AI and Machine Learning
New use cases for artificial intelligence (AI) and machine learning (ML) are emerging all the time, across industries.
In fintech, AI and ML are increasingly being used to improve financial decision making,
Strengthen security operations and fraud detection, manage assets, and provide low-friction customer service and new opportunities for fintech businesses to implement AI and machine learning models are constantly in development. A new wave of fintech entrepreneurs are using AI to create hyper-convenient and personalised financial services for customers; while more established fintech companies are integrating artificial intelligence into their operations to ensure they stay competitive.
What is artificial intelligence?
Artificial intelligence (AI) is a field of study that combines computer science and mathematics to develop systems that can simulate human intelligence, or perform actions autonomously.
What is machine learning?
Machine learning is a sub-field of AI that focuses on developing algorithms and models that allow computers to learn from data without being explicitly or specifically programmed.
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The Role of AI and Machine Learning in Fintech Innovation
The Role of AI and Machine Learning in Fintech Innovation
Before AI, fintech companies had to rely on traditional data analysis and decision-making strategies, requiring many hours of human labour and with more scope for error. Customer support was largely offered by human agents, leading to long wait times and inconsistent quality of service during busy periods. And even when fintech operators had access to large volumes of data, they didn’t always have the capacity to analyse that data and generate insights they could actually use.
AI and machine learning models are already solving those problems. But as well as providing new methods to address existing challenges in the fintech industry, AI is also enabling fresh innovation. It’s creating opportunities for customers to take more control over their finances, while simultaneously generating new AI and ML jobs; and it’s handling repetitive tasks – so that human fintech developers have more time to develop innovative products and services.
Before AI, fintech companies had to rely on traditional data analysis and decision-making strategies, requiring many hours of human labour and with more scope for error. Customer support was largely offered by human agents, leading to long wait times and inconsistent quality of service during busy periods. And even when fintech operators had access to large volumes of data, they didn’t always have the capacity to analyse that data and generate insights they could actually use.
AI and machine learning models are already solving those problems. But as well as providing new methods to address existing challenges in the fintech industry, AI is also enabling fresh innovation. It’s creating opportunities for customers to take more control over their finances, while simultaneously generating new AI and ML jobs; and it’s handling repetitive tasks – so that human fintech developers have more time to develop innovative products and services.
The Importance of Data Quality and Integrity
One of the key benefits of AI is that it enables fintech companies to analyse vast volumes of data. AI and ML engineers can use supervised learning models to analyse labelled data, and apply unsupervised learning models to learn from unlabelled data without any guidance – so they can discover hidden data structures that reveal new information.
AI-powered data analysis streamlines business development and management processes – for example, it can rapidly identify correlations within a huge number of data sequences, and extract key information from data sets that might be missed in manual data processing operations.
But as artificial intelligence and machine learning relies on data, it’s crucial that it has access to high quality data – which has been collected and analysed using strict guidelines to ensure that it’s accurate and consistent, and which tracks all of the variables that might affect the reliability of information.
The quality of data directly impacts the performance and accuracy of AI models. Better data results in better predictions – and over time, that creates trust in artificial intelligence.
Key Applications: How AI and Machine Learning are
Transforming Finance
Here are areas where AI and ML are revolutionising finance – both for financial industry professionals and for consumers.
Loan underwriting, risk assessment, and credit scoring
Machine learning models can use the analysis of large bodies of data to create accurate risk models – which enable more reliable credit decisions that are better for both lenders and consumers.
Investments and trading
AI is catalysing important changes in investment management and trading; by enabling fast and accurate market analysis that enables traders to make informed decisions, and by enabling investment managers to build customised portfolios that suit their customers’ goals and circumstances.
Customer service
We’re all becoming familiar with the AI chatbots that answer our consumer queries. Fintech companies are increasingly relying on AI to handle customer service volumes – and one recent study found that while people want a service that feels human, most don’t care whether that ‘humanness’ is provided by a human being or a machine.
Fraud detection
AI is making it much easier for financial services firms to spot fraudulent activity. Machine learning algorithms can track and analyse transactions in real time – identifying suspicious activity and triggering alerts. AI-powered fraud detection systems have the capacity to analyse billions of transactions every year.
Personalised financial services
With high-volume data analysis capabilities and the ability to respond to a large number of customer requests at the same time, AI is enabling fintech companies to offer more tailored services. Machine learning models can use data to generate personalised recommendations – driving a rise in user engagement and customer satisfaction.
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The Role of AI and Machine Learning in Fintech Innovation
The Role of AI and Machine Learning in Fintech Innovation
Before AI, fintech companies had to rely on traditional data analysis and decision-making strategies, requiring many hours of human labour and with more scope for error. Customer support was largely offered by human agents, leading to long wait times and inconsistent quality of service during busy periods. And even when fintech operators had access to large volumes of data, they didn’t always have the capacity to analyse that data and generate insights they could actually use.
AI and machine learning models are already solving those problems. But as well as providing new methods to address existing challenges in the fintech industry, AI is also enabling fresh innovation. It’s creating opportunities for customers to take more control over their finances, while simultaneously generating new AI and ML jobs; and it’s handling repetitive tasks – so that human fintech developers have more time to develop innovative products and services.
Before AI, fintech companies had to rely on traditional data analysis and decision-making strategies, requiring many hours of human labour and with more scope for error. Customer support was largely offered by human agents, leading to long wait times and inconsistent quality of service during busy periods. And even when fintech operators had access to large volumes of data, they didn’t always have the capacity to analyse that data and generate insights they could actually use.
AI and machine learning models are already solving those problems. But as well as providing new methods to address existing challenges in the fintech industry, AI is also enabling fresh innovation. It’s creating opportunities for customers to take more control over their finances, while simultaneously generating new AI and ML jobs; and it’s handling repetitive tasks – so that human fintech developers have more time to develop innovative products and services.
Future Trends: What to Expect in AI and Machine Learning
The future of AI and ML in the fintech sector is bright – and Saudi Fintech Week will host some of the most innovative developers, entrepreneurs, and AI-curious investors in the industry.
A 2023 report from the Business Research Company
Estimated that the market for AI in fintech will reach USD $31.71 billion in 2027, growing at a CAGR of 28.6%.
Major players in the AI fintech market
Include Microsoft, Google, Intel Corporation, Amazon Web Services, IBM Corporation, IPsoft, Trifacta Software Inc., and more.
Growing number of AI-native fintech startups
And there’s a growing number of AI-native fintech startups gaining traction in regional and global markets – such as Affirm, a startup that offers instalment loans to consumers at point-of-sale, using AI to assess credit and make lending decisions; and Active.ai, a fintech startup that provides conversational finance services via AI-powered virtual assistants.
90% of fintech companies
Estimates suggest that 90% of fintech companies already use AI in some form (source: Cambridge Centre for Alternative Finance).
AI and machine learning will be the basis for key technologies in the future of fintech. Join us at Saudi Fintech Week to learn from the industry leaders who are pushing the boundaries of what’s possible; discover the latest AI use cases and trends; and find out how you can leverage developing technologies to grow your business.