The field of finance has long been very active in artificial intelligence (AI) research and implementation.
In fact, the financial industry was already involved in developing innovations around Bayesian statistics, a staple of machine learning, as early as the 1960s. These foundational use cases were based on stock market monitoring and making forecasts for investors. Today, that legacy continues with AI-powered robo-advisers designed to deliver automated, algorithm-based financial planning services with minimal or no human assistance.
Modern finance has since diversified its use of AI, including streamlining internal business processes and improving the overall customer experience. Finance professionals and clients are likely to have regular encounters with AI, as most routine service issues are addressed / resolved using some degree of automation powered by the ‘IA. This trend is likely to accelerate in order to meet growing customer demands for faster, more convenient and more secure financial experiences.
AI in finance today
AI in fintech had a market value of $ 7.91 billion in 2020 in 2020 and is expected to reach $ 26.67 billion by 2026, at a compound annual growth rate (CAGR) of 23.17%, according to Mordor Intelligence.
Expected growth is fueled by continued advancements in automated trading technologies and algorithms as well as relatively newer applications for smarter fraud prevention, more effective risk management, faster customer support, such as chatbots and through agent call routing, and continued tighter compliance with the financial industry. regulations.
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5 examples of AI in finance
1. Automation of process management and back-end operations
Forward-thinking companies are browsing massive amounts of data with technology. In the case of finance, the automation of transaction processing and back-end operations has enabled organizations to adapt to meet the demands of an ever-connected global economy. Using AI and natural language processing (NLP), businesses can automate the ingestion of accounts receivable / payable, invoices and accounting requests in structured and unstructured formats.
2. Optimize trading activity for better returns
Finance was one of the early innovators in AI, focusing on optimizing investor trading decisions. These days the two quantitative and algorithmic trading rely heavily on AI. In the case of quantitative trading, AI and statistical methods are used to identify investment opportunities but not necessarily to place orders automatically. In contrast, algorithmic trading involves fully automated systems that perform analysis and open / closed positions on behalf of a trader. These systems can process large data sets and identify patterns faster and more efficiently, enabling better predictive capabilities and more accurate estimates of future market patterns.
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3. Improve the personalized banking experience
The majority of banking customers have already become accustomed to regular meetings with AI, as common banking issues related to services are more often treated or solved with some degree of artificial intelligence. AI-enhanced banking experiences span every platform a customer uses, from personalized offers and alerts through a bank’s website and mobile app to the service’s faster and smarter call routing customer and problem solving. On these platforms, conversational AIs are at the forefront of delivering personalized financial advice and guidance, tailored to each client’s unique profile and requirements.
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4. Create more effective fraud detection measures
Not surprisingly, financial and banking companies are considered high-level targets for cybercriminals. Industry-grade cybersecurity and fraud detection measures are therefore the norm when it comes to preventing malicious actors from gaining the upper hand. For example, AI is used to detect and connect anomalous spending patterns among credit customers, which in turn can inform broader investigations of data breaches.
5. Inform credit decisions
In the past, three credit reporting agencies, Equifax, TransUnion and Experian, provided the data that underpins the vast majority of consumer credit decisions around the world. This has effectively left most of the world’s population without an account, as credible but “unbankable” consumers in developing countries or impoverished regions do not have formal access to global lending institutions. AI has changed this dynamic by allowing banks to use behavioral attributes, such as phone information, invoices / payment records, and social media information to create machine learning (ML) credit and solvency risk models.
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