Apr 30, 2021

Analytics Insight say AI in banking to reach $48bn by 2025

AI
Banking
Digitaltransformation
Technology
Tilly Kenyon
2 min
With artificial intelligence (AI) technologies becoming increasingly integral to the banking industry, Analytics Insight expects revenue to boom
With artificial intelligence (AI) technologies becoming increasingly integral to the banking industry, Analytics Insight expects revenue to boom...

Analytics Insight has predicted that the Global Artificial Intelligence market revenue in the banking sector will be $48.3 billion by 2025 compared to $13.7 billion in 2019 growing at a CAGR of 28.6% during the forecast period, 2019-2024. 

Across the financial sector, there has been a surge in AI and the adoption of different technologies as customers are looking for more personalised, and easier services. People have shifted to the digital space, which has given rise to digital banking and open banking. AI has enabled the banking, financial services, and insurance (BFSI) sector to meet the demands of a smarter consumer base. 

This drastic growth of AI in banking is likely to attract more audiences and will pave the way for innovations. The emergence of fintech players has positively impacted the banking industry’s growth by providing digital assistance and compliance. AI has effectively led the BFSI market to enhanced operational efficiency, intelligent automation, and customisation.

AI in banking 

McKinsey estimated that in the first few months of the COVID-19 pandemic, the use of online and mobile banking channels across countries increased by 20 to 50% and is expected to continue at this higher level once the pandemic subside.

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Conversational AI has been adopted by most banks as it improves customer experience, enables cost optimisation, and allows employees to carry out other tasks. 

“One of the most important use cases for AI in banking is to improve personalisation for customers – especially for incumbents looking to compete with digital natives. This is because AI can spot novel strategies that would never have been identified by human data scientists, and, in turn, allow companies to take full advantage of today’s massive data sets – ultimately helping to provide hyper-personalised experiences tailored to specific customers. For example, through the use of advanced chatbots and tailored content and interfaces in apps and on digital platforms.” says Babak Hodjat, VP Evolutionary AI, Cognizant.

AI has been widely accepted and appreciated in the banking sector as it can provide precise, real-time fraud prediction and detection. A Business Today report quotes RBI’s annual report, which revealed a massive increase in banking frauds in the 2019-20 fiscal year. 

AI enables banks to leverage human and machine capabilities optimally to drive operational and cost efficiencies, and deliver personalised services. The future of the financial industry with AI can be extremely innovative, but there is no doubt that its intelligence will need to stay up to date and keep growing. 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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