The domain of finance has long been highly active in artificial intelligence (AI) research and implementation.
In fact, the financial sector was already involved in developing innovations around Bayesian statistics, a staple of machine learning, as early as the 1960s. These seminal use cases were based around monitoring stock markets and making predictions for investors. Today, this legacy continues with AI-powered robo-advisers designed to give automated, algorithm-based financial planning services with minimal to no human assistance.
Modern finance has since diversified its AI use, including the streamlining of internal business processes and improvement of the overall customer experience. Both finance pros and customers are likely to have AI encounters on a regular basis, since most routine service-related issues are handled/resolved using some degree of AI-powered automation. This trend is likely to accelerate in order to meet rising customer demands for faster, more convenient, and 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.
The anticipated growth is fueled by continuing advances 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 tighter ongoing compliance with finance industry regulations.
See more: Artificial Intelligence Market
5 Examples of AI in Finance
1. Automating Process Management and Back-end Operations
Forward-thinking companies navigate massive amounts of data with technology. In the case of finance, the automation of transaction processing and back-end operations has enabled organizations to scale to fulfill the demands of an always-connected, global economy. By utilizing AI and natural language processing (NLP), firms can automate the ingestion of accounts receivables/payables, invoices, and accounting requests in structured and unstructured formats.
2. Optimizing Trading Activity for Better Returns
Finance was an early AI innovator, focusing on the optimization of investor trading decisions. These days, both quantitative and algorithmic trading rely significantly on AI. In the case of quantitative trading, AI and statistical methods are used to surface investment opportunities but not necessarily place orders automatically. In contrast, algorithmic trading involves fully automated systems that perform analysis and open/close positions on a trader’s behalf. These systems can process large data sets and identify patterns faster and more efficiently, enabling better predictive capabilities and more accurate estimations of future market patterns.
3. Enhancing the Personalized Banking Experience
The majority of banking customers have already become accustomed to regular encounters with AI, since routine service-related banking issues are more often handled or resolved with some degree of artificial intelligence. AI-enhanced banking experiences span all platforms used by a customer, from customized offers and alerts via a bank’s website and mobile app to faster/smarter customer service call routing and problem resolution. Across these platforms, conversational AIs are taking the front line to provide personalized financial advice and guidance, customized to the unique profile and requirements of each customer.
4. Creating More Effective Fraud Detection Measures
Unsurprisingly, finance and banking enterprises are considered upper-echelon targets for cyber criminals. Industrial-grade cybersecurity and fraud detection measures are therefore the norm when it comes to preventing malicious actors from getting the upper hand. For example, AI is used for detecting and connecting anomalous spending patterns among credit customers, which in turn can inform broader data breach investigations.
5. Informing Credit Decisions
In the past, three credit reporting agencies, Equifax, TransUnion, and Experian, provided the data behind the vast majority of consumer credit decisions globally. This effectively left most of the world’s population unaccounted for, since credible but “unbankable” consumers from developing nations or impoverished regions lack formal access to global credit-granting institutions. AI has changed this dynamic by allowing banks to use behavioral attributes, such as phone information, bills/payment records, and social media information to create machine learning (ML) models for credit risk and worthiness.