA

3.7.2.3. Asia Pacific

3.8. Regulatory landscape

3.8.1. Health Insurance Portability and Accountability Act (HIPAA)

3.8.2. Payment Card Industry Data Security Standard (PCI DSS)

3.8.3. North American Electric Reliability Corp. (NERC) Standards

3.8.4. Federal Information Security Management Act (FISMA)

3.8.5. The Gramma-Leach-Bliley Act (GLB) Act of 1999

3.8.6. Sarbanes-Oxley Act of 2002

3.8.7. General Data Protection Regulation (GDPR)

3.8.8. Alternative Investment Fund Managers Directive (AIFMD)

3.8.9. Anti-Money Laundering Directive 2015/849/EU (AMLD)

3.8.10. Dodd–Frank Wall Street Reform and Consumer Protection Act

3.8.11. European Market Infrastructure Regulation (EMIR)

3.8.12. Foreign Account Tax Compliance Act (FATCA)

3.8.13. Markets in Financial Instruments Directive (MIFID)

3.9. Use cases

3.9.1. Algorithmic trading

3.9.2. Anti-money laundering (AML) pattern detection

3.9.3. Customer service

3.9.4. Fraud detection

3.9.5. Insurance & loan underwriting

3.9.6. Portfolio management

3.10. Industry impact forces

3.10.1. Growth drivers

3.10.1.1. Exponentially growing digital data

3.10.1.2. Rising investment in AI

3.10.1.3. Increasing partnership between financial institutes and fintech companies

3.10.1.4. Growing need to provide enhanced customer experience

3.10.2. Industry pitfalls & challenges

3.10.2.1. Data safety & security

3.10.2.2. Black box effect

3.11. Growth potential analysis

3.12. Porter's analysis

3.13. PESTEL analysis

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