Banking and Financial institutions have been using artificial intelligence for a number of years, although initially restricted to specialised applications. Nowadays, Artificial Intelligence (AI) techniques are being increasingly adopted for a new variety of applications in the banking services industry. Banks are spending more in research and development of AI technologies, and technology has become an important part of a variety of activities, from enhancing customer service to more effective enforcement management. This is due to enhanced access to massive data sets and increased data processing power. In recent years, there has been an increase in interest among managers, regulators and policy makers owing to the shrewd analysis of datasets for learning and prediction of transactions. Technological advances in the financial sector have led to the growth of infrastructure and volume of data. The emergence of electronic trading platforms was followed by an increase in the availability of high-quality market data in standardised formats. Thus, such factors are driving the growth of AI in banking and finance market.
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Detection of credit card fraud has stood as one of the most promising applications of artificial intelligence. Banks are equipped with monitoring systems or workflow engines that are trained on historical payment data. Training, back-testing and validation algorithms are based on very broad data sets of credit card transaction data. Classification algorithms can then be used to mark activities as fraud versus non-fraud and to stop fraudulent transactions in real time. Mastercard, for example, has introduced Decision Intelligence (DI) technology and its approach is relatively straightforward. Rather than being confined to a predefined set of rules, DI draws trends from the historical shopping and spending behaviour of Mastercard customers in order to create a baseline. It is against this baseline that Mastercard compares and rates every new customer transaction. This is a huge improvement over conventional fraud detection systems, which rely on a common template to analyse all transactions is thus driving the adoption of AI in banking and finance market. Similarly, banking chatbots and robo-advisory services is further finding application across this industry. Chatbots and robo-advisors have made major appeals to millennial customers who do not need a physical consultant to feel secure while investing and who are less likely to validate the fees paid to human consultants. There are many ways in which AI chatbots can boost the banking industry, including helping users manage their money and savings. For example, Plum is a chatbot that can be accessed via Facebook Messenger and helps a customer save money in small increments. After initial registration, Plum is linked to the customer's bank account and its AI engine, then analyses the customer's income and expenditure history and estimates how much they can afford to save. Tiny sums are then deposited in the Plum savings account on a periodic basis. These applications are propelling AI in banking and finance market.
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The implementation of artificial intelligence in the banking sector has therefore proved to be a lucrative deal. AI has helped to gain a deeper understanding of consumer purchasing habits that help banks tailor products by incorporating personalised features. It ensures positive customer loyalty, good partnerships and business development for the bank. Hence, these factors have led to the increasing adoption of AI in banking and finance market
The detailed research study provides qualitative and quantitative analysis of the global AI in banking and finance market. The AI in banking and finance market has been analyzed from demand as well as supply side. The demand side analysis covers market revenue across regions and further across all the major countries. The supply side analysis covers the major market players and their regional and global presence and strategies. The geographical analysis done emphasizes on each of the major countries across North America, Europe, Asia Pacific, Middle East & Africa and Latin America
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Global AI In Banking and Finance Market
- By Offerings
- Solutions
- Services
- By Organization Size
- Small and Medium Enterprises
- Large Enterprises
- By Technology
- Machine Learning
- Computer Vision
- Natural Language Processing
- Others
- By Application
- Risk and Compliance Management
- Customer Engagement and Lead Generation
- Asset and Wealth Management
- Fraud Prevention
- Others
- By Region:
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- France
- The UK
- Spain
- Germany
- Italy
- Nordic Countries
- Denmark
- Finland
- Iceland
- Sweden
- Norway
- Benelux Union
- Belgium
- The Netherlands
- Luxembourg
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- New Zealand
- Australia
- South Korea
- Southeast Asia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Rest of Southeast Asia
- Rest of Asia Pacific
- Middle East & Africa
- Saudi Arabia
- UAE
- Egypt
- Kuwait
- South Africa
- Rest of Middle East & Africa
- Latin America
- Brazil
- Argentina
- Rest of Latin America
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