The Artificial Intelligence (AI) in BFSI Market from data analytics & visualization solution segment is expected to grow at 30% CAGR from 2022 to 2028 driven by the continuously growing digital data and increasing inclination toward the customer-centric business model.
The AI in BFSI Market is expected to surpass USD 140 billion by 2028, as reported in a research study by Global Market Insights Inc. The market growth can be attributed to the emergence of fintech technology and innovations in banking processes along with digitization.
The growing demand to analyze, report, and collect a large volume of data and gain meaningful insights to support banking processes will support market growth. The increasing adoption of advanced technologies including big data, blockchain, cloud computing, and biometrics generates extensive data. AI-based solutions are integrated with machine learning algorithms to help banks in collecting and analyzing data. It provides an in-depth analysis of the customer data and helps banks to make decisions, enabling operational efficiency and gaining higher ROI.
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The data analytics & visualization solution segment is expected to grow at a CAGR of over 30% from 2022 to 2028. The demand for data analytics across financial enterprises has increased exponentially, which is attributed to the continuously growing digital data and increasing inclination toward the customer-centric business model. Data analytics & visualization solutions help BFSI enterprises in analyzing a large volume of structure & unstructured data and provide in-depth analysis. They also help organizations to identify customer needs and provide personalized services.
The computer vision technology segment is anticipated to grow significantly over the forecast timeline. Insurance and wealth management companies are using automated processing with computer vision technology to analyze digital information. Enterprises leverage computer vision to automate the analysis of digital information such as images, content, etc. This technology is also used in processes including underwriting and automated data extraction.
The customer service segment is projected to hold a market share of above 40% in the global AI in BFSI market by 2028. Growing competition in the BFSI sector and the need to acquire a large customer base are driving enterprises to focus on improved customer relationships. AI-based chatbots are being deployed extensively in the financial services sector to improve service delivery, customer query handling, and assisting customers in banking transactions.
The adoption of AI-based solutions for wealth management is growing significantly and is expected to register lucrative gains by 2028. The use of AI-backed tools helps wealth managers to advise customers based on analyzed historical data and present analysis. It also provides AI-powered recommendations and insights specifically tailored to customer requirements. For instance, in November 2021, Verint, Inc. announced AI-driven Real-time Agent Assist capabilities. These capabilities included sentiment analysis and new work assist function, which offered brands a robust tool to help employees and raise customer satisfaction and loyalty.
North America is anticipated to hold a substantial revenue share in the AI in BFSI market by 2028. The COVID-19 outbreak has severely impacted the regional BFSI sector. In response, the companies operating in the market are developing innovative solutions to help banking & financial institutions to drive their business. For instance, in April 2020, Temenos, a leading banking software company, launched explainable AI models for banks. This enabled credit unions & banks to provide rapid loans to SMEs and retail customers.
The companies operating in the market are focusing on the development of advanced AI-based solutions to help banking institutions in data-driven applications. Technology companies are leveraging AI and ML capabilities to improve the data collection processes, which is expected to drive the demand for AI-based solutions from financial institutions to modernize data collection process and improve customer experience. For instance, in February 2022, Intel introduced an upgraded version of the OpenVINO toolkit. This update added Natural Language Processing (NLP) support, improved device portability on hardware and improved inferencing performance. This tool helps users to develop solutions to handle tasks including automatic speech recognition, NLP, and emulation of human vision.
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Some major findings of the AI in BFSI market report include:
- The increasing adoption of cloud-based services and demand for virtual assistance across BFSI enterprises will support technology development in the market.
- North America is expected to hold a major market share in AI solutions due to hefty investments in AI technology by companies such as AWS and Google.
- The major players operating in the AI in BFSI market are Amazon Web Services, Google LLC, Intel Corporation, Microsoft Corporation, and Oracle Corporation, among others.
- The companies operating in the market are focusing on the development of innovative AI-based solutions such as explainable AI (XAI) models and platforms.
Partial chapters of report table of contents (TOC):
Chapter 2 Executive Summary
2.1 AI in BFSI industry 360º synopsis, 2018 – 2028
2.1.1 Business trends
2.1.2 Regional trends
2.1.3 Component trends
2.1.4 Technology trends
2.1.5 Application trends
2.1.6 End-use trends
Chapter 3 AI in BFSI Industry Insights
3.1 Introduction
3.2 Impact of COVID-19 outbreak
3.3 Evolution of AI in BFSI technology
3.4 AI in BFSI industry ecosystem analysis
3.5 Investment portfolio
3.6 Patent analysis
3.7 Regulatory landscape
3.8 Use cases
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 with AI tools
3.10 Growth potential analysis
3.11 Porter’s analysis
3.12 PESTEL analysis
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